# CRUISE on Quantum Computing for Feature Selection in Recommender Systems

arXiv: https://arxiv.org/abs/2407.02839 · HTML: https://mlsanity.com/paper/2407.02839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://mlsanity.com/paper/2109.05261/) (2021)
- [Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning](https://mlsanity.com/paper/2101.12446/) (2021)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [Quantum Computing: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2010.15559/) (2020)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers](https://mlsanity.com/paper/2205.04346/) (2022)
- [Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks](https://mlsanity.com/paper/2205.04490/) (2022)
- [GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering](https://mlsanity.com/paper/2204.04179/) (2022)

## Built on by (most cited first, 1 of 1)

- [Performance-Driven QUBO for Recommender Systems on Quantum Annealers](https://mlsanity.com/paper/2410.15272/) (2024)

## 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.