A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
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.
Code (1)
Tasks
Language ModelingLanguage ModellingLarge Language ModelRecommendation SystemsSequential RecommendationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of sequential recommender systems renders them v…
Language ModelingLanguage ModellingLarge Language ModelRecommendation Systems+2MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogu…
Recommendation SystemsC-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation
Sequential recommender systems aim to model users' evolving preferences by capturing patterns in their historical interactions. Recent advances in this area have leveraged deep neural networks and attention mechanisms to…
BenchmarkingRecommendation SystemsSequential RecommendationGraph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integr…
Graph Neural NetworkRecommendation SystemsTransfer LearningIntegrating Textual Embeddings from Contrastive Learning with Generative Recommender for Enhanced Personalization
Recent advances in recommender systems have highlighted the complementary strengths of generative modeling and pretrained language models. We propose a hybrid framework that augments the Hierarchical Sequential Transduct…
Contrastive LearningRecommendation Systems