Word2Vec-based efficient privacy-preserving shared representation learning for federated recommendation system in a cross-device setting

Recommendation systems have required centralized storage of user data, but due to privacy concerns, recent studies adopted federated learning (FL) that discloses intermediate statistics instead of raw data to build privacy-preserving federated recommendation systems. However, they suffer from inefficiencies in privacy-preserving mechanisms and inaccuracies in simple algorithms that ignore sequential information. This study proposes an extension of Word2Vec for a privacy-preserving federated sequential recommendation system (PPFSRS). This method exploits sequential information to generate contextual item representations for accurate recommendations while concealing privacy-sensitive features efficiently. Specifically, we mixed updates from negative samples to inhibit the direct leakage of purchased items from model updates. In addition, our method computes approximate model updates that can occur when sensitive features only belong to negative samples to prevent inference attacks. In experiments, we used benchmark datasets for recommendation and simulated highly distributed data such that each user stores historical data locally. While preserving privacy with reasonable complexity, the proposed method showed little degradation in recommendation performance compared to FL-based Word2Vec without privacy-preserving mechanisms. Utilizing contextual item representations trained by our method from highly distributed data will be a practical starting point for PPFSRS in a cross-device setting.