Federated one-class collaborative filtering via privacy-aware non-sampling matrix factorization

In this paper, we study an emerging and important recommendation problem called federated one-class collaborative filtering (FOCCF). Specifically, we aim to build a recommendation model by exploiting each user's one-class or implicit feedback in a distributed and privacy-aware manner rather than collecting and learning from data in a central server. For the studied problem, there are three important issues, i.e., recommendation accuracy, privacy and efficiency. As a response, we start from the state-of-the-art one-class collaborative filtering (OCCF) method, i.e., non-sampling matrix factorization (NSMF), and propose a novel federated recommendation framework called privacy-aware NSMF (P-NSMF). Our P-NSMF protects user privacy well without sacrificing recommendation accuracy and contains two variants, i.e., P-NSMF(ALS) and P-NSMF(BGD), which are based on alternating least squares (ALS) and batch gradient descent (BGD), respectively. Moreover, we design an improved strategy called group-wise concealing and adopt a secure aggregation technique in our framework for privacy and efficiency. We then analyze the security and complexity of our P-NSMF, and conduct extensive experiments on four public datasets. In particular, compared with the existing methods, our P-NSMF outperforms them in terms of recommendation accuracy, privacy and efficiency, which are important merits for real deployment. (C) 2022 Elsevier B.V. All rights reserved.