A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation

• URL: http://arxiv.org/abs/2403.13574v1
• Date: Wed, 20 Mar 2024 13:14:29 GMT
• Title: A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
• Authors: Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Wayne Xin Zhao, Ji-Rong Wen,
• Abstract summary: We propose a novel recommendation approach called LSVCR to jointly conduct personalized video and comment recommendation. Our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. In particular, we achieve a significant overall gain of 4.13% in comment watch time.
• Score: 77.42486522565295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In online video platforms, reading or writing comments on interesting videos has become an essential part of the video watching experience. However, existing video recommender systems mainly model users' interaction behaviors with videos, lacking consideration of comments in user behavior modeling. In this paper, we propose a novel recommendation approach called LSVCR by leveraging user interaction histories with both videos and comments, so as to jointly conduct personalized video and comment recommendation. Specifically, our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. The SR model serves as the primary recommendation backbone (retained in deployment) of our approach, allowing for efficient user preference modeling. Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors. In order to integrate the merits of the SR model and the supplemental LLM recommender, we design a twostage training paradigm. The first stage is personalized preference alignment, which aims to align the preference representations from both components, thereby enhancing the semantics of the SR model. The second stage is recommendation-oriented fine-tuning, in which the alignment-enhanced SR model is fine-tuned according to specific objectives. Extensive experiments in both video and comment recommendation tasks demonstrate the effectiveness of LSVCR. Additionally, online A/B testing on the KuaiShou platform verifies the actual benefits brought by our approach. In particular, we achieve a significant overall gain of 4.13% in comment watch time.

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