{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:59:29Z","timestamp":1774447169025,"version":"3.50.1"},"reference-count":90,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100020771","name":"Young Scientists Fund of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62502253"],"award-info":[{"award-number":["62502253"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Quan Cheng Laboratory","award":["QCLZD202301"],"award-info":[{"award-number":["QCLZD202301"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Existing\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    equential\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    ecommendation (SR) methods have conventionally viewed historical interactions as one-dimensional sequences, often overlooking the fact that user behaviors can be multi-faceted and uncertain. Such a straightforward perspective fails to account for varied behavior patterns embedded in the historical sequences. Moving beyond adhering to singular historical sequences, we treat augmented sequences as meaningful behavior patterns and jointly optimize all sequences (augmented and original sequences) to capture diverse patterns, intricate dependencies, and uncertainties. To acknowledge and distinguish new patterns derived from the original sequence, we develop a sequential order-enhanced method to calculate the edge weight, highlighting the unique dependency relationships inherent in each individual sequence. To prevent recommendations from becoming monotonous due to similarities in augmented sequences, we apply a repulsive mechanism to sequences with similar topics\/categories, ensuring a broader spectrum of suggestions. Considering the potent expressive capability of the probabilistic model, Structured Determinantal Point Processes (SDPP), in representing structures, we perceive original and augmented sequences as such structures, leading to our\n                    <jats:italic toggle=\"yes\">generic<\/jats:italic>\n                    learning framework\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    tructured\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    equential\n                    <jats:italic toggle=\"yes\">Rec<\/jats:italic>\n                    ommendation (SSRec), which is theoretically proved to be a\n                    <jats:italic toggle=\"yes\">structured ranking optimization criterion<\/jats:italic>\n                    . Comprehensive experiments on real-world datasets demonstrate SSRec\u2019s distinct advantages over state-of-the-art models in terms of both diversity and accuracy.\n                  <\/jats:p>","DOI":"10.1145\/3796521","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:42:59Z","timestamp":1770738179000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SSRec: Structured Ranking Optimization Criterion for Sequential Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8268-6792","authenticated-orcid":false,"given":"Yuli","family":"Liu","sequence":"first","affiliation":[{"name":"Quan Cheng Laboratory, Jinan, China and Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2359-286X","authenticated-orcid":false,"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9195-6228","authenticated-orcid":false,"given":"Bokang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8650-9013","authenticated-orcid":false,"given":"Yachao","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0537-1997","authenticated-orcid":false,"given":"Yu","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6763-2892","authenticated-orcid":false,"given":"Zhengjun","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6539-7703","authenticated-orcid":false,"given":"Xiaojing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1224","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Affandi Raja Hafiz","year":"2014","unstructured":"Raja Hafiz Affandi, Emily Fox, Ryan Adams, and Ben Taskar. 2014. Learning the parameters of determinantal point process kernels. In Proceedings of the International Conference on Machine Learning. PMLR, 1224\u20131232."},{"key":"e_1_3_2_3_2","first-page":"60","volume-title":"Proceedings of the World Wide Web Conference","author":"Bai Jinze","year":"2019","unstructured":"Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, and Jun Gao. 2019. Personalized bundle list recommendation. In Proceedings of the World Wide Web Conference, 60\u201371."},{"key":"e_1_3_2_4_2","first-page":"46","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Beutel Alex","year":"2018","unstructured":"Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed. H. Chi. 2018. Latent cross: Making use of context in recurrent recommender systems. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 46\u201354."