Content-Based Collaborative Generation for Recommender Systems
ACM International Conference on Information and Knowledge Management (CIKM), pp. 2420–2430
Abstract
Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recommendation. Although some existing large language model (LLM)-based methods contribute to fusing content information and collaborative signals, they fundamentally rely on textual language generation, which is not fully aligned with the recommendation task. How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge.
Authors 10
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Yidan Wang Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Zhaochun Ren Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Leiden University, Leiden, Netherlands
Shandong University Qingdao, China
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
-
Weiwei Sun Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Jiyuan Yang Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Xin Chen Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
WeChat, Tencent, Beijing, China
Leiden University Leiden, Netherlands
Shandong University Qingdao, China
Tencent Beijing, China
Zhejiang University Hangzhou, China
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Ruobing Xie Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Tencent, Beijing, China
Leiden University Leiden, Netherlands
Shandong University Qingdao, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Xu Zhang Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
WeChat, Tencent, Beijing, China
Leiden University Leiden, Netherlands
Shandong University Qingdao, China
Tencent Beijing, China
Zhejiang University Hangzhou, China
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Pengjie Ren Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Zhumin Chen Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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Xin Xin Aachen
Leiden University · Shandong University · Tencent (China) · Zhejiang University
Affiliation as printed
Shandong University, Qingdao, China
Leiden University Leiden, Netherlands
Tencent Beijing, China
WeChat, Tencent Beijing, China
Zhejiang University Hangzhou, China
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References 41
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W4288089799details pending0citations
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W2962770929details pending0citations
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W3100278010details pending0citations
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W3098638686details pending0citations
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W3215615641details pending0citations