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A Novel Differentially Private Implicit Recommendation Algorithm Based on Gradient Perturbation Optimization
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AI
DOI：10.1109/TCSS.2025.3645943.png)
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Abstract
En 中文
With the explosive growth of digital data, recommendation systems (RSs) play a crucial role in alleviating the problem of information overload. Implicit feedback data has become the primary data source for training recommendation models because of its richness and ease of collection. Leveraging such data for personalized recommendation services requires a large amount of user historical interactions, which poses serious privacy risks. Differential privacy (DP) has been integrated into implicit recommendation algorithms to protect user privacy. However, due to the inherent characteristics of implicit feedback, the current studies still have certain deficiencies in terms of data utility and privacy level. To this end, this article proposes a novel differentially private implicit recommendation algorithm. It integrates the Bayesian personalized ranking (BPR) matrix factorization (MF) with the Gaussian mechanism in Rényi DP (RDP) and designs an optimization strategy based on the binary index tree (BIT) to alleviate the cumulative errors. The proposed method not only can effectively capture the user preferences from sparse implicit feedback data by maximizing the posterior probability of rankings but also can more precisely manage the privacy budget allocation according to the query matrix. The theoretical analyses prove that the proposed method can satisfy the privacy guarantee and give the upper bound of privacy loss. The experimental results show that our method outperforms several existing advanced methods. It achieves a maximum performance improvement of 6.5% and 6.9% on Hit Rate (HR@10) and Normalized Discounted Cumulative Gain (NDCG@10) at a low privacy budget, which indicates that it can provide good recommendation quality while ensuring a strict privacy level.
Keywords:
Bayesian personalized ranking (BPR)
differential privacy (DP)
implicit feedback
matrix factorization (MF)
recommendation systems (RSs)
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