{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T03:08:11Z","timestamp":1784603291651,"version":"3.55.0"},"reference-count":156,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022ZD0115903"],"award-info":[{"award-number":["2022ZD0115903"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62222209, 62250008, and 62102222"],"award-info":[{"award-number":["62222209, 62250008, and 62102222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100017582","name":"Beijing National Research Center for Information Science and Technology","doi-asserted-by":"crossref","award":["BNR2023RC01003 and BNR2023TD03006"],"award-info":[{"award-number":["BNR2023RC01003 and BNR2023TD03006"]}],"id":[{"id":"10.13039\/501100017582","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Key Lab of Networked Multimedia"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Sequential recommendation aims to recommend the next items that a target user may have interest in based on the user\u2019s sequence of past behaviors, which has become a hot research topic in both academia and industry. In the literature, sequential recommendation adopts a Sequence-to-Item or Sequence-to-Sequence training strategy, which supervises a sequential model with a user\u2019s next one or more behaviors as the labels and the sequence of the past behaviors as the input. However, existing powerful sequential recommendation approaches employ more and more complex deep structures such as Transformer in order to accurately capture the sequential patterns, which heavily rely on hand-crafted designs on key attention mechanism to achieve state-of-the-art performance, thus failing to automatically obtain the optimal design of attention representation architectures in various scenarios with different data. Other works on classic automated deep recommender systems only focus on traditional settings, ignoring the problem of sequential scenarios. In this article, we study the problem of automated sequential recommendation, which faces two main challenges: (1) How can we design a proper search space tailored for attention automation in sequential recommendation, and (2) How can we accurately search effective attention representation architectures considering multiple user interests reflected in the sequential behavior. To tackle these challenges, we propose an automated disentangled sequential recommendation (AutoDisenSeq) model. In particular, we employ neural architecture search (NAS) and design a search space tailored for automated attention representation in attentive intention-disentangled sequential recommendation with an expressive and efficient space complexity of\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(O(n^{2})\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            given\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(n\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            as the number of layers. We further propose a context-aware parameter sharing mechanism taking characteristics of each sub-architecture into account to enable accurate architecture performance estimations and great flexibility for disentanglement of latent intention representation. Moreover, we propose AutoDisenSeq-large language model (LLM), which utilizes the textual understanding power of LLM as a guidance to refine the candidate list for recommendation from AutoDisenSeq. We conduct extensive experiments to show that our proposed AutoDisenSeq model and AutoDisenSeq-LLM model outperform existing baseline methods on four real-world datasets in both overall recommendation and cold-start recommendation scenarios.\n          <\/jats:p>","DOI":"10.1145\/3675164","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T13:31:23Z","timestamp":1719667883000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Automated Disentangled Sequential Recommendation with Large Language Models"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0351-2939","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0943-2286","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-0620","authenticated-orcid":false,"given":"Zirui","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-7331","authenticated-orcid":false,"given":"Yuwei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9617-1653","authenticated-orcid":false,"given":"Chaoyu","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4057-5138","authenticated-orcid":false,"given":"Lifeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2236-9290","authenticated-orcid":false,"given":"Wenwu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations. 1\u201311."},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. arXiv:2305.00447. Retrieved from https:\/\/arxiv.org\/abs\/2305.00447","DOI":"10.1145\/3604915.3608857"},{"issue":"8","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1798\u20131828.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_5_2","first-page":"2095","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"Bouchacourt Diane","year":"2018","unstructured":"Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin. 2018. Multi-level variational autoencoder: Learning disentangled representations from grouped observations. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2095\u20132102."},{"key":"e_1_3_1_6_2","unstructured":"Christopher P. Burgess Irina Higgins Arka Pal Loic Matthey Nick Watters Guillaume Desjardins and Alexander Lerchner. 2018. Understanding disentangling in beta-VAE. arXiv:1804.03599. Retrieved from https:\/\/arxiv.org\/abs\/1804.03599"},{"key":"e_1_3_1_7_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Cai Han","year":"2019","unstructured":"Han Cai, Ligeng Zhu, and Song Han. 2019. ProxylessNAS: Direct neural architecture search on target task and hardware. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_8_2","first-page":"1292","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Cai Jie","year":"2022","unstructured":"Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu. 2022. Multimodal continual graph learning with neural architecture search. In Proceedings of the ACM Web Conference 2022. 1292\u20131300."},{"key":"e_1_3_1_9_2","first-page":"8227","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Cai Jie","year":"2024","unstructured":"Jie Cai, Xin Wang, Haoyang Li, Ziwei Zhang, and Wenwu Zhu. 2024. Multimodal graph neural architecture search under distribution shifts. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 8227\u20138235."