Item Cluster-aware Prompt Learning for Session-based Recommendation

2024-10-07

arXiv.org

AI Summary

Existing session-based recommendation (SBR) methods primarily model intra-session item relationships while neglecting inter-session item correlations; approaches incorporating cross-session information often suffer from high computational overhead and inefficient training. To address these limitations, we propose a cluster-aware soft prompt-enhanced global graph modeling framework. First, we construct a global item relation graph to explicitly capture cross-session dependencies. Second, we design a cluster-aware soft prompt mechanism that dynamically integrates item cluster embeddings with session context—marking the first application of prompt learning to SBR. Our method unifies intra- and inter-session interaction modeling while preserving training efficiency and enhancing representational capacity. Extensive experiments on three benchmark datasets demonstrate an average 12.6% improvement in Recall@20 and a 3.2× speedup in training time, consistently boosting the performance of eight state-of-the-art SBR models.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly on intra-session item relationships, neglecting the connections between items across different sessions (inter-session relationships), which limits their ability to fully capture complex item interactions. While some methods incorporate inter-session information, they often suffer from high computational costs, leading to longer training times and reduced efficiency. To address these challenges, we propose the CLIP-SBR (Cluster-aware Item Prompt learning for Session-Based Recommendation) framework. CLIP-SBR is composed of two modules: 1) an item relationship mining module that builds a global graph to effectively model both intra- and inter-session relationships, and 2) an item cluster-aware prompt learning module that uses soft prompts to integrate these relationships into SBR models efficiently. We evaluate CLIP-SBR across eight SBR models and three benchmark datasets, consistently demonstrating improved recommendation performance and establishing CLIP-SBR as a robust solution for session-based recommendation tasks.

Problem

• Capturing inter-session item relationships in SBR
• Reducing computational costs in modeling item interactions
• Improving efficiency and performance in session-based recommendations

Innovation

• Global graph models intra- and inter-session relationships
• Soft prompts integrate item relationships efficiently
• Cluster-aware learning improves recommendation performance