Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
Session-based Recommendation (SBR), seeking to predict a user's next action based on an anonymous session, has drawn increasing attention for its practicability. Most SBR models only rely on the contextual transitions within a short session to learn item representations while neglecting additional valuable knowledge. As such, their model capacity is largely limited by the data sparsity issue caused by short sessions. A few studies have exploited the Modeling of Item Attributes (MIA) to enrich item representations. However, they usually involve specific model designs that can hardly transfer to existing attribute-agnostic SBR models and thus lack universality. In this paper, we propose a model-agnostic framework, named AttrGAU (Attributed Graph Networks with Alignment and Uniformity Constraints), to bring the MIA's superiority into existing attribute-agnostic models, to improve their accuracy and robustness for recommendation. Specifically, we first build a bipartite attributed graph and design an attribute-aware graph convolution to exploit the rich attribute semantics hidden in the heterogeneous item-attribute relationship. We then decouple existing attribute-agnostic SBR models into the graph neural network and attention readout sub-modules to satisfy the non-intrusive requirement. Lastly, we design two representation constraints, i.e., alignment and uniformity, to optimize distribution discrepancy in representation between the attribute semantics and collaborative semantics. Extensive experiments on three public benchmark datasets demonstrate that the proposed AttrGAU framework can significantly enhance backbone models' recommendation performance and robustness against data sparsity and data noise issues. Our implementation codes will be available at https://github.com/ItsukiFujii/AttrGAU.
Code (1)
Tasks
AttributeGraph Neural NetworkSession-Based RecommendationsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Generalizable Person Re-identification via Balancing Alignment and Uniformity
Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalizat…
Data AugmentationGeneralizable Person Re-identificationPerson Re-IdentificationRethinking Graph Masked Autoencoders through Alignment and Uniformity
Self-supervised learning on graphs can be bifurcated into contrastive and generative methods. Contrastive methods, also known as graph contrastive learning (GCL), have dominated graph self-supervised learning in the past…
Contrastive LearningSelf-Supervised LearningGraph-based Alignment and Uniformity for Recommendation
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to…
Collaborative FilteringRecommendation SystemsRepresentation LearningGCL-OT: Graph Contrastive Learning with Optimal Transport for Heterophilic Text-Attributed Graphs
Recently, structure-text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks and language models. However, existing methods ty…
Contrastive LearningERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs
Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics. Recent efforts to integrate Graph Neural Networks (GNNs) and Large Language Models (LLMs) hav…