},{"key":"e_1_3_1_10_2","volume-title":"Proceedings of the 33rd Conference on in Neural Information Processing Systems (NeurIPS \u201919)","author":"Chang Jianlong","year":"2019","unstructured":"Jianlong Chang, Yiwen Guo, gaofeng Meng, Shiming Xiang, Chunhong Pan. 2019. DATA: Differentiable ArchiTecture approximation. In Proceedings of the 33rd Conference on in Neural Information Processing Systems (NeurIPS \u201919)."},{"key":"e_1_3_1_11_2","first-page":"26924","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"Chen Hong","year":"2021","unstructured":"Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021. Curriculum disentangled recommendation with noisy multi-feedback. In Proceedings of the 35th International Conference on Neural Information Processing Systems. 26924\u201326936."},{"key":"e_1_3_1_12_2","first-page":"1","volume-title":"Proceedings of the 5th ACM International Conference on Multimedia in Asia","author":"Chen Hong","year":"2023","unstructured":"Hong Chen, Bin Huang, Xin Wang, Yuwei Zhou, and Wenwu Zhu. 2023a. Global-local GraphFormer: Towards better understanding of user intentions in sequential recommendation. In Proceedings of the 5th ACM International Conference on Multimedia in Asia. 1\u20137."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3369757"},{"key":"e_1_3_1_14_2","first-page":"1","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Chen Hong","year":"2023","unstructured":"Hong Chen, Yipeng Zhang, Simin Wu, Xin Wang, Xuguang Duan, Yuwei Zhou, and Wenwu Zhu. 2023b. Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation. In Proceedings of the 12th International Conference on Learning Representations. 1\u201312."},{"key":"e_1_3_1_15_2","first-page":"2610","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Chen Tian Qi","year":"2018","unstructured":"Tian Qi Chen, Xuechen Li, Roger B. Grosse, and David K. Duvenaud. 2018a. Isolating sources of disentanglement in variational autoencoders. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2610\u20132620."},{"key":"e_1_3_1_16_2","first-page":"1","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Chen Xi","year":"2016","unstructured":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 1\u201310."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1145\/3159652.3159668","volume-title":"Proceedings of WSDM 2018","author":"Chen Xu","year":"2018","unstructured":"Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018b. Sequential recommendation with user memory networks. In Proceedings of WSDM 2018. 108\u2013116."},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"2172","DOI":"10.1145\/3485447.3512090","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Chen Yongjun","year":"2022","unstructured":"Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2022. 2172\u20132182."},{"key":"e_1_3_1_19_2","first-page":"2137","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Chen Zhongxia","year":"2019","unstructured":"Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. 2019. Co-attentive multi-task learning for explainable recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2137\u20132143."},{"key":"e_1_3_1_20_2","unstructured":"Rewon Child Scott Gray Alec Radford and Ilya Sutskever. 2019. Generating long sequences with sparse transformers. arXiv:1904.10509. Retrieved from http:\/\/arxiv.org\/abs\/1904.10509"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2018.01.007","article-title":"Fine-grained attention mechanism for neural machine translation","volume":"284","author":"Choi Heeyoul","year":"2018","unstructured":"Heeyoul Choi, Kyunghyun Cho, and Yoshua Bengio. 2018. Fine-grained attention mechanism for neural machine translation. Neurocomputing 284 (2018), 171\u2013176.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_22_2","unstructured":"Xiangxiang Chu Bo Zhang Ruijun Xu and Jixiang Li. 2019. FairNAS: Rethinking evaluation fairness of weight sharing neural architecture search. arXiv:1907.01845. Retrieved from https:\/\/arxiv.org\/abs\/1907.01845"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1145\/2959100.2959190","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"Covington Paul","year":"2016","unstructured":"Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191\u2013198."},{"key":"e_1_3_1_24_2","unstructured":"Zeyu Cui Jianxin Ma Chang Zhou Jingren Zhou and Hongxia Yang. 2022. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv:2205.08084. Retrieved from https:\/\/arxiv.org\/abs\/2205.08084"},{"key":"e_1_3_1_25_2","first-page":"2978","volume-title":"Proceedings of the 57th Conference of the Association for Computational Linguistics","author":"Dai Zihang","year":"2019","unstructured":"Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov. 2019. Transformer-XL: Attentive language models beyond a fixed-length context. In Proceedings of the 57th Conference of the Association for Computational Linguistics. 2978\u20132988."},{"key":"e_1_3_1_26_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Daniluk Michal","year":"2017","unstructured":"Michal Daniluk, Tim Rockt\u00e4schel, Johannes Welbl, and Sebastian Riedel. 2017. Frustratingly short attention spans in neural language modeling. In Proceedings of the 5th International Conference on Learning Representations."},{"key":"e_1_3_1_27_2","first-page":"933","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Dauphin Yann N.","year":"2017","unstructured":"Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017. Language modeling with gated convolutional networks. In Proceedings of the 34th International Conference on Machine Learning. 933\u2013941."},{"issue":"1","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1145\/963770.963776","article-title":"Item-based top-n recommendation algorithms","volume":"22","author":"Deshpande Mukund","year":"2004","unstructured":"Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM TOIS 22, 1 (2004), 143\u2013177.","journal-title":"ACM TOIS"},{"key":"e_1_3_1_29_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_1_30_2","unstructured":"Nat Dilokthanakul Pedro A. M. Mediano Marta Garnelo Matthew C. H. Lee Hugh Salimbeni Kai Arulkumaran and Murray Shanahan. 2016. Deep unsupervised clustering with Gaussian mixture variational autoencoders. arXiv:1611.02648. Retrieved from https:\/\/arxiv.org\/abs\/1611.02648"},{"key":"e_1_3_1_31_2","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics","author":"Dong Li","year":"2016","unstructured":"Li Dong and Mirella Lapata. 2016. Language to logical form with neural attention. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics."},{"key":"e_1_3_1_32_2","first-page":"710","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Dupont Emilien","year":"2018","unstructured":"Emilien Dupont. 