A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation

• URL: http://arxiv.org/abs/2405.18260v1
• Date: Tue, 28 May 2024 15:13:29 GMT
• Title: A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation
• Authors: Weijiang Lai, Beihong Jin, Beibei Li, Yiyuan Zheng, Rui Zhao,
• Abstract summary: We propose a vlogger-augmented graph neural network model VA-GNN, which takes the effect of vloggers into consideration. Specifically, we construct a tripartite graph with users, micro-videos, and vloggers as nodes, capturing user preferences from different views. When predicting the next user-video interaction, we adaptively combine the user preferences for a video itself and its vlogger.
• Score: 7.54949302096348
• License:
• Abstract: Existing micro-video recommendation models exploit the interactions between users and micro-videos and/or multi-modal information of micro-videos to predict the next micro-video a user will watch, ignoring the information related to vloggers, i.e., the producers of micro-videos. However, in micro-video scenarios, vloggers play a significant role in user-video interactions, since vloggers generally focus on specific topics and users tend to follow the vloggers they are interested in. Therefore, in the paper, we propose a vlogger-augmented graph neural network model VA-GNN, which takes the effect of vloggers into consideration. Specifically, we construct a tripartite graph with users, micro-videos, and vloggers as nodes, capturing user preferences from different views, i.e., the video-view and the vlogger-view. Moreover, we conduct cross-view contrastive learning to keep the consistency between node embeddings from the two different views. Besides, when predicting the next user-video interaction, we adaptively combine the user preferences for a video itself and its vlogger. We conduct extensive experiments on two real-world datasets. The experimental results show that VA-GNN outperforms multiple existing GNN-based recommendation models.

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