用大模型增强跨域推荐，解决数据不均与偏好迁移难题。
LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
- 通过可迁移物品增强器生成跨域行为，缓解数据失衡问题。
- 双阶段训练让模型既学共性又保留领域特性，提升预测准确率。
- 领域感知画像模块精准捕捉用户多域偏好，适合做个性化推荐系统。
跨域序列推荐（CDSR）通过融合多个领域的用户-物品交互信息来丰富行为数据。然而，现有方法仍受数据不平衡和偏好迁移困难的制约：某一领域交互过强，导致难以捕捉其他领域的特征；混合序列中用户跨域偏好难以建模，影响特定领域下的下一物品预测性能。借助大语言模型（LLM）的世界知识与推理能力，本工作提出一种双阶段训练的LLM增强跨域推荐框架（LLM-EDT）。首先设计可迁移物品增强器，自适应生成用户可能的跨域行为，缓解数据不均并减少无关噪声；其次引入双阶段训练策略，使模型在共享背景中学习领域特异性特征；最后构建领域感知画像模块，分别总结各领域偏好并自适应融合，生成完整用户画像。在三个公开数据集上的实验验证了该方法的有效性。代码已开源，便于复现。
原文摘要 · Abstract (English)
Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains. Despite current progress, the imbalance issue and transition issue hinder further development of CDSR. The former one presents a phenomenon that the interactions in one domain dominate the entire behavior, leading to difficulty in capturing the 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 ability, Large Language Models (LLMs) partially alleviate the above issues by performing as 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 make peace with the aforementioned challenges, we proposed an LLMs Enhanced Cross-domain Sequential Recommendation with Dual-phase Training ({LLM-EDT}). To address the imbalance issue while introducing less irrelevant noise, we first propose the transferable item augmenter to adaptively generate possible cross-domain behaviors for users. Then, to alleviate the 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 at {https://anonymous.4open.science/r/LLM-EDT-583F}.
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