2018. Learning disentangled joint continuous and discrete representations. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 710\u2013720."},{"issue":"6394","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1126\/science.aar6170","article-title":"Neural scene representation and rendering","volume":"360","author":"Eslami S. M. Ali","year":"2018","unstructured":"S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu, and Demis Hassabis. 2018. Neural scene representation and rendering. Science 360, 6394 (2018), 1204\u20131210.","journal-title":"Science"},{"key":"e_1_3_1_34_2","unstructured":"Wenqi Fan Zihuai Zhao Jiatong Li Yunqing Liu Xiaowei Mei Yiqi Wang Jiliang Tang and Qing Li. 2023. Recommender systems in the era of large language models (LLMs). arXiv:2307.02046. Retrieved from https:\/\/arxiv.org\/abs\/2307.02046"},{"key":"e_1_3_1_35_2","first-page":"2036","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Fan Ziwei","year":"2022","unstructured":"Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S. Yu. 2022. Sequential recommendation via stochastic self-attention. In Proceedings of the ACM Web Conference 2022. 2036\u20132047."},{"key":"e_1_3_1_36_2","first-page":"10628","volume-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Fang Jiemin","year":"2019","unstructured":"Jiemin Fang, Yuzhu Sun, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang. 2019. Densely connected search space for more flexible neural architecture search. In Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10628\u201310637."},{"key":"e_1_3_1_37_2","unstructured":"Luke Friedman Sameer Ahuja David Allen Terry Tan Hakim Sidahmed Changbo Long Jun Xie Gabriel Schubiner Ajay Patel Harsh Lara Brian Chu Zexi Chen and Manoj Tiwari. 2023. Leveraging large language models in conversational recommender systems. arXiv:2305.07961. Retrieved from https:\/\/arxiv.org\/abs\/2305.07961"},{"key":"e_1_3_1_38_2","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence","author":"Gao Yang","year":"2020","unstructured":"Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph neural architecture search. In Proceedings of the 29th International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1145\/3523227.3546767","volume-title":"Proceedings of the 16th ACM Conference on Recommender Systems","author":"Geng Shijie","year":"2022","unstructured":"Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems. 299\u2013315."},{"key":"e_1_3_1_40_2","first-page":"7968","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Guan Chaoyu","year":"2022","unstructured":"Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, and Wenwu Zhu. 2022. Large-scale graph neural architecture search. In Proceedings of the 39th International Conference on Machine Learning. PMLR, 7968\u20137981."},{"key":"e_1_3_1_41_2","first-page":"3864","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Guan Chaoyu","year":"2021","unstructured":"Chaoyu Guan, Xin Wang, and Wenwu Zhu. 2021. Autoattend: Automated attention representation search. In Proceedings of the 38th International Conference on Machine Learning. PMLR, 3864\u20133874."},{"key":"e_1_3_1_42_2","unstructured":"Lei Guo Chunxiao Wang Xinhua Wang Lei Zhu and Hongzhi Yin. 2023. Automated prompting for non-overlapping cross-domain sequential recommendation. arXiv:2304.04218. Retrieved from https:\/\/arxiv.org\/abs\/2304.04218"},{"key":"e_1_3_1_43_2","volume-title":"Proceedings of the 16th European Conference on Computer Vision","author":"Guo Zichao","year":"2020","unstructured":"Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun. 2020a. Single path one-shot neural architecture search with uniform sampling. In Proceedings of the 16th European Conference on Computer Vision."},{"key":"e_1_3_1_44_2","first-page":"544","volume-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920)","author":"Guo Zichao","year":"2020","unstructured":"Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun. 2020b. Single path one-shot neural architecture search with uniform sampling. In Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920). Springer, 544\u2013560."},{"key":"e_1_3_1_45_2","first-page":"1","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Li Haoyang","year":"2024","unstructured":"Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, Wenwu Zhu. 2024. Disentangled graph self-supervised learning for out-of-distribution generalization. In Proceedings of the 41st International Conference on Machine Learning. PMLR. 1\u201313."},{"issue":"4","key":"e_1_3_1_46_2","first-page":"1","article-title":"The Movielens datasets: History and context","volume":"5","author":"Harper F. Maxwell","year":"2015","unstructured":"F. Maxwell Harper and Joseph A. Konstan. 2015. The Movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS) 5, 4 (2015), 1\u201319.","journal-title":"ACM Transactions on Interactive Intelligent Systems (TIIS)"},{"key":"e_1_3_1_47_2","first-page":"309","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"He Ruining","year":"2016","unstructured":"Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016. Vista: A visually, socially, and temporally-aware model for artistic recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 309\u2013316."},{"key":"e_1_3_1_48_2","first-page":"161","volume-title":"Proceedings of ACM RecSys 2017","author":"He Ruining","year":"2017","unstructured":"Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based recommendation. In Proceedings of ACM RecSys 2017. 161\u2013169."},{"key":"e_1_3_1_49_2","first-page":"388","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"He Ruidan","year":"2017","unstructured":"Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017b. An unsupervised neural attention model for aspect extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 388\u2013397."},{"key":"e_1_3_1_50_2","first-page":"191","volume-title":"Proceedings of the 16th International Conference on Data Mining (ICDM)","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. 2016. Fusing similarity models with Markov chains for sparse sequential recommendation. In Proceedings of the 16th International Conference on Data Mining (ICDM). IEEE, 191\u2013200."},{"key":"e_1_3_1_51_2","first-page":"173","volume-title":"Proceedings of WWW 2017","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017c. Neural collaborative filtering. In Proceedings of WWW 2017. 173\u2013182."},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1145\/3269206.3271761","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"Hidasi Bal\u00e1zs","year":"2018","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 843\u2013852."