BLC: Private Matrix Factorization Recommenders via Automatic Group Learning
This work addresses privacy concerns in recommender systems for users and platforms, but appears incremental as it builds on existing matrix factorization methods with a focus on privacy enhancements.
The paper tackles the problem of enhancing privacy in matrix factorization recommenders by grouping users by interest, introducing the BLC algorithm for privacy-enhanced matrix factorization, and demonstrates that this approach maintains recommendation accuracy without sacrificing privacy.
We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.