Content-Based Collaborative Generation for Recommender Systems
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
- Yidan Wang (ORCID: https://orcid.org/0009-0002-5772-5910)
- Pengjie Ren (ORCID: https://orcid.org/0000-0003-2964-6422)
- Zhumin Chen (ORCID: https://orcid.org/0000-0003-4592-4074)
- Zhaochun Ren (ORCID: https://orcid.org/0000-0002-9076-6565)
- Xin Xin (ORCID: https://orcid.org/0000-0001-6116-9115)
- Xu Zhang (ORCID: https://orcid.org/0009-0006-5685-316X)
- Weiwei Sun (ORCID: https://orcid.org/0000-0002-4817-9500)
- Xin Chen (ORCID: https://orcid.org/0009-0007-2070-141X)
- Jiyuan Yang (ORCID: https://orcid.org/0000-0003-2700-5533)
- Ruobing Xie (ORCID: https://orcid.org/0000-0003-3170-5647)
- Yan Su (ORCID: https://orcid.org/0000-0002-4997-1559)
- Zhouchi Liang
Institutions
- Leiden University (NL)
- Shandong University (CN)
- Tencent (China) (CN)
- Zhejiang University (CN)
Publication Details
- Published
- 2024-10-20
- DOI
- https://doi.org/10.1145/3627673.3679692
- Citations
- 7
- Primary Topic
- Recommender Systems and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.45