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.

在线视频平台中，评论已经成为观看视频时不可或缺的一部分，我们提出了一种名为 LSVCR 的新型推荐方法，通过利用用户与视频和评论的互动历史，联合进行个性化视频和评论推荐。

基于大型语言模型的增强顺序推荐器用于联合视频和评论推荐

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

Path reasoning is a notable recommendation approach that models high-order
user-product relations, based on a Knowledge Graph (KG). This approach can
extract reasoning paths between recommended products and already experienced
products and, then, turn such paths into textual explanations for the user.
Unfortunately, evaluation protocols in this field appear heterogeneous and
limited, making it hard to contextualize the impact of the existing methods. In
this paper, we replicated three state-of-the-art relevant path reasoning
recommendation methods proposed in top-tier conferences. Under a common
evaluation protocol, based on two public data sets and in comparison with other
knowledge-aware methods, we then studied the extent to which they meet
recommendation utility and beyond objectives, explanation quality, and consumer
and provider fairness. Our study provides a picture of the progress in this
field, highlighting open issues and future directions. Source code:
https://github.com/giacoballoccu/rep-path-reasoning-recsys.

通过复制三种最新的路径推理推荐方法，基于知识图谱 (KG) 设计公共评估协议，在两个公共数据集上研究它们的推荐效用、解释质量和公平性等领域，以提高路径推理推荐的性能和应用效果。