# Feature Selection for Recommender Systems with Quantum Computing

arXiv: https://arxiv.org/abs/2110.05089 · HTML: https://mlsanity.com/paper/2110.05089/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Quantum Recommendation Systems](https://mlsanity.com/paper/1603.08675/) (2016)
- [A quantum-inspired classical algorithm for recommendation systems](https://mlsanity.com/paper/1807.04271/) (2018)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Support vector machines on the D-Wave quantum annealer](https://mlsanity.com/paper/1906.06283/) (2019)
- [A practical heuristic for finding graph minors](https://mlsanity.com/paper/1406.2741/) (2014)
- [Nonnegative/binary matrix factorization with a D-Wave quantum annealer](https://mlsanity.com/paper/1704.01605/) (2017)
- [Item Listing Optimization for E-commerce Websites based on Diversity](https://mlsanity.com/paper/1903.12478/) (2019)
- [A novel graph-based model for hybrid recommendations in cold-start scenarios](https://mlsanity.com/paper/1808.10664/) (2018)
- [Dynamic Grover Search: Applications in Recommendation systems and Optimization problems](https://mlsanity.com/paper/1505.00895/) (2015)
- [Recommendation systems with quantum k-NN and Grover's algorithms for data processing](https://mlsanity.com/paper/1812.05095/) (2018)
- [Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation](https://mlsanity.com/paper/1904.11799/) (2019)

## Built on by (most cited first, 14 of 14)

- [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)
- [Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer](https://mlsanity.com/paper/2210.12953/) (2022)
- [An Advantage Using Feature Selection with a Quantum Annealer](https://mlsanity.com/paper/2211.09756/) (2022)
- [CRUISE on Quantum Computing for Feature Selection in Recommender Systems](https://mlsanity.com/paper/2407.02839/) (2024)
- [Analyzing the Effectiveness of Quantum Annealing with Meta-Learning](https://mlsanity.com/paper/2408.00570/) (2024)
- [Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering](https://mlsanity.com/paper/2507.15063/) (2025)
- [Minor Embedding for Quantum Annealing with Reinforcement Learning](https://mlsanity.com/paper/2507.16004/) (2025)
- [Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting](https://mlsanity.com/paper/2508.06455/) (2025)
- [Performance-Driven QUBO for Recommender Systems on Quantum Annealers](https://mlsanity.com/paper/2410.15272/) (2024)
- [Quantum Annealing Feature Selection on Light-weight Medical Image Datasets](https://mlsanity.com/paper/2502.19201/) (2025)
- [Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems](https://mlsanity.com/paper/2508.00027/) (2025)
- [Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction](https://mlsanity.com/paper/2603.04416/) (2026)
- [Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection](https://mlsanity.com/paper/2610.03161/) (2026)

## Abstract (arXiv)

The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular, quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.