# Comprehensive Personalized Ranking Using One-Bit Comparison Data

## TL;DR

This paper introduces a Bayesian-based comprehensive personalized ranking system that effectively utilizes one-bit comparison data to predict user preferences, leveraging matrix factorization to uncover low-rank structures.

## Contribution

It presents a novel Bayesian approach for personalized ranking using one-bit preference data and connects it to matrix factorization techniques for improved recommendation accuracy.

## Key findings

- The proposed method outperforms existing algorithms in experiments.
- It successfully learns low-rank user-item preference structures.
- Numerical results confirm the effectiveness of the approach.

## 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.