Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
This work addresses cross-domain recommendation challenges for e-commerce and content platforms, offering a novel integration of LLMs but is incremental in applying existing LLM capabilities to a specific domain.
The paper tackles the overlap dilemma and transition complexity in cross-domain sequential recommendation by proposing LLM4CDSR, which uses large language models to capture semantic item relationships and user preferences, achieving state-of-the-art performance on three public datasets with concrete improvements in metrics like Recall@10 and NDCG@10.
Cross-domain Sequential Recommendation (CDSR) aims to extract the preference from the user's historical interactions across various domains. Despite some progress in CDSR, two problems set the barrier for further advancements, i.e., overlap dilemma and transition complexity. The former means existing CDSR methods severely rely on users who own interactions on all domains to learn cross-domain item relationships, compromising the practicability. The latter refers to the difficulties in learning the complex transition patterns from the mixed behavior sequences. With powerful representation and reasoning abilities, Large Language Models (LLMs) are promising to address these two problems by bridging the items and capturing the user's preferences from a semantic view. Therefore, we propose an LLMs Enhanced Cross-domain Sequential Recommendation model (LLM4CDSR). To obtain the semantic item relationships, we first propose an LLM-based unified representation module to represent items. Then, a trainable adapter with contrastive regularization is designed to adapt the CDSR task. Besides, a hierarchical LLMs profiling module is designed to summarize user cross-domain preferences. Finally, these two modules are integrated into the proposed tri-thread framework to derive recommendations. We have conducted extensive experiments on three public cross-domain datasets, validating the effectiveness of LLM4CDSR. We have released the code online.