Categorical Features of entities in Recommendation Systems Using Graph Neural Networks
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: Graph Neural Networks, Representation learning, recommender engines, Hyper edges
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Abstract: The paper tackles the challenge of capturing entity attribute-specific preferences in recommender systems, with a particular focus on the role of categorical features within GNN-based user-item recommender engines. Despite the significant influence of categorical features such as brand, category, and price bucket on the user decision-making process, there are not many studies dedicated to understanding the GNN's capability to extract and model such preferences effectively. The study extensively compares and tests various techniques for incorporating categorical features into the GNN framework to address this gap. These techniques include one-hot encoding-based node features, category-value nodes, and hyperedges. Three real-world datasets are used to answer what is the most optimal way to incorporate such information. In addition, the paper introduces a novel hyperedge-based method designed to leverage categorical features more effectively compared to existing approaches. The advantage of the hyperedge approach is demonstrated through extensive experiments in effectively modeling categorical features and extracting user attribute-specific preferences.
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Submission Number: 9246
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