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ID-centric Pre-training for Recommendation

文献类型：期刊论文

作者Wu, Yiqing2,3; Xie, Ruobing4; Zhang, Zhao2; Zhang, Xu4; Zhuang, Fuzhen5,6; Lin, Leyu4; Kang, Zhanhui1; An, Zhulin2; Xu, Yongjun2
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2025-09-01
卷号43期号:5页码:29
关键词Pre-trained Recommendation ID-based Recommendation Multi-domain Recommendation
ISSN号1046-8188
DOI10.1145/3735128
英文摘要Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of pre-trained language model (PLM), some pioneer works adopt PLM for pre-trained recommendation, where modality information is considered universal across domains via PLM. Unfortunately, the behavioral information in ID embeddings is verified to currently dominate in recommendation compared to modality information and thus limits these models' performance. In this work, we propose a novel ID-centric recommendation pre-training paradigm (IDP), which directly transfers informative ID embeddings learned in pre-training domains to item representations in new domains. Specifically, in pre-training stage, besides the ID-based sequential recommendation model, we also build a Cross-domain ID-matcher (CDIM) learned by both behavioral and modality information. In the tuning stage, modality information of new domain items is regarded as a cross-domain bridge built by CDIM. They first adopted to retrieve behaviorally and embeddings are directly adopted to generate downstream new items' embeddings. Through extensive experiments on real-world datasets, we demonstrate that our proposed model significantly outperforms all baselines.
资助项目National Key Research and Development Program of China[2024YFF0729003] ; Young Elite Scientists Sponsorship Program by CAST[2023QNRC001] ; National Natural Science Foundation of China[62206266] ; National Natural Science Foundation of China[62176014] ; Fundamental Research Funds for the Central Universities
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001552823100001
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/41782]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Zhao
作者单位1.Tencent, Shenzhen, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Tencent, Beijing, Peoples R China
5.Beihang Univ, Inst Artificial Intelligence, Beijing, Peoples R China
6.Zhongguancun Lab, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Yiqing,Xie, Ruobing,Zhang, Zhao,et al. ID-centric Pre-training for Recommendation[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2025,43(5):29.
APA Wu, Yiqing.,Xie, Ruobing.,Zhang, Zhao.,Zhang, Xu.,Zhuang, Fuzhen.,...&Xu, Yongjun.(2025).ID-centric Pre-training for Recommendation.ACM TRANSACTIONS ON INFORMATION SYSTEMS,43(5),29.
MLA Wu, Yiqing,et al."ID-centric Pre-training for Recommendation".ACM TRANSACTIONS ON INFORMATION SYSTEMS 43.5(2025):29.

入库方式： OAI收割

来源：计算技术研究所

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