Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

Abstract

Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of more complicated models is the panacea for improving the empirical performance. However, these models achieve relatively marginal improvements with exponential growth in model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that some sophisticated GNN propagations are redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process. To this end, we propose the Multi-Level Attention Mixture Network (Atten-Mixer), which leverages both concept-view and instance-view readouts to achieve multi-level reasoning over item transitions. As simply enumerating all possible high-level concepts is infeasible for large real-world recommender systems, we further incorporate SBR-related inductive biases, i.e., local invariance and inherent priority to prune the search space. Experiments on three benchmarks demonstrate the effectiveness and efficiency of our proposal. We also have already launched the proposed techniques to a large-scale e-commercial online service since April 2021, with significant improvements of top-tier business metrics demonstrated in the online experiments on live traffic.

References in corpus (4)

• Global Context Enhanced Graph Neural Networks for Session-based Recommendation
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
• Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
• Session-aware Linear Item-Item Models for Session-based Recommendation

Cited by in corpus (14)

• AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
• Continual Learning on Dynamic Graphs via Parameter Isolation
• Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
• Homophily-oriented Heterogeneous Graph Rewiring
• Large Language Models for Intent-Driven Session Recommendations
• Disentangling ID and Modality Effects for Session-based Recommendation
• Multi-intent-aware Session-based Recommendation
• FineRec:Exploring Fine-grained Sequential Recommendation
• A Survey on Intent-aware Recommender Systems
• Predicting Information Pathways Across Online Communities
• GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
• Contrastive Learning on Medical Intents for Sequential Prescription Recommendation
• Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
• R3-REC: Reasoning-Driven Recommendation via Retrieval-Augmented LLMs over Multi-Granular Interest Signals