To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.
Personalized Recommendation Algorithm for Mobile Based on Federated Matrix Factorization