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.

中文翻译：

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一种用于视频和评论联合推荐的大语言模型增强序列推荐器

在在线视频平台中，阅读或撰写有趣视频的评论已成为视频观看体验的重要组成部分。然而，现有的视频推荐系统主要对用户与视频的交互行为进行建模，缺乏对用户行为建模中评论的考虑。在本文中，我们提出了一种新颖的推荐方法，称为LSVCR，利用用户与视频和评论的交互历史记录，从而联合进行个性化视频和评论推荐。具体来说，我们的方法由两个关键组件组成，即顺序推荐（SR）模型和补充大语言模型（LLM）推荐器。 SR 模型充当我们方法的主要推荐主干（在部署中保留），允许高效的用户偏好建模。同时，我们利用 LLM 推荐器作为补充组件（在部署中丢弃），以更好地从异构交互行为中捕获潜在的用户偏好。为了整合 SR 模型和补充 LLM 推荐器的优点，我们设计了一个两阶段训练范例。第一阶段是个性化偏好对齐，旨在对齐两个组件的偏好表示，从而增强 SR 模型的语义。第二阶段是面向推荐的微调，其中根据特定目标对对齐增强的SR模型进行微调。视频和评论推荐任务中的大量实验证明了 LSVCR 的有效性。此外，快手平台上的在线A/B测试验证了我们的方法带来的实际好处。特别是，我们的评论观看时间整体显着增加了 4.13%。