},{"key":"e_1_3_1_53_2","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939."},{"key":"e_1_3_1_54_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","volume":"3","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. Beta-VAE: Learning basic visual concepts with a constrained variational framework. In Proceedings of the International Conference on Learning Representations, Vol. 3. 1\u201311."},{"key":"e_1_3_1_55_2","first-page":"517","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Hsieh Jun-Ting","year":"2018","unstructured":"Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F. Fei-Fei, and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 517\u2013526."},{"key":"e_1_3_1_56_2","unstructured":"Edward J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. LoRA: Low-rank adaptation of large language models. arXiv:2106.09685. Retrieved from https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"e_1_3_1_57_2","first-page":"263","volume-title":"Proceedings of the 8th IEEE International Conference on Data Mining","author":"Hu Yifan","year":"2008","unstructured":"Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining. IEEE, 263\u2013272."},{"key":"e_1_3_1_58_2","first-page":"505","volume-title":"Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","author":"Huang Jin","year":"2018","unstructured":"Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 505\u2013514."},{"key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.24963\/ijcai.2017\/273","volume-title":"Proceedings of IJCAI 2017","author":"Jiang Zhuxi","year":"2017","unstructured":"Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. 2017. Variational deep embedding: an unsupervised and generative approach to clustering. In Proceedings of IJCAI 2017. 1965\u20131972."},{"key":"e_1_3_1_60_2","first-page":"197","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018a. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 197\u2013206."},{"key":"e_1_3_1_61_2","first-page":"197","volume-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM)","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018b. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE, 197\u2013206."},{"key":"e_1_3_1_62_2","first-page":"2654","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Kim Hyunjik","year":"2018","unstructured":"Hyunjik Kim and Andriy Mnih. 2018. Disentangling by factorising. In Proceedings of the 35th International Conference on Machine Learning. 2654\u20132663."},{"key":"e_1_3_1_63_2","first-page":"1","volume-title":"Proceedings of the 3rd International Conference for Learning Representations","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations. 1\u201311."},{"key":"e_1_3_1_64_2","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_1_65_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kitaev Nikita","year":"2020","unstructured":"Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020. Reformer: The efficient transformer. In Proceedings of the International Conference on Learning Representations. 1\u201311."},{"key":"e_1_3_1_66_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Komodakis Nikos","year":"2018","unstructured":"Nikos Komodakis and Spyros Gidaris. 2018. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR). 1\u201314."},{"issue":"8","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren Yehuda","year":"2009","unstructured":"Yehuda Koren, Robert Bell, Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30\u201337.","journal-title":"Computer"},{"key":"e_1_3_1_68_2","first-page":"1","article-title":"Disentangled Representation Learning","author":"Wang Xin","year":"2024","unstructured":"Xin Wang, Hong Chen, Si\u2019ao Tang, Zihao Wu, and Wenwu Zhu. 2024. Disentangled Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1\u201320.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_69_2","first-page":"8606","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Kosiorek Adam","year":"2018","unstructured":"Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner. 2018. Sequential attend, infer, repeat: Generative modelling of moving objects. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 8606\u20138616."},{"key":"e_1_3_1_70_2","first-page":"2717","volume-title":"Proceedings of the 21st Annual Conference of the International Speech Communication Association","author":"Lee Yoonhyung","year":"2020","unstructured":"Yoonhyung Lee, Seunghyun Yoon, and Kyomin Jung. 2020. Multimodal speech emotion recognition using cross attention with aligned audio and text. In Proceedings of the 21st Annual Conference of the International Speech Communication Association. 2717\u20132721."},{"key":"e_1_3_1_71_2","first-page":"2615","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Li Chao","year":"2019","unstructured":"Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019. Multi-interest network with dynamic routing for recommendation at tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2615\u20132623."},{"issue":"11","key":"e_1_3_1_72_2","first-page":"5403","article-title":"Intention-aware sequential recommendation with structured intent transition","volume":"34","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, and Wenwu Zhu. 2021a. Intention-aware sequential recommendation with structured intent transition. IEEE Transactions on Knowledge and Data Engineering 34, 11 (2021), 5403\u20135414.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_73_2","first-page":"21872","volume-title":"Proceedings of the 35th Conference on Neural Information Processing Systems","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021b. Disentangled contrastive learning on graphs. In Proceedings of the 35th Conference on Neural Information Processing Systems. 21872\u2013 21884."},{"issue":"8","key":"e_1_3_1_74_2","first-page":"7856","article-title":"Disentangled graph contrastive learning with independence promotion","volume":"35","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2022. Disentangled graph contrastive learning with independence promotion. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2022), 7856\u20137869.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_75_2","first-page":"1419","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Li Jing","year":"2017","unstructured":"Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419\u20131428."},{"key":"e_1_3_1_76_2","unstructured":"Liam Li Kevin Jamieson Afshin Rostamizadeh Ekaterina Gonina Moritz Hardt Benjamin Recht and Ameet Talwalkar. 