Item Cluster-aware Prompt Learning for Session-based Recommendation

TL;DR

This paper introduces CLIP-SBR, a framework that models both intra- and inter-session item relationships using graph mining and prompt learning, significantly improving session-based recommendation accuracy and efficiency.

Contribution

The paper proposes a novel cluster-aware prompt learning framework that effectively captures complex item relationships across sessions with reduced computational costs.

Findings

Consistently improves recommendation performance across multiple models and datasets.

Effectively models both intra- and inter-session item relationships.

Reduces training time compared to existing methods.

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.