用压缩与鲁棒性增强，让大模型更好理解跨域用户行为。
Heterogeneous User Modeling for LLM-based Recommendation
- 自定义提示词压缩异构行为为专属标记，提升信息密度。
- 引入领域重要性评分，缓解跨域推荐中的性能波动。
- 在多个异构数据集上表现更优，适合开放域推荐场景。
将大语言模型（LLMs）应用于推荐系统已在多个领域展现出显著成效，凸显其在开放域推荐中的潜力。当前面临的挑战在于如何从多领域异构用户行为中有效建模用户偏好。现有方法（如基于ID或语义的建模）普遍存在泛化能力差、难以压缩噪声交互、以及领域跷跷板现象等问题。为此，我们提出异构用户建模（HUM）方法，包含压缩增强器和鲁棒性增强器。压缩增强器通过定制提示词将异构行为压缩为特定标记，同时利用掩码机制促进跨领域知识提取与理解。鲁棒性增强器引入领域重要性评分，指导领域优化以缓解领域跷跷板现象。在多个异构数据集上的大量实验表明，HUM能有效建模用户异质性，在保持高精度的同时具备更强鲁棒性，显著提升开放域推荐性能。
原文摘要 · Abstract (English)
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key challenge to advancing open-domain recommendation lies in effectively modeling user preferences from users' heterogeneous behaviors across multiple domains. Existing approaches, including ID-based and semantic-based modeling, struggle with poor generalization, an inability to compress noisy interactions effectively, and the domain seesaw phenomenon. To address these challenges, we propose a Heterogeneous User Modeling (HUM) method, which incorporates a compression enhancer and a robustness enhancer for LLM-based recommendation. The compression enhancer uses a customized prompt to compress heterogeneous behaviors into a tailored token, while a masking mechanism enhances cross-domain knowledge extraction and understanding. The robustness enhancer introduces a domain importance score to mitigate the domain seesaw phenomenon by guiding domain optimization. Extensive experiments on heterogeneous datasets validate that HUM effectively models user heterogeneity by achieving both high efficacy and robustness, leading to superior performance in open-domain recommendation.
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