Enhancing Content-based Recommendation via Large Language Model
Wentao Xu Qianqian Xie Shuo Yang Jiangxia Cao Shuchao Pang
In real-world applications, users express different behaviors when they
interact with different items, including implicit click/like interactions, and
explicit comments/reviews interactions. Nevertheless, almost all recommender
works are focused on how to describe user preferences by the implicit
click/like interactions, to find the synergy of people. For the content-based
explicit comments/reviews interactions, some works attempt to utilize them to
mine the semantic knowledge to enhance recommender models. However, they still
neglect the following two points: (1) The content semantic is a universal world
knowledge; how do we extract the multi-aspect semantic information to empower
different domains? (2) The user/item ID feature is a fundamental element for
recommender models; how do we align the ID and content semantic feature space?
In this paper, we propose a `plugin' semantic knowledge transferring method
\textbf{LoID}, which includes two major components: (1) LoRA-based large
language model pretraining to extract multi-aspect semantic information; (2)
ID-based contrastive objective to align their feature spaces. We conduct
extensive experiments with SOTA baselines on real-world datasets, the detailed
results demonstrating significant improvements of our method LoID.