Integrating Item-Item Semantic Similarity With Item Sequence in Graph Neural Network for Collaborative Filtering

Recent graph neural network-based collaborative filtering models are capable of representing complex user-item interaction information, which is crucial when datasets exhibit high sparsity. Many heterogeneous GNN architectures have been introduced to capture higher-order user-item interaction information, such as neighborhood information among items and item-item interaction sequential information. However, recent GNN-based CF models overlook the relationship between item sequence information and the semantic similarity between items. Hence, in this study, we propose a GNN framework for collaborative filtering that fuses item-item interaction signals derived using item-item interaction sequential graph which in turn derived from the order of all user-item interactions, with semantic similarity, utilizing two graphs: the first is a user-item bipartite graph, and the second is used for propagating item-item semantic relationship information. This helps the recommender system model to effectively represent users’ preferences towards items by using item feature information. We conducted experiments on three benchmark datasets—ML-1M, Amazon Books, and Yelp — using standard ranking-based measures. The results show significant improvements in the recommendation quality over the related state-of-the-art baselines.