Behavior-Aware Aggregation and Graph Attention Fusion Network for Multi-Behavior Recommendation

In real world recommender systems, users interact with the same item through multiple behaviors—click, favorite, add-to-cart, and purchase that are heterogeneous and time dependent. Existing multi-behavior approaches suffer from three gaps: (i) static behavior categorization that ignores how frequency, intensity, and anomalies dynamically affect preference; (ii) shallow or fixed graph propagation that misses higher-order preferences and cross-graph semantics; and (iii) weak handling of temporal dependencies, which obscures interest evolution. We propose the Behavior-Aware Aggregation and Graph-Attention Fusion Network (BAGAR) to jointly model behavior differences, temporal dynamics, and semantic relations. BAGAR includes: a behavior-aware aggregation module combining behavior-intensity pooling, core-preference pooling, and anomaly filtering, fused by an adaptive graph multilayer perceptron; a multi-hop graph-attention module that forms hierarchical channels for first-order direct interests, higher-order latent preferences, and global semantic paths, reconciled by gating and a joint behavior-time dependency mechanism that embeds behavior relations and inter-event time gaps within a unified attention framework to track preference evolution. Experiments on Taobao, Tmall, and Yelp show that BAGAR outperforms strong baselines on hit rate and normalized discounted cumulative gain, validating its effectiveness for complex behavior modeling.