智能信息与大数据实验室

@article{10.1145/3722553, author = {Su, Yixin and Jiang, Wei and Lin, Fangquan and Yang, Cheng and Erfani, Sarah M. and Gan, Junhao and Zhao, Yunxiang and Li, Ruixuan and Zhang, Rui}, title = {Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios}, year = {2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {1046-8188}, url = {https://doi.org/10.1145/3722553}, doi = {10.1145/3722553}, abstract = {In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jointly determined by two types of factors: intrinsic factors, which reflect consistent user preference, and extrinsic factors, which reflect external incentives that may vary in different contexts. Differentiating between intrinsic and extrinsic factors helps learn user behaviors better. However, existing studies have only considered differentiating them from a single, pre-defined context (e.g., time or location), ignoring the fact that a user's extrinsic factors may be influenced by the interplay of various contexts at the same time. In this paper, we propose the Intrinsic-Extrinsic Disentangled Recommendation (IEDR) model, a generic framework that differentiates intrinsic from extrinsic factors considering various contexts simultaneously, enabling more accurate differentiation of factors and hence the improvement of recommendation accuracy. IEDR contains a context-invariant contrastive learning component to capture intrinsic factors, and a disentanglement component to extract extrinsic factors under the interplay of various contexts. The two components work together to achieve effective factor learning. Extensive experiments on real-world datasets demonstrate IEDR’s effectiveness in learning disentangled factors and significantly improving recommendation accuracy by up to 4\% in NDCG.}, note = {Just Accepted}, journal = {ACM Trans. Inf. Syst.}, month = mar, keywords = {Recommender Systems, Intrinsic and Extrinsic Factors, Contrastive Learning, Disentanglement, Mutual Information} }

@article{uncompleted:EvdCLIP, author = {Guanghao Meng and Sunan He and Jinpeng Wang and Tao Dai and Letian Zhang and Jieming Zhu and Qing Li and Gang Wang and Rui Zhang and Yong Jiang}, title = {EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models}, year = {2025}, journal = {AAAI 2025}, }

@article{DBLP:journals/pacmmod/GuFYCLZ24, author = {Tu Gu and Kaiyu Feng and Jingyi Yang and Gao Cong and Cheng Long and Rui Zhang}, title = {BT-Tree: {A} Reinforcement Learning Based Index for Big Trajectory Data}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/pacmmod/GuFYCLZ24.bib}, timestamp = {Wed, 06 Nov 2024 00:00:00 +0100}, doi = {10.1145/3677130}, url = {https://doi.org/10.1145/3677130}, year = {2025}, pages = {194:1--194:27}, number = {4}, volume = {2}, journal = {Proc. {ACM} Manag. Data}, }

@inproceedings{DBLP:conf/mm/WeiY0DC24, author = {Haojie Wei and Jun Yuan and Rui Zhang and Quanyu Dai and Yueguo Chen}, editor = {Jianfei Cai and Mohan S. Kankanhalli and Balakrishnan Prabhakaran and Susanne Boll and Ramanathan Subramanian and Liang Zheng and Vivek K. Singh and Pablo C{\'{e}}sar and Lexing Xie and Dong Xu}, title = {{MAJL:} {A} Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation}, booktitle = {Proceedings of the 32nd {ACM} International Conference on Multimedia, {MM} 2024, Melbourne, VIC, Australia, 28 October 2024 - 1 November 2024}, pages = {8623--8632}, publisher = {{ACM}}, year = {2024}, url = {https://doi.org/10.1145/3664647.3680985}, doi = {10.1145/3664647.3680985}, timestamp = {Wed, 06 Nov 2024 22:17:34 +0100}, biburl = {https://dblp.org/rec/conf/mm/WeiY0DC24.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, }

@article{DBLP:journals/csur/ZhaoDYWZCLSX24, author = {Xiaoyan Zhao and Yang Deng and Min Yang and Lingzhi Wang and Rui Zhang and Hong Cheng and Wai Lam and Ying Shen and Ruifeng Xu}, title = {A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers}, journal = {{ACM} Comput. Surv.}, volume = {56}, number = {11}, pages = {293:1--293:39}, year = {2024}, url = {https://doi.org/10.1145/3674501}, doi = {10.1145/3674501}, timestamp = {Thu, 14 Nov 2024 14:26:12 +0100}, biburl = {https://dblp.org/rec/journals/csur/ZhaoDYWZCLSX24.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, }

