Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

arXiv:2309.10435v2 Announce Type: replace-cross
Abstract: Recommender systems are essential for online applications, and sequential recommendation has enjoyed significant prevalence due to its expressive ability to capture dynamic user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason for this issue is that language models often lack an understanding of domain-specific knowledge and item-related textual content. To address this issue, we adopt a new sequential recommendation paradigm and propose LANCER, which leverages the semantic …

abstract applications arxiv cs.cl cs.ir dynamic however information issue language limitations modeling reason recommendation recommender systems systems type user interests