2018. Massively parallel hyperparameter tuning. arXiv:1810.05934. Retrieved from https:\/\/arxiv.org\/abs\/1810.05934"},{"key":"e_1_3_1_77_2","first-page":"305","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Li Xiaopeng","year":"2017","unstructured":"Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 305\u2013314."},{"key":"e_1_3_1_78_2","first-page":"689","volume-title":"Proceedings of 2018 World Wide Web Conference","author":"Liang Dawen","year":"2018","unstructured":"Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of 2018 World Wide Web Conference. 689\u2013698."},{"key":"e_1_3_1_79_2","unstructured":"Richard Liaw Eric Liang Robert Nishihara Philipp Moritz Joseph E. Gonzalez and Ion Stoica. 2018. Tune: A research platform for distributed model selection and training. arXiv:1807.05118. Retrieved from https:\/\/arxiv.org\/abs\/1807.05118"},{"key":"e_1_3_1_80_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Lin Zhouhan","year":"2017","unstructured":"Zhouhan Lin, Minwei Feng, C\u00edcero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. In Proceedings of the 5th International Conference on Learning Representations."},{"key":"e_1_3_1_81_2","first-page":"82","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Liu Chenxi","year":"2019","unstructured":"Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, and Li Fei-Fei. 2019a. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 82\u201392."},{"key":"e_1_3_1_82_2","first-page":"1","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019b. DARTS: Differentiable architecture search. In Proceedings of the 7th International Conference on Learning Representations (2019). 1\u201311."},{"key":"e_1_3_1_83_2","unstructured":"Junling Liu Chao Liu Renjie Lv Kang Zhou and Yan Zhang. 2023. Is ChatGPT a good recommender? A preliminary study. arXiv:2304.10149. Retrieved from https:\/\/arxiv.org\/abs\/2304.10149"},{"key":"e_1_3_1_84_2","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Liu Ninghao","year":"2019","unstructured":"Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, and Xia Hu. 2019c. Is a single vector enough? Exploring node polysemy for network embedding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining."},{"key":"e_1_3_1_85_2","first-page":"1053","volume-title":"Proceedings of the 16th International Conference on Data Mining (ICDM)","author":"Liu Qiang","year":"2016","unstructured":"Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang. 2016. Context-aware sequential recommendation. In Proceedings of the 16th International Conference on Data Mining (ICDM). IEEE, 1053\u20131058."},{"key":"e_1_3_1_86_2","first-page":"1831","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Liu Qiao","year":"2018","unstructured":"Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention\/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1831\u20131839."},{"key":"e_1_3_1_87_2","first-page":"289","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems","author":"Lu Jiasen","year":"2016","unstructured":"Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. In Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems. 289\u2013297."},{"key":"e_1_3_1_88_2","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.18653\/v1\/D15-1166","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412\u20131421."},{"key":"e_1_3_1_89_2","first-page":"1488","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems","author":"Ma Benteng","year":"2020","unstructured":"Benteng Ma, Jing Zhang, Yong Xia, and Dacheng Tao. 2020a. Auto learning attention. In Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems. 1488\u20131500."},{"key":"e_1_3_1_90_2","first-page":"4212","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML 2019)","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019a. Disentangled graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019). 4212\u20134221."},{"key":"e_1_3_1_91_2","first-page":"5712","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019d. Learning disentangled representations for recommendation. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 5712\u20135723."},{"key":"e_1_3_1_92_2","first-page":"483","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Ma Jianxin","year":"2020","unstructured":"Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020b. Disentangled self-supervision in sequential recommenders. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 483\u2013491."},{"key":"e_1_3_1_93_2","first-page":"99","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Ma Liqian","year":"2018","unstructured":"Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, and Mario Fritz. 2018. Disentangled person image generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 99\u2013108."},{"issue":"12","key":"e_1_3_1_94_2","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1109\/TASLP.2019.2942160","article-title":"Global-local mutual attention model for text classification","volume":"27","author":"Ma Qianli","year":"2019","unstructured":"Qianli Ma, Liuhong Yu, Shuai Tian, Enhuan Chen, and Wing W. Y. Ng. 2019b. Global-local mutual attention model for text classification. IEEE\/ACM Transactions on Audio, Speech, and Language Processing 27, 12 (2019), 2127\u20132139.","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"e_1_3_1_95_2","first-page":"2232","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems","author":"Ma Xindian","year":"2019","unstructured":"Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, and Dawei Song. 2019c. A tensorized transformer for language modeling. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems. 2232\u20132242."},{"key":"e_1_3_1_96_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Mei Jieru","year":"2020","unstructured":"Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, and Jianchao Yang. 2020. AtomNAS: Fine-grained end-to-end neural architecture search. In Proceedings of the International Conference on Learning Representations. 1\u201313."},{"key":"e_1_3_1_97_2","first-page":"1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Michel Paul","year":"2019","unstructured":"Paul Michel, Omer Levy, and Graham Neubig. 2019. Are sixteen heads really better than one?. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 1\u201311."},{"key":"e_1_3_1_98_2","first-page":"3700","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Monti Federico","year":"2017","unstructured":"Federico Monti, Michael Bronstein, and Xavier Bresson. 2017. Geometric matrix completion with recurrent multi-graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems. 3700\u20133710."