@inproceedings{DBLP:conf/www/ShenZ0ZX24, author = {Xiaoteng Shen and Rui Zhang and Xiaoyan Zhao and Jieming Zhu and Xi Xiao}, title = {{PMG} : Personalized Multimodal Generation with Large Language Models}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/www/ShenZ0ZX24.bib}, timestamp = {Thu, 27 Jun 2024 01:00:00 +0200}, doi = {10.1145/3589334.3645633}, url = {https://doi.org/10.1145/3589334.3645633}, year = {2024}, publisher = {{ACM}}, pages = {3833--3843}, booktitle = {Proceedings of the {ACM} on Web Conference 2024, {WWW} 2024, Singapore, May 13-17, 2024}, editor = {Tat{-}Seng Chua and Chong{-}Wah Ngo and Ravi Kumar and Hady W. Lauw and Roy Ka{-}Wei Lee}, }

@inproceedings{DBLP:conf/kdd/LiuZYDD0ZZD24, author = {Qijiong Liu and Jieming Zhu and Yanting Yang and Quanyu Dai and Zhaocheng Du and Xiao{-}Ming Wu and Zhou Zhao and Rui Zhang and Zhenhua Dong}, title = {Multimodal Pretraining, Adaptation, and Generation for Recommendation: {A} Survey}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/kdd/LiuZYDD0ZZD24.bib}, timestamp = {Fri, 20 Sep 2024 01:00:00 +0200}, doi = {10.1145/3637528.3671473}, url = {https://doi.org/10.1145/3637528.3671473}, year = {2024}, publisher = {{ACM}}, pages = {6566--6576}, booktitle = {Proceedings of the 30th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, {KDD} 2024, Barcelona, Spain, August 25-29, 2024}, editor = {Ricardo Baeza{-}Yates and Francesco Bonchi}, }

@inproceedings{DBLP:conf/www/LiWDZYZX24, author = {Yushen Li and Jinpeng Wang and Tao Dai and Jieming Zhu and Jun Yuan and Rui Zhang and Shu{-}Tao Xia}, title = {{RAT:} Retrieval-Augmented Transformer for Click-Through Rate Prediction}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/www/LiWDZYZX24.bib}, timestamp = {Wed, 24 Jul 2024 01:00:00 +0200}, doi = {10.1145/3589335.3651550}, url = {https://doi.org/10.1145/3589335.3651550}, year = {2024}, publisher = {{ACM}}, pages = {867--870}, booktitle = {Companion Proceedings of the {ACM} on Web Conference 2024, {WWW} 2024, Singapore, Singapore, May 13-17, 2024}, editor = {Tat{-}Seng Chua and Chong{-}Wah Ngo and Roy Ka{-}Wei Lee and Ravi Kumar and Hady W. Lauw}, }

@article{DBLP:journals/csur/LiGTTHLZ24, author = {Shiwei Li and Huifeng Guo and Xing Tang and Ruiming Tang and Lu Hou and Ruixuan Li and Rui Zhang}, title = {Embedding Compression in Recommender Systems: {A} Survey}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/csur/LiGTTHLZ24.bib}, timestamp = {Sat, 13 Apr 2024 01:00:00 +0200}, doi = {10.1145/3637841}, url = {https://doi.org/10.1145/3637841}, year = {2024}, pages = {130:1--130:21}, number = {5}, volume = {56}, journal = {{ACM} Comput. Surv.}, }

@article{DBLP:journals/tkde/ZhangSTZYCQ24, author = {Rui Zhang and Yixin Su and Bayu Distiawan Trisedya and Xiaoyan Zhao and Min Yang and Hong Cheng and Jianzhong Qi}, title = {AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment Enabled by Large Language Models}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/tkde/ZhangSTZYCQ24.bib}, timestamp = {Fri, 30 Aug 2024 01:00:00 +0200}, doi = {10.1109/TKDE.2023.3325484}, url = {https://doi.org/10.1109/TKDE.2023.3325484}, year = {2024}, pages = {2357--2371}, number = {6}, volume = {36}, journal = {{IEEE} Trans. Knowl. Data Eng.}, }