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Social recommendations have been widely adopted in substantial domains.Recently, graph neural networks (GNN) have been employed in recommender systemsdue to their success in graph representation learning. However, dealing withthe dynamic property of social network data is a challenge. This researchpresents a novel method that provides social recommendations by incorporatingthe dynamic property of social network data in a heterogeneous graph. The modelaims to capture user preference over time without going through thecomplexities of a dynamic graph by adding period nodes to define users'long-term and short-term preferences and aggregating assigned edge weights. Themodel is applied to real-world data to argue its superior performance.Promising results demonstrate the effectiveness of this model.

Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time Spans