2024/08/14 by CanYi Liu, Wei Li, Liu, CanYi +7
Computer Science · #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2408.07427
openalex publication_date 2024/08/14 · openalex created_date 2024/09/11 · openalex updated_date 2026/07/28
Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. Despite their attractive performance, existing LLM-based SRS still exhibit some limitations, including neglecting intra-item relations, ignoring long-term collaborative knowledge and using inflexible architecture designs for adaption. To alleviate these issues, we propose an LLM-based sequential recommendation model named DARec. Built on top of coarse-grained adaption for capturing inter-item relations, DARec is further enhanced with (1) context masking that models intra-item relations to help LLM better understand token and item semantics in the context of SRS, (2) collaborative knowledge injection that helps LLM incorporate long-term collaborative knowledge, and (3) a dynamic adaption mechanism that uses Bayesian optimization to flexibly choose layer-wise adapter architectures in order to better incorporate different sequential information. Extensive experiments demonstrate that DARec can effectively handle sequential recommendation in a dynamic and adaptive manner.