SeSMR: Secure and Efficient Session-based Multimedia Recommendation in Edge Computing

Session-based multimedia recommendation in edge computing remains an important issue for boosting the utilization of services since service composition has increasingly attracted attention. Existing session-based recommendations (SBRs) model the session sequence with multilevel feature extraction in graph neural networks (GNNs). However, multilevel feature extraction in disentangled graph neural  networks causes over-smoothing and privacy leakage. To address the aforementioned problems, Secure and Efficient Session-based  Multimedia recommendation (SeSMR) model is proposed. In the proposed SeSMR model, based on BGV homomorphic encryption, a  ciphertext training submodel  is proposed  to  address  the privacy  leakage, ensuring  the  security in  session-based  recommendation. 

Furthermore, based on the reinforcement of feature activation, a residual attention mechanism is proposed to mitigate over-smoothing  while maintaining the independence of multiple features. Finally, based on location coding, a soft attention mechanism is proposed to  improve the recommendation accuracy, by introducing the position difference information between items into intra-session and inter- session scenarios. Experiments demonstrate that both Recall and MRR metrics exhibit nearly 2%~5% improvement.