},{"issue":"1","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"20118","DOI":"10.1038\/s41598-019-56191-7","article-title":"Recursive patterns in online echo chambers","volume":"9","author":"Brugnoli Emanuele","year":"2019","unstructured":"Emanuele Brugnoli, Matteo Cinelli, Walter Quattrociocchi, and Antonio Scala. 2019. Recursive patterns in online echo chambers. Scientific Reports 9, 1 (2019), 20118.","journal-title":"Scientific Reports"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1145\/3404835.3462968","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chang Jianxin","year":"2021","unstructured":"Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021. Sequential recommendation with graph neural networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 378\u2013387."},{"key":"e_1_3_2_7_2","first-page":"1597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the International Conference on Machine Learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_8_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_2_9_2","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1145\/3038912.3052585","volume-title":"Proceedings of the 26th International Conference on World Wide Web","author":"Cheng Peizhe","year":"2017","unstructured":"Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In Proceedings of the 26th International Conference on World Wide Web, 183\u2013192."},{"key":"e_1_3_2_10_2","unstructured":"Kyunghyun Cho Bart Van Merri\u00ebnboer Dzmitry Bahdanau and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259. Retrieved from https:\/\/arxiv.org\/abs\/1409.1259"},{"issue":"4","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1137\/19M1281848","article-title":"Low-rank approximation in the Frobenius norm by column and row subset selection","volume":"41","author":"Cortinovis Alice","year":"2020","unstructured":"Alice Cortinovis and Daniel Kressner. 2020. Low-rank approximation in the Frobenius norm by column and row subset selection. SIAM Journal on Matrix Analysis and Applications 41, 4 (2020), 1651\u20131673.","journal-title":"SIAM Journal on Matrix Analysis and Applications"},{"key":"e_1_3_2_12_2","first-page":"152","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems","author":"Donkers Tim","year":"2017","unstructured":"Tim Donkers, Benedikt Loepp, and J\u00fcrgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In Proceedings of the 11th ACM Conference on Recommender Systems, 152\u2013160."},{"key":"e_1_3_2_13_2","first-page":"516","volume-title":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"Du Yingpeng","year":"2021","unstructured":"Yingpeng Du, Hongzhi Liu, and Zhonghai Wu. 2021. Modeling multi-factor and multi-faceted preferences over sequential networks for next item recommendation. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 516\u2013531."},{"key":"e_1_3_2_14_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_2_15_2","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence","author":"Gartrell Mike","year":"2017","unstructured":"Mike Gartrell, Ulrich Paquet, and Noam Koenigstein. 2017. Low-rank factorization of determinantal point processes. In Proceedings of the 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_16_2","first-page":"710","volume-title":"Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning","author":"Gillenwater Jennifer","year":"2012","unstructured":"Jennifer Gillenwater, Alex Kulesza, and Ben Taskar. 2012. Discovering diverse and salient threads in document collections. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 710\u2013720."},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"16853","DOI":"10.1609\/aaai.v39i16.33852","article-title":"DivGCL: A graph contrastive learning model for diverse recommendation","volume":"39","author":"Gong Wenwen","year":"2025","unstructured":"Wenwen Gong, Yangliao Geng, Dan Zhang, Yifan Zhu, Xiaolong Xu, Haolong Xiang, Amin Beheshti, Xuyun Zhang, and Lianyong Qi. 2025. DivGCL: A graph contrastive learning model for diverse recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, 16853\u201316861.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_18_2","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 27.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_19_2","first-page":"1384","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Han Insu","year":"2017","unstructured":"Insu Han, Prabhanjan Kambadur, Kyoungsoo Park, and Jinwoo Shin. 2017. Faster greedy MAP inference for determinantal point processes. In Proceedings of the International Conference on Machine Learning. PMLR, 1384\u20131393."