A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation
<|reference_start|>A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation: 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.<|reference_end|>
@article{lai2024a,
    title={A Vlogger-augmented Graph Neural Network Model for Micro-video
  Recommendation},
    author={Weijiang Lai (1 and 2), Beihong Jin (1 and 2), Beibei Li (3), Yiyuan
  Zheng (1 and 2), Rui Zhao (1 and 2) ((1) State Key Laboratory of Computer
  Science, Institute of Software, Chinese Academy of Sciences, Beijing, China,
  (2) University of Chinese Academy of Sciences, Beijing, China, (3) College of
  Computer Science, Chongqing University, Chongqing, China)},
    journal={(2023) Machine Learning and Knowledge Discovery in Databases:
  Applied Data Science and Demo Track (pp. 684-699). Cham: Springer Nature
  Switzerland},
    year={2024},
    doi={10.1007/978-3-031-43427-3_41},
    archivePrefix={arXiv},
    eprint={2405.18260},
    primaryClass={cs.IR}
}