Multi-Scale Collaborative Distillation Graph Neural Networks for Session-Based Recommendation

作者：Jianping Gou, Youhui Cheng, Benteng Ma, Lan Du, Xin Luo, Yi Zhang · 发表于：IEEE Transactions on Services Computing · 年份：2025 · DOI：10.1109/tsc.2025.3637009 · 被引用次数：1 · 研究领域：Recommender Systems and Techniques、Advanced Graph Neural Networks、Machine Learning in Healthcare

Session-based recommendation (SBR) in service computing is pivotal in predicting a user's next action based on their current anonymous session. While Graph Neural Network (GNN)-based methods have shown promise in capturing intricate item transformation relationships within sessions, they often fall short in accurately modeling user preferences. This is primarily due to the common practice of solely considering the last item in the session as the user's current interest, neglecting potentially valuable information embedded in other session items which is essential for capturing user global preferences. Moreover, existing models typically optimize performance solely through cross-entropy loss between predicted items and ground truth labels, while overlooking latent valuable knowledge embedded in intermediate features and item-item relationships that lends support to the model in accurately capturing and modeling user preferences. To address these shortcomings, we propose Multi-Scale Collaborative Distillation (MSCD) for SBR. Our approach introduces a current interest adaptive selection module, which dynamically selects appropriate item embeddings as session-local embeddings by evaluating the importance of each item within the session. This allows for a more accurate capture of the user's current true preferences. Additionally, we propose collaborative knowledge distillation, where multiple models are trained concurrently, enabling the transfer of three types of knowledge includ...