Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network

Abstract

Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in a sparse matrix. This inherent sparsity poses a challenge because it necessitates accurately and effectively filling in these gaps to provide users with meaningful and personalized recommendations. Our solution addresses sparsity in recommendations by incorporating diverse data sources, including trust statements and an imputation graph. The trust graph captures user relationships and trust levels, working in conjunction with an imputation graph, which is constructed by estimating the missing rates of each user based on the user–item matrix using the average rates of the most similar users. Combined with the user–item rating graph, an attention mechanism fine tunes the influence of these graphs, resulting in more personalized and effective recommendations. Our method consistently outperforms state-of-the-art recommenders in real-world dataset evaluations, underscoring its potential to strengthen recommendation systems and mitigate sparsity challenges. • Triplet path GCN captures nonlinear relationships between users and items. • Sparsity is addressed by adding both imputation and social relation graphs. • Imputation matrix is pre-constructed in preprocessing and used during learning. • Attention mechanism defines graph contributions in the embedded representation space. • Extensive experiments validate the method on two datasets in various settings.

Topics & Concepts