LLM-EDT: Large Language Models Enhanced Cross-domain Sequential Recommendation with Dual-phase Training

Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains. Despite current progress, the domain imbalance issue and domain transition issue hinder further development of CDSR. The former presents a phenomenon where interactions in one domain dominate the entire behavior, leading to difficulty in capturing domain-specific features in the other domain. The latter points to the difficulty in capturing users' cross-domain preferences within the mixed interaction sequence, resulting in poor next-item prediction performance for specific domains. With world knowledge and powerful reasoning abilities, Large Language Models (LLMs) partially alleviate the above issues by functioning as both a generator and an encoder. However, current LLMs-enhanced CDSR methods are still under exploration, which fail to recognize the irrelevant noise and rough profiling problems. Thus, to address the aforementioned challenges, we propose an LLMs Enhanced Cross-domain Sequential Recommendation with Dual-phase Training (LLM-EDT). To address the domain imbalance issue while minimizing irrelevant noise, we propose the transferable item augmenter to adaptively generate possible cross-domain behaviors for users. Then, to alleviate the domain transition issue, we introduce a dual-phase training strategy to empower the domain-specific thread with a domain-shared background. As for the rough profiling problem, we devise a domain-aware profiling module to summarize the user's preference in each domain and adaptively aggregate them to generate comprehensive user profiles. The experiments on three public datasets validate the effectiveness of our proposed LLM-EDT. To ease reproducibility, we have released the detailed code online {https://github.com/Applied-Machine-Learning-Lab/SIGIR26_LLM-EDT}.

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