Year: 2019
Type: conference-paper
Abstract: 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.
Authors: Aria Ameri, Arindam Bose, Mojtaba Soltanalian
Institution: University of Illinois Chicago
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2948760003