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A logistic matrix factorization recommendation algorithm based on polynomial coefficient perturbation
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AI
DOI：10.1016/j.engappai.2026.114426.png)
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Abstract
En 中文
Most current privacy-preserving recommendation schemes designed for explicit ratings have made significant progress. However, the privacy concerns arising from implicit feedback data have not received sufficient attention. To this end, we propose a novel logistic matrix factorization recommendation algorithm based on polynomial coefficient perturbation. This algorithm adopts logistic matrix factorization to fit implicit feedback data, while introducing perturbation into the objective function to protect user privacy. To manage the privacy budget efficiently, Taylor expansion is leveraged to approximate the objective function as a polynomial. Noise is only added to the first-order term to satisfy the differential privacy constraint, thereby minimizing the potential error accumulation. Theoretical analyses rigorously prove the privacy level and data utility of the proposed method. Experimental results on multiple datasets further demonstrate that our scheme can effectively protect user privacy while delivering good recommendation performance.
Keywords:
logistic matrix factorization
implicit feedback
differential privacy
polynomial coefficient perturbation
recommendation system
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