},{"issue":"4","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","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 5, 4 (2015), 1\u201319.","journal-title":"ACM Transactions on Interactive Intelligent Systems"},{"key":"e_1_3_2_21_2","first-page":"161","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems","author":"He Ruining","year":"2017","unstructured":"Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems, 161\u2013169."},{"key":"e_1_3_2_22_2","first-page":"191","volume-title":"Proceedings of the 2016 IEEE 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 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 191\u2013200."},{"key":"e_1_3_2_23_2","first-page":"507","volume-title":"Proceedings of the 25th International Conference on World Wide Web","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web, 507\u2013517."},{"key":"e_1_3_2_24_2","first-page":"173","volume-title":"Proceedings of the 26th International Conference on World Wide Web","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web, 173\u2013182."},{"key":"e_1_3_2_25_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_2_26_2","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939. Retrieved from https:\/\/arxiv.org\/abs\/1511.06939"},{"issue":"8","key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"e_1_3_2_28_2","first-page":"2968","volume-title":"Proceedings of the Web Conference 2021","author":"Hsu Cheng","year":"2021","unstructured":"Cheng Hsu and Cheng-Te Li. 2021. RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recommendation. In Proceedings of the Web Conference 2021, 2968\u20132979."},{"key":"e_1_3_2_29_2","first-page":"505","volume-title":"Proceedings of the 41st International ACM SIGIR Conference on Research and 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 and Development in Information Retrieval, 505\u2013514."},{"key":"e_1_3_2_30_2","unstructured":"Juyong Jiang Jae Boum Kim Yingtao Luo Kai Zhang and Sunghun Kim. 2022. AdaMCT: Adaptive mixture of CNN-transformer for sequential recommendation. arXiv:2205.08776. Retrieved from https:\/\/arxiv.org\/abs\/2205.08776"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1145\/3404835.3463016","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Kang Taegwan","year":"2021","unstructured":"Taegwan Kang, Hwanhee Lee, Byeongjin Choe, and Kyomin Jung. 2021. Entangled bidirectional encoder to autoregressive decoder for sequential recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1657\u20131661."},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/ICDM.2018.00035","volume-title":"Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM)","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197\u2013206."},{"key":"e_1_3_2_33_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_34_2","article-title":"Structured determinantal point processes","volume":"23","author":"Kulesza Alex","year":"2010","unstructured":"Alex Kulesza and Ben Taskar. 2010. Structured determinantal point processes. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 23.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"issue":"2","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1561\/2200000044","article-title":"Determinantal point processes for machine learning","volume":"5","author":"Kulesza Alex","year":"2012","unstructured":"Alex Kulesza and Ben Taskar. 2012. Determinantal point processes for machine learning. Foundations and Trends\u00ae in Machine Learning 5, 2\u20133 (2012), 123\u2013286.","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"e_1_3_2_36_2","first-page":"1231","volume-title":"Proceedings of the 40th ACM\/SIGAPP Symposium on Applied Computing","author":"Lee Dongjun","year":"2025","unstructured":"Dongjun Lee, Donggeun Ko, and Jaekwang Kim. 2025. Hierarchical contrastive learning with multiple augmentations for sequential recommendation. In Proceedings of the 40th ACM\/SIGAPP Symposium on Applied Computing, 1231\u20131239."},{"key":"e_1_3_2_37_2","first-page":"707","volume-title":"Soviet Physics Doklady","author":"Levenshtein Vladimir I.","year":"1966","unstructured":"Vladimir I. Levenshtein, et al. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet Physics Doklady, Vol. 10. Soviet Union, 707\u2013710."},{"key":"e_1_3_2_38_2","first-page":"101","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"Li Chengxi","year":"2023","unstructured":"Chengxi Li, Yejing Wang, Qidong Liu, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan, and Qing Li. 2023. STRec: Sparse transformer for sequential recommendations. In Proceedings of the 17th ACM Conference on Recommender Systems, 101\u2013111."},{"key":"e_1_3_2_39_2","first-page":"5307","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Li Fan","year":"2024","unstructured":"Fan Li, Xu Si, Shisong Tang, Dingmin Wang, Kunyan Han, Bing Han, Guorui Zhou, Yang Song, and Hechang Chen. 2024. Contextual distillation model for diversified recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5307\u20135316."