CRUISE on Quantum Computing for Feature Selection in Recommender Systems

TL;DR

This paper explores leveraging Quantum Annealers and Counterfactual Analysis to enhance feature selection in recommender systems, demonstrating significant performance improvements over traditional methods through extensive experiments.

Contribution

It introduces a novel quantum computing approach combined with counterfactual analysis for feature selection in recommendation algorithms.

Findings

Quantum annealing effectively solves QUBO problems in recommendation systems.

Counterfactual analysis improves recommendation accuracy over mutual information.

Experimental results show significant performance gains with the proposed method.

Abstract

Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms. This feature selection problem is a Quadratic Unconstrained Binary Optimization(QUBO) problem. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm compared to using pure Mutual Information. Extensive experiments have demonstrated that the use of Counterfactual Analysis holds great promise for addressing such problems.