RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods rely primarily on textual semantics and often fail to incorporate the most relevant items, limiting the effectiveness of the systems.
Authors
- Linqi Song (ORCID: https://orcid.org/0000-0003-2756-4984)
- Sichun Luo (ORCID: https://orcid.org/0000-0001-8753-9137)
- Hanxu Hou (ORCID: https://orcid.org/0000-0001-7328-9341)
- Xiangyu Chen (ORCID: https://orcid.org/0000-0002-4312-6936)
- Jian Xu (ORCID: https://orcid.org/0000-0001-6201-9215)
- Haoming Huang
Institutions
- City University of Hong Kong (HK)
- Dongguan University of Technology (CN)
- City University of Hong Kong, Shenzhen Research Institute (CN)
- City College of Dongguan University of Technology (CN)
- Alibaba Group (China) (CN)
- Tsinghua University (CN)
Publication Details
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1145/3701716.3715508
- Citations
- 5
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- National Natural Science Foundation of China