# Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics

arXiv: https://arxiv.org/abs/2406.10250 · HTML: https://mlsanity.com/paper/2406.10250/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Multi-Period Trading via Convex Optimization](https://mlsanity.com/paper/1705.00109/) (2017)
- [Cardinality-constrained Distributionally Robust Portfolio Optimization](https://mlsanity.com/paper/2112.12454/) (2021)
- [Robust portfolio optimization model for electronic coupon allocation](https://mlsanity.com/paper/2405.12865/) (2024)
- [Privacy-preserving recommender system using the data collaboration analysis for distributed datasets](https://mlsanity.com/paper/2406.01603/) (2024)

## Built on by (most cited first, 1 of 1)

- [Robust personalized pricing under uncertainty of purchase probabilities](https://mlsanity.com/paper/2407.15332/) (2024)

## Abstract (arXiv)

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.