CRUISE on Quantum Computing for Feature Selection in Recommender Systems

• URL: http://arxiv.org/abs/2407.02839v1
• Date: Wed, 3 Jul 2024 06:34:56 GMT
• Title: CRUISE on Quantum Computing for Feature Selection in Recommender Systems
• Authors: Jiayang Niu, Jie Li, Ke Deng, Yongli Ren,
• Abstract summary: We use Quantum Annealers to address the feature selection problem in recommendation algorithms. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm.
• Score: 9.703634723062127
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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