Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics

TL;DR

This paper introduces a robust portfolio optimization model for recommender systems that accounts for uncertainty in estimated statistics, improving both accuracy and diversity of recommendations.

Contribution

It proposes a novel robust optimization approach using cardinality-based uncertainty sets, solvable via mixed-integer linear programming, enhancing recommendation quality.

Findings

Improves recommendation accuracy over traditional models

Enhances diversity of recommended items

Effective across different rating prediction algorithms

Abstract

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean--variance portfolio optimization are subject to inevitable estimation errors. To remedy this situation, we focus on robust optimization techniques that derive reliable solutions to uncertain optimization problems. Specifically, we propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets. This robust portfolio optimization model can be reduced to a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. Experimental results using two publicly available rating datasets demonstrate that our method can improve not only the recommendation accuracy but also the diversity of recommendations compared with conventional mean--variance portfolio optimization models. Notably, our method has the potential to improve the recommendation quality of various rating prediction algorithms.