IA-GCN: Interactive Graph Convolutional Network for Recommendation

• URL: http://arxiv.org/abs/2204.03827v2
• Date: Mon, 6 May 2024 18:50:23 GMT
• Title: IA-GCN: Interactive Graph Convolutional Network for Recommendation
• Authors: Yinan Zhang, Pei Wang, Congcong Liu, Xiwei Zhao, Hao Qi, Jie He, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao,
• Abstract summary: Graph Convolutional Network (GCN) has become a novel state-of-the-art for Collaborative Filtering (CF) based Recommender Systems (RS) We build bilateral interactive guidance between each user-item pair and propose a new model named IA-GCN (short for InterActive GCN) Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion.
• Score: 13.207235494649343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on the representations. Despite effectiveness, existing algorithms neglect precious interactive features between user-item pairs in the embedding process. When predicting a user's preference for different items, they still aggregate the user tree in the same way, without emphasizing target-related information in the user neighborhood. Such a uniform aggregation scheme easily leads to suboptimal user and item representations, limiting the model expressiveness to some extent. In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN). Specifically, when learning the user representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item. Correspondingly, when learning the item representation, we pay more attention to those neighbors resembling the target user. This leads to interactive and interpretable features, effectively distilling target-specific information through each graph convolutional operation. Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of IA-GCN.

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