TL;DR
This work proposes IFCRec, an Interest-Feature dual calibration recommendation method, which builds upon LightGCN style graph propagation and treats representations from different layers as hierarchical interest views under different-order neighborhoods and achieves superior performance over representative graph recommendation and self-supervised recommendation baselines on most Recall and NDCG metrics.
Sequential recommender systems increasingly rely on transformer attention to model complex dependencies in user histories, yet standard attention is typically unconstrained: any candidate item may attend to any historical interaction. This flexibility can improve accuracy, but it often amplifies spurious correlations,...
This work introduces RPGCN (Relation-Preserving Graph Convolutional Network), a novel hybrid framework that unifies early and intermediate fusion strategies with multi-branch Graph Attention Networks (GATs) for enhanced relation modeling.
This work identifies two dimensions of diversity preferences—validated by psychological theory and readily inferable from interaction data and modeled using graph neighbor sampling: user preferences for diverse content across category levels, and preferences for fine-grained diversity within broad-level interests: user...
Graph-based recommender systems capture high-order user–item dependencies but often underperform in cold-start settings, especially for new items with limited interactions. Inductive graph models generalize to unseen users and items via node features and neighborhood aggregation. However, most rely on shared parameters...
With the widespread adoption of online social networks, social recommender systems have become a key paradigm for mitigating data sparsity and cold-start issues in traditional collaborative filtering. However, existing graph neural network (GNN) based recommendation models face two fundamental structural bottlenecks wh...
Increasing user exposure through highly personalized digital services remains a crucial element for modern e-commerce growth. Contemporary customer consumption patterns involving purchasing multiple items simultaneously have driven the rapid emergence and widespread adoption of Bundle Recommender Systems (BRS). However...
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