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Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2016 · 2016
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Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In International Conference on Information and Knowledge Management . ACM, 2615–2623
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019
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Controllable Multi-Interest Framework for Recommendation. In Conference on Knowledge Discovery and Data Mining . ACM, 2942–2951
Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020 · 2020
Earlier work this paper cites.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2311–2320
Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Billion-Scale Similarity Search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2021 · 2021
Cited alongside, same era.
Surrogate for Long-Term User Experience in Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4100–4109
Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, and Minmin Chen. 2022 · 2022
Cited alongside, same era.
Multi-Interest Recommendation on Shopping for Others. In Companion Proceedings of the ACM Web Conference 2023 . 176–179
Shuang Li, Yaokun Liu, Xiaowang Zhang, Yuexian Hou, and Zhiyong Feng. 2023 · 2023
Later among the works it cites.
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems. In Proceedings of the 17th ACM Conference on Recommender Systems . 283–293
Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, and Sunghun Kim. 2023 · 2023
Later among the works it cites.
Divide and Conquer: Towards Better Embedding-Based Retrieval for Recommender Systems from a Multi-Task Perspective. In Companion Proceedings of the ACM Web Conference 2023 . 366–370
Yuan Zhang, Xue Dong, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2023 · 2023
Later among the works it cites.
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