},{"key":"e_1_3_2_40_2","first-page":"726","volume-title":"Proceedings of the 18th ACM International Conference on Web Search and Data Mining","author":"Li Wuchao","year":"2025","unstructured":"Wuchao Li, Rui Huang, Haijun Zhao, Chi Liu, Kai Zheng, Qi Liu, Na Mou, Guorui Zhou, Defu Lian, Yang Song, et al. 2025. DimeRec: A unified framework for enhanced sequential recommendation via generative diffusion models. In Proceedings of the 18th ACM International Conference on Web Search and Data Mining, 726\u2013734."},{"issue":"3","key":"e_1_3_2_41_2","first-page":"1","article-title":"DiffuRec: A diffusion model for sequential recommendation","volume":"42","author":"Li Zihao","year":"2023","unstructured":"Zihao Li, Aixin Sun, and Chenliang Li. 2023. DiffuRec: A diffusion model for sequential recommendation. ACM Transactions on Information Systems 42, 3 (2023), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_42_2","unstructured":"Yile Liang and Tieyun Qian. 2021. Recommending accurate and diverse items using bilateral branch network. arXiv:2101.00781. Retrieved from https:\/\/arxiv.org\/abs\/2101.00781"},{"key":"e_1_3_2_43_2","first-page":"3332","volume-title":"Proceedings of the 31st ACM International Conference on Information and Knowledge Management","author":"Liu Junning","year":"2022","unstructured":"Junning Liu, Xinjian Li, Bo An, Zijie Xia, and Xu Wang. 2022. Multi-faceted hierarchical multi-task learning for recommender systems. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management, 3332\u20133341."},{"key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"102488","DOI":"10.1016\/j.is.2024.102488","article-title":"A generative and discriminative model for diversity-promoting recommendation","volume":"128","author":"Liu Yuli","year":"2025","unstructured":"Yuli Liu. 2025. A generative and discriminative model for diversity-promoting recommendation. Information Systems 128 (2025), 102488.","journal-title":"Information Systems"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1145\/3627673.3679610","volume-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","author":"Liu Yuli","year":"2024","unstructured":"Yuli Liu, Min Liu, Christian Walder, and Lexing Xie. 2024. A universal sets-level optimization framework for next set recommendation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 1544\u20131554."},{"key":"e_1_3_2_46_2","first-page":"132","volume-title":"Proceedings of the 5th International Conference on Crowd Science and Engineering","author":"Liu Yong","year":"2021","unstructured":"Yong Liu, Zhiqi Shen, Yinan Zhang, and Lizhen Cui. 2021. Diversity-promoting deep reinforcement learning for interactive recommendation. In Proceedings of the 5th International Conference on Crowd Science and Engineering, 132\u2013139."},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","unstructured":"Yuli Liu Christian Walder and Lexing Xie. 2022. Determinantal point process likelihoods for sequential recommendation. arXiv:2204.11562. Retrieved from https:\/\/arxiv.org\/abs\/2204.11562","DOI":"10.1145\/3477495.3531965"},{"key":"e_1_3_2_48_2","first-page":"1036","volume-title":"Proceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE)","author":"Liu Yuli","year":"2024","unstructured":"Yuli Liu, Christian Walder, and Lexing Xie. 2024. Learning k-determinantal point processes for personalized ranking. In Proceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1036\u20131049."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1145\/3637528.3671733","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Liu Yuli","year":"2024","unstructured":"Yuli Liu, Christian Walder, Lexing Xie, and Yiqun Liu. 2024. Probabilistic attention for sequential recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1956\u20131967."},{"key":"e_1_3_2_50_2","unstructured":"Zhiwei Liu Yongjun Chen Jia Li Philip S. Yu Julian McAuley and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv:2108.06479. Retrieved from https:\/\/arxiv.org\/abs\/2108.06479"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s10618-021-00744-w","article-title":"Sequential recommendation with metric models based on frequent sequences","volume":"35","author":"Lonjarret Corentin","year":"2021","unstructured":"Corentin Lonjarret, Roch Auburtin, C\u00e9line Robardet, and Marc Plantevit. 2021. Sequential recommendation with metric models based on frequent sequences. Data Mining and Knowledge Discovery 35 (2021), 1087\u20131133.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"5045","DOI":"10.1609\/aaai.v34i04.5945","article-title":"Memory augmented graph neural networks for sequential recommendation","volume":"34","author":"Ma Chen","year":"2020","unstructured":"Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, and Mark Coates. 