TiDGRec: Dual-Graph Modeling with Target-intention Filtering for Session-based Recommendation

Abstract Session-based recommendation (SBR) focuses on forecasting the next item a user is likely to select using brief and anonymous sequences of interactions. Existing methods face three key challenges: (1) difficulty in distinguishing noisy transitions within sessions, (2) absence of explicit modeling for target intent, and (3) misalignment between intra- and inter-session information. We propose TiDGRec (Target-intention aware Dual-Graph Recommender), a framework designed to address these limitations through hierarchical denoising and targetguided dual-graph learning. A Target Proxy Node (TPN) is introduced into the Sequential Transition Graph (STG) to capture user intent representations. An Adaptive Target-aware Sparsifier (ATS) based on dynamic αs, adaptively filters irrelevant transitions. The learned target representation and item embeddings from the Cross-session Co-occurrence Graph (CCG) are jointly input to the Target-guided Cross-graph Filter (TCF) to enhance target-aware global relations. By connecting STG and CCG through shared target signals, TiDGRec forms a dual-graph, dual-target architecture that enhances intent alignment, suppresses semantic noise, and improves overall recommendation quality. A comprehensive evaluation across various benchmark datasets demonstrates that TiDGRec achieves superior performance compared to existing SBR methods.