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Item Attribute-aware Graph Collaborative Filtering
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AI
DOI：10.1016/j.eswa.2023.122242.png)
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Abstract
En 中文
Collaborative filtering (CF) is a widely used technique in recommender systems. While many CF methods primarily focus on collaborative signals derived from user-item interactions, they often overlook other types of similarities (e.g., user similarity in loving actors, movie similarity in sharing genres, etc.). These similarities offer the fine-grained knowledge to understand users and items, which can complement the collaborative signal. In this paper, we introduce a model that harnesses these additional similarities for improved recommendations. Our approach involves constructing both a user and an item relational graph, based on multiple item attributes. These graphs reflect semantic similarities from various perspectives among users and items. Then, we develop a recommendation framework that employs a dual graph neural network, integrating these graphs into the recommendation process. We name our approach Item Attribute-aware Graph Collaborative Filtering (IAGCF). Through evaluations on six real-world datasets, we found that IAGCF surpasses several state-of-the-art recommenders.
Keywords:
Recommender systems
Collaborative filtering
Graph neural networks
Relation learning
Item attribute
Data mining
Journal
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2.9W
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