Comprehensive Personalized Ranking Using One-Bit Comparison Data
This addresses personalized recommendation problems for users, but appears incremental as it builds on existing matrix factorization methods.
The paper tackles personalized recommendation systems using one-bit comparison data of user preferences, developing a comprehensive personalized ranking (CPR) system with a Bayesian approach and connecting it to matrix factorization. Numerical results verify the algorithm's performance, though no specific metrics are provided.
The task of a personalization system is to recommend items or a set of items according to the users' taste, and thus predicting their future needs. In this paper, we address such personalized recommendation problems for which one-bit comparison data of user preferences for different items as well as the different user inclinations toward an item are available. We devise a comprehensive personalized ranking (CPR) system by employing a Bayesian treatment. We also provide a connection to the learning method with respect to the CPR optimization criterion to learn the underlying low-rank structure of the rating matrix based on the well-established matrix factorization method. Numerical results are provided to verify the performance of our algorithm.