TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

Abstract

Session-based recommendation nowadays plays a vital role in many websites, which aims to predict users' actions based on anonymous sessions. There have emerged many studies that model a session as a sequence or a graph via investigating temporal transitions of items in a session. However, these methods compress a session into one fixed representation vector without considering the target items to be predicted. The fixed vector will restrict the representation ability of the recommender model, considering the diversity of target items and users' interests. In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model. Moreover, TAGNN harnesses the power of graph neural networks to capture rich item transitions in sessions. Comprehensive experiments conducted on real-world datasets demonstrate its superiority over state-of-the-art methods.

5 pages, accepted to SIGIR 2020, authors' version

References in corpus (1)

• Session-based Recommendation with Graph Neural Networks

Cited by in corpus (15)

• STAN: Spatio-Temporal Attention Network for Next Location Recommendation
• Mining Latent Structures for Multimedia Recommendation
• Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
• AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
• Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
• Large Language Models for Intent-Driven Session Recommendations
• Understanding Diversity in Session-Based Recommendation
• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
• Relation-aware Heterogeneous Graph for User Profiling
• GraphPAS: Parallel Architecture Search for Graph Neural Networks
• Adaptive In-Context Learning with Large Language Models for Bundle Generation
• Implicit Session Contexts for Next-Item Recommendations
• Performance Comparison of Session-based Recommendation Algorithms based on GNNs
• CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
• PAS: A Position-Aware Similarity Measurement for Sequential Recommendation