Trustworthy Recommendation for Consumer Electronics Using Hypernetworks

In the context of rapidly evolving electronic technology, numerous consumer electronic products have begun to employ recommendation systems to enhance user experience. Traditional recommendation systems utilize deep learning to predict user ratings for items; however, this approach requires users to share their data, leading to potential distrust in recommendations. The integration of federated learning into recommendation systems can achieve trustworthy recommendations, but current federated recommendation models necessitate multiple instances of user-item interaction data to learn global parameters. Therefore, this paper introduces Trustworthy Recommendation for Consumer Electronics Using Hypernetworks (TRCE) to ensure trustworthy recommendations for consumer electronics while also catering to users’ personalized needs. Initially, hypernetworks are used to rapidly initialize the recommendation model on the client side, with user preferences embedded as inputs to the hypernetwork to obtain personalized preferences; subsequently, within the client-side recommendation model, Item attribute content embeddings function as global information to offer more contextual facts; finally, attention residual blocks are employed to learn the significance of different item attributes. Experiments demonstrate that this method exhibits commendable recommendation performance on the Movielens1M, Hetrec-movielens, and Douban datasets compared to other models, with improvements in MAE, RMSE, and Accuracy of approximately 4.31%, 4.01%, and 3.70%, respectively.