CRUISE on Quantum Computing for Feature Selection in Recommender Systems
<|reference_start|>CRUISE on Quantum Computing for Feature Selection in Recommender Systems: 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.<|reference_end|>
@article{niu2024cruise,
    title={CRUISE on Quantum Computing for Feature Selection in Recommender Systems},
    author={Jiayang Niu, Jie Li, Ke Deng and Yongli Ren},
    journal={arXiv preprint arXiv:2407.02839},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02839},
    primaryClass={cs.IR cs.AI}
}