# Performance-Driven QUBO for Recommender Systems on Quantum Annealers

arXiv: https://arxiv.org/abs/2410.15272 · HTML: https://mlsanity.com/paper/2410.15272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 84 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09433/) (2019)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2202.08816/) (2022)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [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)
- [A Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2207.12515/) (2022)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation](https://mlsanity.com/paper/2202.06466/) (2022)
- [Reinforced Path Reasoning for Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2207.06674/) (2022)
- [Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers](https://mlsanity.com/paper/2205.04346/) (2022)
- [Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions](https://mlsanity.com/paper/1501.07242/) (2015)
- [Counterfactual Explanation for Fairness in Recommendation](https://mlsanity.com/paper/2307.04386/) (2023)
- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [Path-Specific Counterfactual Fairness for Recommender Systems](https://mlsanity.com/paper/2306.02615/) (2023)
- [SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems](https://mlsanity.com/paper/2308.09395/) (2023)
- [LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction](https://mlsanity.com/paper/2206.00267/) (2022)
- [Supervised Feature Compression based on Counterfactual Analysis](https://mlsanity.com/paper/2211.09894/) (2022)
- [CRUISE on Quantum Computing for Feature Selection in Recommender Systems](https://mlsanity.com/paper/2407.02839/) (2024)

## Abstract (arXiv)

Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadratic Unconstrained Binary Optimization (QUBO). In this work, we propose PDQUBO (Performance-Driven Quadratic Unconstrained Binary Optimization), a QUBO-based feature selection method that is directly executable on quantum annealers. Unlike prior QUBO-based feature selection approaches on quantum annealers, PDQUBO explicitly quantifies the performance impact of both individual features and feature pairs on recommender system models. This alignment between QUBO optimization objectives and model performance ensures that the solution direction is closely tied to recommendation quality, making it well-suited for practical deployment on quantum hardware. Moreover, by leveraging counterfactual analysis, PDQUBO is model-agnostic and evaluation-metric-independent, making it broadly applicable across diverse recommender architectures and assessment criteria. In addition, we investigate the instability of quantum annealing on real quantum devices with respect to varying problem sizes and problem difficulties. Extensive experiments on real-world datasets demonstrate that PDQUBO consistently outperforms prior QUBO-based feature selection methods on quantum annealers. Furthermore, we compare PDQUBO against classical feature selection baselines on click-through rate (CTR) prediction tasks, showing its strong performance and highlighting the potential of using quantum annealers for real-world feature selection applications. Our findings suggest that integrating quantum optimization with counterfactual analysis provides a promising direction for effective feature selection in recommender systems.