Self-Attention Mechanism-Based Federated Learning Model for Cross Context Recommendation System

The traditional single-context recommendation algorithm is constrained by the sparse connection between users and items and a user/item cold start problem. Based on the review, the text-based cross-context recommendation algorithm extracts user/item comment information in the auxiliary context to alleviate the target context’s data sparsity problem and improve recommendation accuracy. A cross-context recommendation strategy based on self-attention-based federated learning (SAFL) is suggested in this work. In contrast to current algorithms, SAFL fully mixes the target and auxiliary background information. Federated learning addresses crucial challenges including data privacy, data security, data access rights, and access to heterogeneous data by allowing several players to develop a single, strong machine learning model without sharing data. To begin, self-attention mechanism is introduced to model the user’s preference information; then, the information from one field is used to improve the recommendation accuracy of another area; and finally, the information from the two contexts is integrated into the knowledge fusion module and the score prediction module to predict the score. Experiments on the Amazon dataset indicate that MAE and MSE values of SAFL are higher when compared to the current cross-context recommendation model, MAE values rose by 8.4%, 13.2%, and 19.4% across three cross-context datasets.