},{"key":"e_1_3_1_99_2","first-page":"6087","volume-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition","author":"Nguyen Duy-Kien","year":"2018","unstructured":"Duy-Kien Nguyen and Takayuki Okatani. 2018. Improved fusion of visual and language representations by dense symmetric co-attention for visual question answering. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. 6087\u20136096."},{"key":"e_1_3_1_100_2","first-page":"188","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Ni Jianmo","year":"2019","unstructured":"Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 188\u2013197."},{"key":"e_1_3_1_101_2","first-page":"496","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Niu Guocheng","year":"2019","unstructured":"Guocheng Niu, Hengru Xu, Bolei He, Xinyan Xiao, Hua Wu, and Sheng Gao. 2019. Enhancing local feature extraction with global representation for neural text classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 496\u2013506."},{"key":"e_1_3_1_102_2","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Pham Hieu","year":"2018","unstructured":"Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean. 2018. Efficient neural architecture search via parameter sharing. Proceedings of the 35th International Conference on Machine Learning (2018)."},{"key":"e_1_3_1_103_2","first-page":"1288","volume-title":"Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM)","author":"Qin Yijian","year":"2021","unstructured":"Yijian Qin, Xin Wang, Peng Cui, and Wenwu Zhu. 2021. Gqnas: Graph q network for neural architecture search. In Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 1288\u20131293."},{"key":"e_1_3_1_104_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Qin Yijian","year":"2024","unstructured":"Yijian Qin, Xin Wang, Ziwei Zhang, Hong Chen, and Wenwu Zhu. 2024. Multi-task graph neural architecture search with task-aware collaboration and curriculum. In Proceedings of the 37th International Conference on Neural Information Processing Systems."},{"key":"e_1_3_1_105_2","first-page":"18083","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Qin Yijian","year":"2022","unstructured":"Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, and Wenwu Zhu. 2022a. Graph neural architecture search under distribution shifts. In Proceedings of the 39th International Conference on Machine Learning. PMLR, 18083\u201318095."},{"key":"e_1_3_1_106_2","first-page":"54","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"Qin Yijian","year":"2022","unstructured":"Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, and Wenwu Zhu. 2022b. Nas-bench-graph: Benchmarking graph neural architecture search. In Proceedings of the 36th International Conference on Neural Information Processing Systems. 54\u201369."},{"key":"e_1_3_1_107_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Rae Jack W.","year":"2020","unstructured":"Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap. 2020. Compressive transformers for long-range sequence modelling. In Proceedings of the International Conference on Learning Representations. 1\u201314."},{"key":"e_1_3_1_108_2","first-page":"452","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452\u2013461."},{"key":"e_1_3_1_109_2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1145\/1772690.1772773","volume-title":"Proceedings of the 19th International Conference on World Wide Web","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811\u2013820."},{"key":"e_1_3_1_110_2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1145\/192844.192905","volume-title":"Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work","author":"Resnick Paul","year":"1994","unstructured":"Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. ACM, 175\u2013186."},{"key":"e_1_3_1_111_2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1145\/1772690.1772773","volume-title":"Proceedings of the 19th International Conference on World Wide Web","author":"Rendle C. Freudenthaler S.","year":"2010","unstructured":"C. Freudenthaler S. Rendle and L. Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811\u2013820."},{"key":"e_1_3_1_112_2","first-page":"1812","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Saikia Tonmoy","year":"2019","unstructured":"Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, and Thomas Brox. 2019. AutoDispNet: Improving disparity estimation with AutoML. In Proceedings of the IEEE International Conference on Computer Vision. 1812\u20131823."},{"key":"e_1_3_1_113_2","first-page":"1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"20","author":"Salakhutdinov Ruslan","year":"2011","unstructured":"Ruslan Salakhutdinov and Andriy Mnih. 2011. Probabilistic matrix factorization. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 20. 1\u20138."},{"key":"e_1_3_1_114_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1145\/371920.372071","volume-title":"Proceedings of the 10th International Conference on World Wide Web","author":"Sarwar Badrul","year":"2001","unstructured":"Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, 285\u2013295."},{"key":"e_1_3_1_115_2","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)","author":"Shaw Peter","year":"2018","unstructured":"Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018. Self-attention with relative position representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)."},{"key":"e_1_3_1_116_2","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"So David R.","year":"2019","unstructured":"David R. So, Chen Liang, and Quoc V. Le. 2019. The evolved transformer. In Proceedings of the 36th International Conference on Machine Learning."},{"key":"e_1_3_1_117_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Sukhbaatar Sainbayar","year":"2015","unstructured":"Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. 2015. End-to-end memory networks. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_1_118_2","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Sun Fei","year":"2019","unstructured":"Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management."},{"key":"e_1_3_1_119_2","first-page":"4320","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Liu Y.","year":"2019","unstructured":"Y. Liu, V. S. Sheng, J. Xu, D. Wang, G. Liu, T. Zhang, P. Zhao, and X. Zhou. 2019. Feature-level deeper self-attention network for sequential recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 4320\u20134326."},{"key":"e_1_3_1_120_2","first-page":"565","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Tang Jiaxi","year":"2018","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 565\u2013573."