Enhancing Content-based Recommendation via Large Language Model
This work addresses a gap in recommender systems by better utilizing explicit content interactions, offering potential benefits for domains relying on user-generated content, though it appears incremental as it builds on existing semantic extraction and alignment techniques.
The paper tackles the problem of enhancing content-based recommendation by leveraging explicit user comments/reviews, which are often neglected, and proposes a method called LoID that uses a LoRA-based large language model to extract multi-aspect semantic information and aligns it with user/item ID features via contrastive learning, resulting in significant improvements over state-of-the-art baselines on real-world datasets.
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.