Interest Enhanced Subgraph Neural Network With Data Distillation Replay to Continual Learning for Session-Based Recommendation

Abstract Session-based recommendation (SBR) predicts potential items of interest by analyzing user behavior within sessions. In this work, we explore the continual learning for SBR task, a challenging and practical task closely aligned with real-world online recommendation system due to (1) periodic updates of the model may trigger catastrophic forgetting, and (2) the continuous emergence of new interactions reflects rapidly changing user interests. Although recent studies have mitigated catastrophic forgetting by replaying a small subset of historical data into the model, these samples fail to represent the distribution of the entire dataset. Moreover, the research on SBR when examining changes in user interests is confined to offline settings and does not adequately consider multiple time-correlated user interests. This limitation makes it challenging to finely model the rapid changes in user interests in continual learning. To overcome the limitations of traditional data replay, we propose a data distillation framework for SBR, which synthesizes information-rich samples for replay from the entire dataset instead of relying on simple sampling. Furthermore, to address the changing user interests in continual learning scenarios, we developed an Interest Enhanced Sub-graph Neural Network (IES-GNN), which is capable of efficiently extracting and dynamically modeling the evolution of user interests within sessions. Testing on three real-world datasets demonstrates that our approach outperforms several advanced methods in continual learning for SBR task.