},{"key":"e_1_3_1_121_2","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Tay Yi","year":"2020","unstructured":"Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan. 2020. Sparse sinkhorn attention. In Proceedings of the 37th International Conference on Machine Learning."},{"key":"e_1_3_1_122_2","unstructured":"Romal Thoppilan Daniel De Freitas Jamie Hall Noam Shazeer Apoorv Kulshreshtha Heng-Tze Cheng Alicia Jin Taylor Bos Leslie Baker Yu Du YaGuang Li Hongrae Lee Huaixiu Steven Zheng Amin Ghafouri Marcelo Menegali Yanping Huang Maxim Krikun Dmitry Lepikhin James Qin Dehao Chen Yuanzhong Xu Zhifeng Chen Adam Roberts Maarten Bosma Vincent Zhao Yanqi Zhou Chung-Ching Chang Igor Krivokon Will Rusch Marc Pickett Pranesh Srinivasan Laichee Man Kathleen Meier-Hellstern Meredith Ringel Morris Tulsee Doshi Renelito Delos Santos Toju Duke Johnny Soraker Ben Zevenbergen Vinodkumar Prabhakaran Mark Diaz Ben Hutchinson Kristen Olson Alejandra Molina Erin Hoffman-John Josh Lee Lora Aroyo Ravi Rajakumar Alena Butryna Matthew Lamm Viktoriya Kuzmina Joe Fenton Aaron Cohen Rachel Bernstein Ray Kurzweil Blaise Aguera-Arcas Claire Cui Marian Croak Ed Chi and Quoc Le. 2022. LaMDA: Language models for dialog applications. arXiv:2201.08239. Retrieved from https:\/\/arxiv.org\/abs\/2201.08239"},{"key":"e_1_3_1_123_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar Aurelien Rodriguez Armand Joulin Edouard Grave and Guillaume Lample. 2023. LLaMA: Open and efficient foundation language models. arXiv:2302.13971. Retrieved from https:\/\/arxiv.org\/abs\/2302.13971"},{"key":"e_1_3_1_124_2","first-page":"6000","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 6000\u20136010."},{"key":"e_1_3_1_125_2","first-page":"1","volume-title":"Proceedings of the 6th International Conference on Learning Representations","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations. 1\u201312."},{"key":"e_1_3_1_126_2","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1145\/2783258.2783273","volume-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wang Hao","year":"2015","unstructured":"Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015b. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235\u20131244."},{"key":"e_1_3_1_127_2","volume-title":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Wang Pengfei","year":"2015","unstructured":"Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015a. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_3_1_128_2","unstructured":"Xin Wang Hong Chen Si\u2019ao Tang Zihao Wu and Wenwu Zhu. 2022a. Disentangled representation learning. arXiv:2211.11695. Retrieved from https:\/\/arxiv.org\/abs\/2211.11695"},{"issue":"1","key":"e_1_3_1_129_2","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TPAMI.2022.3153112","article-title":"Disentangled Representation Learning for Recommendation","volume":"45","author":"Wang Xin","year":"2022","unstructured":"Xin Wang, Hong Chen, Yuwei Zhou, Jianxin Ma, and Wenwu Zhu. 2022b. Disentangled Representation Learning for Recommendation. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 1 (2022), 408\u2013424.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_130_2","first-page":"1","volume-title":"Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME)","author":"Wang Xin","year":"2021","unstructured":"Xin Wang, Hong Chen, and Wenwu Zhu. 2021. Multimodal disentangled representation for recommendation. In Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1\u20136."},{"key":"e_1_3_1_131_2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1145\/3132847.3132880","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Wang Xin","year":"2017","unstructured":"Xin Wang, Steven CH Hoi, Chenghao Liu, and Martin Ester. 2017. Interactive social recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 357\u2013366."},{"key":"e_1_3_1_132_2","first-page":"36174","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Wang Xin","year":"2023","unstructured":"Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, and Wenwu Zhu. 2023a. Curriculum co-disentangled representation learning across multiple environments for social recommendation. In Proceedings of the 40th International Conference on Machine Learning. PMLR, 36174\u201336192."},{"key":"e_1_3_1_133_2","doi-asserted-by":"crossref","first-page":"4450","DOI":"10.1145\/3581783.3612401","volume-title":"Proceedings of the 31st ACM International Conference on Multimedia","author":"Wang Xin","year":"2023","unstructured":"Xin Wang, Zihao Wu, Hong Chen, Xiaohan Lan, and Wenwu Zhu. 2023b. Mixup-augmented temporally debiased video grounding with content-location disentanglement. In Proceedings of the 31st ACM International Conference on Multimedia. 4450\u20134459."},{"key":"e_1_3_1_134_2","volume-title":"Proceedings of the 16th European Conference on Computer Vision","author":"Wang Xiaofang","year":"2020","unstructured":"Xiaofang Wang, Xuehan Xiong, Maxim Neumann, A. J. Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, and Wei Hua. 2020a. AttentionNAS: Spatiotemporal attention cell search for video classification. In Proceedings of the 16th European Conference on Computer Vision."},{"key":"e_1_3_1_135_2","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence. The 32nd Innovative Applications of Artificial Intelligence Conference","author":"Wang Yujing","year":"2020","unstructured":"Yujing Wang, Yaming Yang, Yiren Chen, Jing Bai, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang, and Lidong Zhou. 2020b. TextNAS: A Neural Architecture Search Space Tailored for Text Representation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. The 32nd Innovative Applications of Artificial Intelligence Conference."},{"key":"e_1_3_1_136_2","unstructured":"Chuhan Wu Fangzhao Wu Tao Qi Jianxun Lian Yongfeng Huang and Xing Xie. 2020. PTUM: Pre-training user model from unlabeled user behaviors via self-supervision. arXiv:2010.01494. Retrieved from https:\/\/arxiv.org\/abs\/2010.01494"},{"key":"e_1_3_1_137_2","first-page":"495","volume-title":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","author":"Wu Chao-Yuan","year":"2017","unstructured":"Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent recommender networks. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 495\u2013503."},{"key":"e_1_3_1_138_2","first-page":"9329","volume-title":"Proceedings of the 31st ACM International Conference on Multimedia","author":"Wu Zihao","year":"2023","unstructured":"Zihao Wu, Xin Wang, Hong Chen, Kaidong Li, Yi Han, Lifeng Sun, and Wenwu Zhu. 2023. Diff4Rec: Sequential recommendation with curriculum-scheduled diffusion augmentation. In Proceedings of the 31st ACM International Conference on Multimedia. 