CRUISE on Quantum Computing for Feature Selection in Recommender Systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 3 Jul 2024.
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16 of 32 references.
References · 16
- 2017Attention Is All You Needcited by 59,932
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
- 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
- 2019Neural Graph Collaborative Filteringcited by 660
- 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
- 2021Counterfactual Explainable Recommendationcited by 60
- 2021CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendationcited by 46
- 2021Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learningcited by 38
- 2021Counterfactual Explanations for Neural Recommenderscited by 27
- 2020Quantum Computing: A Taxonomy, Systematic Review and Future Directionscited by 16
- 2021A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectscited by 13
4 more
- 2021Feature Selection for Recommender Systems with Quantum Computingcited by 13
- 2022Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealerscited by 11
- 2022Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networkscited by 4
- 2022GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filteringcited by 3
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Citing papers per year: 2024: 1.
1 paper.