CRUISE on Quantum Computing for Feature Selection in Recommender Systems
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913415904100352 |
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| author | Niu, Jiayang Li, Jie Deng, Ke Ren, Yongli |
| author_facet | Niu, Jiayang Li, Jie Deng, Ke Ren, Yongli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CRUISE on Quantum Computing for Feature Selection in Recommender Systems Niu, Jiayang Li, Jie Deng, Ke Ren, Yongli Information Retrieval Artificial Intelligence 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. |
| title | CRUISE on Quantum Computing for Feature Selection in Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2407.02839 |