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

TL;DR

This paper introduces VA-GNN, a graph neural network model that incorporates vlogger information into micro-video recommendations, significantly improving prediction accuracy by modeling user preferences across videos and vloggers.

Contribution

The paper proposes a novel tripartite graph model with cross-view contrastive learning to integrate vlogger data into micro-video recommendation systems.

Findings

VA-GNN outperforms existing GNN-based models in experiments.

Incorporating vlogger information enhances recommendation accuracy.

Cross-view contrastive learning maintains embedding consistency.

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.