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

Sequential recommender systems identify user preferences from their past
interactions to predict subsequent items optimally. Although traditional
deep-learning-based models and modern transformer-based models in previous
studies capture unidirectional and bidirectional patterns within user-item
interactions, the importance of temporal contexts, such as individual
behavioral and societal trend patterns, remains underexplored. Notably, recent
models often neglect similarities in users' actions that occur implicitly among
users during analogous timeframes-a concept we term vertical temporal
proximity. These models primarily adapt the self-attention mechanisms of the
transformer to consider the temporal context in individual user actions.
Meanwhile, this adaptation still remains limited in considering the horizontal
temporal proximity within item interactions, like distinguishing between
subsequent item purchases within a week versus a month. To address these gaps,
we propose a sequential recommendation model called TemProxRec, which includes
contrastive learning and self-attention methods to consider temporal
proximities both across and within user-item interactions. The proposed
contrastive learning method learns representations of items selected in close
temporal periods across different users to be close. Simultaneously, the
proposed self-attention mechanism encodes temporal and positional contexts in a
user sequence using both absolute and relative embeddings. This way, our
TemProxRec accurately predicts the relevant items based on the user-item
interactions within a specific timeframe. We validate this work through
comprehensive experiments on TemProxRec, consistently outperforming existing
models on benchmark datasets as well as showing the significance of considering
the vertical and horizontal temporal proximities into sequential
recommendation.

通过对用户行为的纵向和横向时间关系进行建模，我们提出了一个名为 TemProxRec 的顺序推荐模型，该模型通过对比学习和自注意方法来准确预测特定时间段内的相关项目。

基于对比学习和自注意力机制的时间接近度上的顺序推荐

Sequential Recommendation on Temporal Proximities with Contrastive  Learning and Self-Attention

This paper proposes Text mAtching based SequenTial rEcommendation model
(TASTE), which maps items and users in an embedding space and recommends items
by matching their text representations. TASTE verbalizes items and user-item
interactions using identifiers and attributes of items. To better characterize
user behaviors, TASTE additionally proposes an attention sparsity method, which
enables TASTE to model longer user-item interactions by reducing the
self-attention computations during encoding. Our experiments show that TASTE
outperforms the state-of-the-art methods on widely used sequential
recommendation datasets. TASTE alleviates the cold start problem by
representing long-tail items using full-text modeling and bringing the benefits
of pretrained language models to recommendation systems. Our further analyses
illustrate that TASTE significantly improves the recommendation accuracy by
reducing the popularity bias of previous item id based recommendation models
and returning more appropriate and text-relevant items to satisfy users. All
codes are available at this https URL

该研究提出了一种基于文本匹配的顺序推荐模型（TASTE），通过将物品和用户映射到一个嵌入空间，并采用匹配文本表示来推荐物品。TASTE 通过使用物品的标识符和属性来表达物品和用户 - 物品交互，提出了一个关注稀疏性方法，通过减少编码过程中的自注意计算，使 TASTE 能够更好地刻画用户行为。实验证明，TASTE 在广泛使用的顺序推荐数据集上优于现有方法。TASTE 通过使用全文建模来表示长尾物品，并将预训练语言模型的优势引入推荐系统，从而缓解了冷启动问题。进一步的分析表明，TASTE 通过减少先前基于物品 ID 的推荐模型的流行度偏见，返回更加适当和与文本相关的物品以满足用户，显著提高了推荐准确性。所有代码都可以在该 https URL 找到。