Enhancing Content-based Recommendation via Large Language Model
<|reference_start|>Enhancing Content-based Recommendation via Large Language Model: 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.<|reference_end|>
@article{xu2024enhancing,
    title={Enhancing Content-based Recommendation via Large Language Model},
    author={Wentao Xu, Qianqian Xie, Shuo Yang, Jiangxia Cao, Shuchao Pang},
    journal={arXiv preprint arXiv:2404.00236},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00236},
    primaryClass={cs.IR cs.CL}
}