{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:05:40Z","timestamp":1784138740839,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62272001"],"award-info":[{"award-number":["62272001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3809551","type":"proceedings-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:06:26Z","timestamp":1784135186000},"page":"2420-2430","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DIAURec: Dual-Intent Space Representation Optimization for Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2075-8229","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8709-1088","authenticated-orcid":false,"given":"Yiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-0668","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1480-6522","authenticated-orcid":false,"given":"Lei","family":"Sang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088718"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_3_2_1_3_1","volume-title":"Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation","author":"Belkin Mikhail","year":"2003","unstructured":"Mikhail Belkin and Partha Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, Vol. 15, 6 (2003), 1373-1396."},{"key":"e_1_3_2_1_4_1","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin Mikhail","year":"2006","unstructured":"Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of machine learning research, Vol. 7, 11 (2006).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_5_1","volume-title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In The Eleventh International Conference on Learning Representations. https:\/\/arxiv.org\/abs\/2302","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In The Eleventh International Conference on Learning Representations. https:\/\/arxiv.org\/abs\/2302.08191"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635832"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512090"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3639063"},{"key":"e_1_3_2_1_9_1","volume-title":"Thomas Kipf, and Jakub M Tomczak.","author":"Davidson Tim R","year":"2018","unstructured":"Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. 2018. Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018)."},{"key":"e_1_3_2_1_10_1","first-page":"4171","volume-title":"Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1. 4171-4186."},{"key":"e_1_3_2_1_11_1","volume-title":"Graph Neural Networks for Social Recommendation. In The World Wide Web Conference. 417\u2013426","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In The World Wide Web Conference. 417\u2013426."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/367"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599768"},{"key":"e_1_3_2_1_19_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11010141"},{"key":"e_1_3_2_1_21_1","volume-title":"International conference on learning representations.","author":"Lample Guillaume","year":"2018","unstructured":"Guillaume Lample, Alexis Conneau, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herv\u00e9 J\u00e9gou. 2018. Word translation without parallel data. In International conference on learning representations."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671473"},{"key":"e_1_3_2_1_24_1","first-page":"5879","article-title":"Graph self-supervised learning: A survey","volume":"35","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip S Yu. 2022. Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 6 (2022), 5879-5900.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_1_25_1","volume-title":"From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation. arXiv preprint arXiv:2604.05365","author":"Lu Ziang","year":"2026","unstructured":"Ziang Lu, Lei Sang, Lin Mu, and Yiwen Zhang. 2026. From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation. arXiv preprint arXiv:2604.05365 (2026). arXiv:2604.05365"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33345"},{"key":"e_1_3_2_1_27_1","volume-title":"Probabilistic matrix factorization. Advances in neural information processing systems","author":"Mnih Andriy","year":"2007","unstructured":"Andriy Mnih and Russ R Salakhutdinov. 2007. Probabilistic matrix factorization. Advances in neural information processing systems, Vol. 20 (2007)."},{"key":"e_1_3_2_1_28_1","volume-title":"Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al.","author":"Neelakantan Arvind","year":"2022","unstructured":"Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al., 2022. Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005 (2022)."},{"key":"e_1_3_2_1_29_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615086"},{"key":"e_1_3_2_1_31_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_32_1","article-title":"Balancing Embedding Spectrum for Recommendation","volume":"3","author":"Peng Shaowen","year":"2025","unstructured":"Shaowen Peng, Kazunari Sugiyama, Xin Liu, and Tsunenori Mine. 2025b. Balancing Embedding Spectrum for Recommendation. ACM Transaction Recommendation System, Vol. 3, 4, Article 56 (2025), 25 pages.","journal-title":"ACM Transaction Recommendation System"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696662"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3742472"},{"key":"e_1_3_2_1_35_1","volume-title":"International conference on machine learning. PMLR, 5171-5180","author":"Poole Ben","year":"2019","unstructured":"Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker. 2019. On variational bounds of mutual information. In International conference on machine learning. PMLR, 5171-5180."},{"key":"e_1_3_2_1_36_1","volume-title":"Early stopping-but when? In Neural Networks: Tricks of the trade","author":"Prechelt Lutz","unstructured":"Lutz Prechelt. 2002. Early stopping-but when? In Neural Networks: Tricks of the trade. Springer, 55-69."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645458"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591665"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3661821"},{"key":"e_1_3_2_1_40_1","volume-title":"In International Conference on Learning Representations.","author":"Sheng Leheng","year":"2025","unstructured":"Leheng Sheng, An Zhang, Yi Zhang, Yuxin Chen, Xiang Wang, and Tat-Seng Chua. 2025. Language Representations Can be What Recommenders Need: Findings and Potentials. In In International Conference on Learning Representations."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462833"},{"key":"e_1_3_2_1_42_1","unstructured":"Yang Sui Yu-Neng Chuang Guanchu Wang Jiamu Zhang Tianyi Zhang Jiayi Yuan Hongyi Liu Andrew Wen Shaochen Zhong Na Zou et al. 2025. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503.16419 (2025)."},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2315\u20132325","author":"Wang Chen","unstructured":"Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, and Philip S. Yu. 2024. Collaborative Alignment for Recommendation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2315\u20132325."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539253"},{"key":"e_1_3_2_1_45_1","volume-title":"International conference on machine learning. PMLR, 9929-9939","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International conference on machine learning. PMLR, 9929-9939."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730011"},{"key":"e_1_3_2_1_49_1","volume-title":"MLLMRec-R1: Incentivizing Reasoning Capability in Large Language Models for Multimodal Sequential Recommendation. arXiv preprint arXiv:2603.06243","author":"Wang Yu","year":"2026","unstructured":"Yu Wang, Yonghui Yang, Le Wu, Jiancan Wu, Hefei Xu, and Hui Lin. 2026. MLLMRec-R1: Incentivizing Reasoning Capability in Large Language Models for Multimodal Sequential Recommendation. arXiv preprint arXiv:2603.06243 (2026)."},{"key":"e_1_3_2_1_50_1","volume-title":"Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation. arXiv preprint arXiv:2511.18740","author":"Wang Yu","year":"2025","unstructured":"Yu Wang, Yonghui Yang, Le Wu, Yi Zhang, and Richang Hong. 2025b. Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation. arXiv preprint arXiv:2511.18740 (2025)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298988"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-024-01291-2"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671840"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688104"},{"key":"e_1_3_2_1_58_1","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23)","author":"Yang Liangwei","unstructured":"Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, and Philip S. Yu. 2023. Graph-based Alignment and Uniformity for Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23). 4395\u20134399."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33434"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3540912"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657738"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730098"},{"key":"e_1_3_2_1_64_1","volume-title":"Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1985\u20131994","author":"Zhang Yu","year":"2025","unstructured":"Yu Zhang, Yiwen Zhang, Yi Zhang, Lei Sang, and Yun Yang. 2025c. Unveiling Contrastive Learning' Capability of Neighborhood Aggregation for Collaborative Filtering. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1985\u20131994."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3392335"},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. 425\u2013434","author":"Zheng Lei","unstructured":"Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. 425\u2013434."}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:21:32Z","timestamp":1784136092000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3809551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":66,"alternative-id":["10.1145\/3805712.3809551","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3809551","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}