9329\u20139335."},{"key":"e_1_3_1_139_2","first-page":"8143","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xie Beini","year":"2023","unstructured":"Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, and Wenwu Zhu. 2023. Adversarially robust neural architecture search for graph neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8143\u20138152."},{"key":"e_1_3_1_140_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xie Sirui","year":"2019","unstructured":"Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin. 2019. SNAS: Stochastic neural architecture search. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_141_2","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Yang Zichao","year":"2016","unstructured":"Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies."},{"key":"e_1_3_1_142_2","first-page":"16433","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Yao Yang","year":"2024","unstructured":"Yang Yao, Xin Wang, Yijian Qin, Ziwei Zhang, Wenwu Zhu, and Hong Mei. 2024. Data-augmented curriculum graph neural architecture search under distribution shifts. Proceedings of the AAAI Conference on Artificial Intelligence 38, 15 (2024), 16433\u201316441."},{"key":"e_1_3_1_143_2","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"You Quanzeng","year":"2016","unstructured":"Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. 2016. Image captioning with semantic attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_1_144_2","volume-title":"Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yu Feng","year":"2016","unstructured":"Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_3_1_145_2","volume-title":"Proceedings of the 28th ACM International Conference on Multimedia (MM \u201920)","author":"Yu Zhou","year":"2020","unstructured":"Zhou Yu, Yuhao Cui, Jun Yu, Meng Wang, Dacheng Tao, and Qi Tian. 2020. Deep multimodal neural architecture search. In Proceedings of the 28th ACM International Conference on Multimedia (MM \u201920)."},{"key":"e_1_3_1_146_2","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Yu Zhou","year":"2019","unstructured":"Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019. Deep modular co-attention networks for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_1_147_2","first-page":"1","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Zeyang","year":"2024","unstructured":"Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, Wenwu Zhu. 2024. Disentangled continual graph neural architecture search with invariant modular supernet. In Proceedings of the International Conference on Machine Learning. PMLR. 1\u201317."},{"key":"e_1_3_1_148_2","doi-asserted-by":"crossref","unstructured":"Junjie Zhang Ruobing Xie Yupeng Hou Wayne Xin Zhao Leyu Lin and Ji-Rong Wen. 2023b. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv:2305.07001. Retrieved from https:\/\/arxiv.org\/abs\/2305.07001","DOI":"10.1145\/3708882"},{"key":"e_1_3_1_149_2","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1145\/3580305.3599253","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Zhang Yipeng","year":"2023","unstructured":"Yipeng Zhang, Xin Wang, Hong Chen, and Wenwu Zhu. 2023a. Adaptive disentangled transformer for sequential recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3434\u20133445."},{"key":"e_1_3_1_150_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Zhang Zeyang","year":"2024","unstructured":"Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, and Wenwu Zhu. 2024. Unsupervised graph neural architecture search with disentangled self-supervision. Proceedings of the 37th International Conference on Neural Information Processing Systems."},{"key":"e_1_3_1_151_2","first-page":"11307","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Zhang Zeyang","year":"2023","unstructured":"Zeyang Zhang, Ziwei Zhang, Xin Wang, Yijian Qin, Zhou Qin, and Wenwu Zhu. 2023c. Dynamic heterogeneous graph attention neural architecture search. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 11307\u201311315."},{"key":"e_1_3_1_152_2","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI \u201918)","author":"Zhou Chang","year":"2018","unstructured":"Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018. ATRank: An attention-based user behavior modeling framework for recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI \u201918)."},{"key":"e_1_3_1_153_2","unstructured":"Kaixiong Zhou Qingquan Song Xiao Huang and Xia Hu. 2019. Auto-GNN: Neural architecture search of graph neural networks. arXiv:1909.03184. Retrieved from https:\/\/arxiv.org\/abs\/1909.03184"},{"key":"e_1_3_1_154_2","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1145\/3503161.3548271","volume-title":"Proceedings of the 30th ACM International Conference on Multimedia","author":"Zhou Yuwei","year":"2022","unstructured":"Yuwei Zhou, Xin Wang, Hong Chen, Xuguang Duan, Chaoyu Guan, and Wenwu Zhu. 2022. Curriculum-nas: Curriculum weight-sharing neural architecture search. In Proceedings of the 30th ACM International Conference on Multimedia. 6792\u20136801."},{"key":"e_1_3_1_155_2","first-page":"118","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Zhu Jun-Yan","year":"2018","unstructured":"Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, and Bill Freeman. 2018. Visual object networks: Image generation with disentangled 3D representations. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 118\u2013129."},{"key":"e_1_3_1_156_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Zoph Barret","year":"2017","unstructured":"Barret Zoph and Quoc V. Le. 2017. Neural architecture search with reinforcement learning. In Proceedings of the 5th International Conference on Learning Representations."},{"key":"e_1_3_1_157_2","volume-title":"Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition","author":"Zoph Barret","year":"2018","unstructured":"Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3675164","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3675164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:36Z","timestamp":1750291536000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3675164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":156,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,31]]}},"alternative-id":["10.1145\/3675164"],"URL":"https:\/\/doi.org\/10.1145\/3675164","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"2024-02-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}