  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.