2020. Memory augmented graph neural networks for sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 5045\u20135052.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1145\/2566486.2568012","volume-title":"Proceedings of the 23rd International Conference on World Wide Web","author":"Nguyen Tien T.","year":"2014","unstructured":"Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the filter bubble: The effect of using recommender systems on content diversity. In Proceedings of the 23rd International Conference on World Wide Web, 677\u2013686."},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/3556702.3556846","volume-title":"Proceedings of the Recommender Systems Challenge 2022","author":"Panagiotakis Costas","year":"2022","unstructured":"Costas Panagiotakis and Harris Papadakis. 2022. Session-based recommendation by combining probabilistic models and LSTM. In Proceedings of the Recommender Systems Challenge 2022, 39\u201344."},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/2959100.2959149","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"Parambath Shameem A. Puthiya","year":"2016","unstructured":"Shameem A. Puthiya Parambath, Nicolas Usunier, and Yves Grandvalet. 2016. A coverage-based approach to recommendation diversity on similarity graph. In Proceedings of the 10th ACM Conference on Recommender Systems, 15\u201322."},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence","author":"Qin Lijing","year":"2013","unstructured":"Lijing Qin and Xiaoyan Zhu. 2013. Promoting diversity in recommendation by entropy regularizer. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Citeseer."},{"key":"e_1_3_2_57_2","first-page":"548","volume-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","author":"Qin Xiuyuan","year":"2024","unstructured":"Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Guanfeng Liu, Fuzhen Zhuang, and Victor S. Sheng. 2024. Intent contrastive learning with cross subsequences for sequential recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 548\u2013556."},{"key":"e_1_3_2_58_2","first-page":"813","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining","author":"Qiu Ruihong","year":"2022","unstructured":"Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022. Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, 813\u2013823."},{"key":"e_1_3_2_59_2","unstructured":"Steffen Rendle Christoph Freudenthaler Zeno Gantner and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618. Retrieved from https:\/\/arxiv.org\/abs\/1205.2618"},{"key":"e_1_3_2_60_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_2_61_2","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1145\/3357384.3357895","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, 1441\u20131450."},{"key":"e_1_3_2_62_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."},{"issue":"1","key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3522673","article-title":"Sequential recommendation with multiple contrast signals","volume":"41","author":"Wang Chenyang","year":"2023","unstructured":"Chenyang Wang, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. Sequential recommendation with multiple contrast signals. ACM Transactions on Information Systems 41, 1 (2023), 1\u201327.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_64_2","article-title":"Modeling dynamic missingness of implicit feedback for recommendation","volume":"31","author":"Wang Menghan","year":"2018","unstructured":"Menghan Wang, Mingming Gong, Xiaolin Zheng, and Kun Zhang. 2018. Modeling dynamic missingness of implicit feedback for recommendation. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 31.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1145\/2766462.2767694","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. 2015. Learning hierarchical representation model for next basket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 403\u2013412."},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"12748","DOI":"10.1609\/aaai.v39i12.33390","article-title":"Intent oriented contrastive learning for sequential recommendation","volume":"39","author":"Wang Wuhong","year":"2025","unstructured":"Wuhong Wang, Jianhui Ma, Yuren Zhang, Kai Zhang, Junzhe Jiang, Yihui Yang, Yacong Zhou, and Zheng Zhang. 2025. Intent oriented contrastive learning for sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, 12748\u201312756.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_67_2","first-page":"1605","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Warlop Romain","year":"2019","unstructured":"Romain Warlop, J\u00e9r\u00e9mie Mary, and Mike Gartrell. 2019. Tensorized determinantal point processes for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1605\u20131615."