CRUISE on Quantum Computing for Feature Selection in Recommender Systems
This work addresses feature selection in recommender systems, which is a domain-specific problem, but the approach is incremental as it builds on existing quantum annealing and counterfactual analysis methods.
The paper tackled the feature selection problem in recommender systems by using quantum annealers to solve it as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and incorporating counterfactual analysis significantly improved the performance of the item-based KNN recommendation algorithm compared to using pure mutual information.
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.