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.

Related papers

• Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion [2.2194815687410627]
  This paper proposes a cold start recommendation model that integrates contrastive learning. The model dynamically adjusts the weights of key features through an adaptive feature selection module. It integrates user attributes, item meta-information, and contextual features by combining a multimodal feature fusion mechanism.
  arXiv  Detail & Related papers  (2025-02-05T23:15:31Z)
• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward [118.65089648651308]
  This paper introduces a novel framework that utilizes detailed video captions as a proxy of video content. We show that applying this tailored reward through DPO significantly improves the performance of video LMMs on video Question Answering (QA) tasks.
  arXiv  Detail & Related papers  (2024-04-01T17:28:16Z)
• VideolandGPT: A User Study on a Conversational Recommender System [0.14495144578817493]
  We introduce VideolandGPT, a recommender system for a Video-on-Demand (VOD) platform, Videoland, which uses ChatGPT to select from a predetermined set of contents. We evaluate ranking metrics, user experience, and fairness of recommendations, comparing a personalised and a non-personalised version of the system.
  arXiv  Detail & Related papers  (2023-09-07T11:24:47Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Constrained Reinforcement Learning for Short Video Recommendation [18.492477839791274]
  Short videos on social media platforms pose new challenges to optimize recommender systems. We propose a two-stage reinforcement learning approach based on actor-critic framework. Our approach has been fully launched in the production system to optimize user experiences.
  arXiv  Detail & Related papers  (2022-05-26T09:36:20Z)
• Fast Multi-Step Critiquing for VAE-based Recommender Systems [27.207067974031805]
  We present M&Ms-VAE, a novel variational autoencoder for recommendation and explanation. We train the model under a weak supervision scheme to simulate both fully and partially observed variables. We then leverage the generalization ability of a trained M&Ms-VAE model to embed the user preference and the critique separately.
  arXiv  Detail & Related papers  (2021-05-03T12:26:09Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)
• MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection [110.87712780017819]
  We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. We conduct experiments on two public datasets and a real-world production dataset.
  arXiv  Detail & Related papers  (2020-01-22T16:05:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.