},{"key":"e_1_3_2_68_2","first-page":"2088","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Wu Chuhan","year":"2022","unstructured":"Chuhan Wu, Fangzhao Wu, Tao Qi, Qi Liu, Xuan Tian, Jie Li, Wei He, Yongfeng Huang, and Xing Xie. 2022. FeedRec: News feed recommendation with various user feedbacks. In Proceedings of the ACM Web Conference 2022, 2088\u20132097."},{"key":"e_1_3_2_69_2","first-page":"3870","article-title":"PD-GAN: Adversarial learning for personalized diversity-promoting recommendation","author":"Wu Qiong","year":"2019","unstructured":"Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, and Lu Guan. 2019. PD-GAN: Adversarial learning for personalized diversity-promoting recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI \u201919), 3870\u20133876.","journal-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI \u201919)"},{"key":"e_1_3_2_70_2","first-page":"346","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Wu Shu","year":"2019","unstructured":"Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 346\u2013353."},{"key":"e_1_3_2_71_2","first-page":"1","volume-title":"Proceedings of the 2017 12th international conference on intelligent systems and knowledge engineering (ISKE)","author":"Xia Bin","year":"2017","unstructured":"Bin Xia, Yun Li, Qianmu Li, and Tao Li. 2017. Attention-based recurrent neural network for location recommendation. In Proceedings of the 2017 12th international conference on intelligent systems and knowledge engineering (ISKE). IEEE, 1\u20136."},{"key":"e_1_3_2_72_2","first-page":"1259","volume-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE)","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 1259\u20131273."},{"key":"e_1_3_2_73_2","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.neucom.2020.10.066","article-title":"Long-and short-term self-attention network for sequential recommendation","volume":"423","author":"Xu Chengfeng","year":"2021","unstructured":"Chengfeng Xu, Jian Feng, Pengpeng Zhao, Fuzhen Zhuang, Deqing Wang, Yanchi Liu, and Victor S Sheng. 2021. Long-and short-term self-attention network for sequential recommendation. Neurocomputing 423 (2021), 580\u2013589.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","unstructured":"Chengfeng Xu Pengpeng Zhao Yanchi Liu Victor S. Sheng Jiajie Xu Fuzhen Zhuang Junhua Fang and Xiaofang Zhou. 2019. Graph contextualized self-attention network for session-based recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI \u201919) 3940\u20133946.","DOI":"10.24963\/ijcai.2019\/547"},{"key":"e_1_3_2_75_2","first-page":"3398","volume-title":"Proceedings of the World Wide Web Conference","author":"Xu Chengfeng","year":"2019","unstructured":"Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S. Sheng, S. Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019. Recurrent convolutional neural network for sequential recommendation. In Proceedings of the World Wide Web Conference, 3398\u20133404."},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","unstructured":"Yue Xu Hao Chen Zefan Wang Jianwen Yin Qijie Shen Dimin Wang Feiran Huang Lixiang Lai Tao Zhuang Junfeng Ge et al. 2023. Multi-factor sequential re-ranking with perception-aware diversification. arXiv:2305.12420. Retrieved from https:\/\/arxiv.org\/abs\/2305.12420","DOI":"10.1145\/3580305.3599869"},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1145\/3357384.3358113","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Yan An","year":"2019","unstructured":"An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, and Julian McAuley. 2019. CosRec: 2D convolutional neural networks for sequential recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2173\u20132176."},{"key":"e_1_3_2_78_2","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1145\/3543507.3583361","volume-title":"Proceedings of the ACM Web Conference 2023","author":"Yang Yuhao","year":"2023","unstructured":"Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, and Kangyi Lin. 2023. Debiased contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2023, 1063\u20131073."},{"key":"e_1_3_2_79_2","first-page":"729","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, 729\u2013732."},{"issue":"1","key":"e_1_3_2_80_2","doi-asserted-by":"crossref","first-page":"e14358","DOI":"10.1111\/ele.14358","article-title":"How collectively integrated are ecological communities","volume":"27","author":"Zelnik Yuval R.","year":"2024","unstructured":"Yuval R. Zelnik, Nuria Galiana, Matthieu Barbier, Michel Loreau, Eric Galbraith, and Jean-Fran\u00e7ois Arnoldi. 2024. How collectively integrated are ecological communities? Ecology Letters 27, 1 (2024), e14358.","journal-title":"Ecology Letters"},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1145\/3543507.3583513","volume-title":"Proceedings of the ACM Web Conference 2023","author":"Zhang Chi","year":"2023","unstructured":"Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, and Li Li. 2023. Denoising and prompt-tuning for multi-behavior recommendation. In Proceedings of the ACM Web Conference 2023, 1355\u20131363."},{"key":"e_1_3_2_82_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1145\/1454008.1454030","volume-title":"Proceedings of the 2008 ACM Conference on Recommender Systems","author":"Zhang Mi","year":"2008","unstructured":"Mi Zhang and Neil Hurley. 2008. Avoiding monotony: Improving the diversity of recommendation lists. In Proceedings of the 2008 ACM Conference on Recommender Systems, 123\u2013130."},{"issue":"5","key":"e_1_3_2_83_2","first-page":"4741","article-title":"Dynamic graph neural networks for sequential recommendation","volume":"35","author":"Zhang Mengqi","year":"2022","unstructured":"Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. 2022. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4741\u20134753.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_84_2","doi-asserted-by":"crossref","unstructured":"Yixin Zhang Yong Liu Yonghui Xu Hao Xiong Chenyi Lei Wei He Lizhen Cui and Chunyan Miao. 2022. Enhancing sequential recommendation with graph contrastive learning. arXiv:2205.14837. Retrieved from https:\/\/arxiv.org\/abs\/2205.14837","DOI":"10.24963\/ijcai.2022\/333"},{"key":"e_1_3_2_85_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1145\/3240323.3240374","volume-title":"Proceedings of the 12th ACM Conference on Recommender Systems","author":"Zhao Xiangyu","year":"2018","unstructured":"Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2018. Deep reinforcement learning for page-wise recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems, 95\u2013103."},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1007\/s11280-020-00824-9","article-title":"Hybrid graph convolutional networks with multi-head attention for location recommendation","volume":"23","author":"Zhong Ting","year":"2020","unstructured":"Ting Zhong, Shengming Zhang, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, and Jin Wu. 2020. Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23 (2020), 3125\u20133151.","journal-title":"World Wide Web"},{"key":"e_1_3_2_87_2","first-page":"1893","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Zhou Kun","year":"2020","unstructured":"Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 1893\u20131902."},{"key":"e_1_3_2_88_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1145\/3604915.3608786","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"Zhou Peilin","year":"2023","unstructured":"Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, and Sunghun Kim. 2023. Equivariant contrastive learning for sequential recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, 129\u2013140."},{"key":"e_1_3_2_89_2","doi-asserted-by":"crossref","first-page":"3854","DOI":"10.1145\/3589334.3645661","volume-title":"Proceedings of the ACM Web Conference 2024","author":"Zhou Peilin","year":"2024","unstructured":"Peilin Zhou, You-Liang Huang, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, and Sunghun Kim. 2024. Is contrastive learning necessary? A study of data augmentation vs contrastive learning in sequential recommendation. In Proceedings of the ACM Web Conference 2024, 3854\u20133863."},{"issue":"3","key":"e_1_3_2_90_2","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1109\/TCBB.2020.2994780","article-title":"CNN-RNN based intelligent recommendation for online medical pre-diagnosis support","volume":"18","author":"Zhou Xiaokang","year":"2020","unstructured":"Xiaokang Zhou, Yue Li, and Wei Liang. 2020. CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE\/ACM Transactions on Computational Biology and Bioinformatics 18, 3 (2020), 912\u2013921.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"e_1_3_2_91_2","first-page":"2810","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zou Lixin","year":"2019","unstructured":"Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019. Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2810\u20132818."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3796521","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:15:09Z","timestamp":1774440909000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3796521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":90,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3,31]]}},"alternative-id":["10.1145\/3796521"],"URL":"https:\/\/doi.org\/10.1145\/3796521","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]},"assertion":[{"value":"2025-04-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}