In this work, we tackle the feature selection problem for content-based recommender systems using Quadratic Unconstrained Binary Optimization (QUBO). Our approach, submitted as Team MALTO to the QuantumCLEF 2025 challenge, aims to improve the performance of an Item-Based K-Nearest Neighbors (Item-KNN) model by selecting a compact and informative subset of item features. We formulate a QUBO objective that combines feature relevance – estimated via Random Forest (RF) importance scores – and feature redundancy – captured through pairwise Pearson correlations. We compare our method against a collaborative-driven QUBO baseline and a random selection strategy. Experiments on the official QuantumCLEF dataset demonstrate that our relevanceaware strategy outperforms the other methods regarding recommendation quality, especially in low-dimensional feature regimes. Our results highlight the potential of combining machine learning and quantum optimization for effective feature selection in recommender systems
In this work, we tackle the feature selection problem for content-based recommender systems using Quadratic Unconstrained Binary Optimization (QUBO). Our approach, submitted as Team MALTO to the QuantumCLEF 2025 challenge, aims to improve the performance of an Item-Based K-Nearest Neighbors (Item-KNN) model by selecting a compact and informative subset of item features. We formulate a QUBO objective that combines feature relevance – estimated via Random Forest (RF) importance scores – and feature redundancy – captured through pairwise Pearson correlations. We compare our method against a collaborative-driven QUBO baseline and a random selection strategy. Experiments on the official QuantumCLEF dataset demonstrate that our relevanceaware strategy outperforms the other methods regarding recommendation quality, especially in low-dimensional feature regimes. Our results highlight the potential of combining machine learning and quantum optimization for effective feature selection in recommender systems
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 ( P erformance- D riven Q uadratic U nconstrained B inary O ptimization), 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.
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.
Feature selection, the task of identifying the most informative subset of attributes from a large feature pool, is an NP-hard problem central to Information Retrieval (IR) and Recommender Systems (RS). This paper presents Team SYZYGY’s submission to Task 1 of the QuantumCLEF 2026 Lab, formulating feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem solved on a D-Wave quantum annealer. For Task 1A (IR), we introduce five information-theoretic QUBO formulations that organically balance feature relevance and redundancy without hard cardinality constraints, combining mutual information, conditional mutual information, interaction information, and two normalized weighted mutual information variants. On the MQ2007 benchmark, our best configurations reduce the feature count from 46 to just 21 to 24 while surpassing both the full feature and recursive feature elimination baselines. For Task 1B (RS), a simplified collaborative driven QUBO aligns content based similarity with real user behavior, with the simulated annealer successfully improving over the full feature baseline while the physical quantum annealer is hampered by embedding complications. Across both tasks, quantum hardware completes sampling an order of magnitude faster than simulated annealing, though run to run inconsistencies on the physical QPU remain a key open challenge.
One of the biggest obstacles to creating precise machine learning models is choosing representative and pertinent characteristics from big, unbalanced datasets. While too many features raise the risk of overfitting and computational expense, class imbalance frequently results in decreased accuracy and bias. The Simulated Annealing technique is used in this study to tackle a Quadratic Unconstrained Binary Optimization (QUBO) problem that is formulated as a quantum-assisted feature selection method to handle these problems. The technique seeks to reduce inter-feature redundancy and the number of selected features. There are 102,487 samples in the majority class and 11,239 in the minority class, totaling 28 characteristics in the experimental dataset. Nine ideal features were found during the feature selection method (12, 14, 15, 22, 23, 24, 25, 27, and 28). Ten-fold cross-validation was used to assess a Random Forest Classifier that was trained using an 80:20 split. With precision, recall, f1-score, and accuracy all hitting 1.00, the suggested QUBO+SMOTE method demonstrated exceptional performance. Comparatively, QUBO without SMOTE performed worse with accuracy 0.95 and minority-class f1-score of only 0.71, whereas a traditional Recursive Feature Elimination (RFE) approach obtained accuracy 0.97 with minority-class f1-score of 0.94. These findings indicate that QUBO can reduce dimensionality and address class imbalance which requires its integration with SMOTE. This study demonstrates how quantum computing can enhance the effectiveness and efficiency of machine learning, especially for large-scale imbalanced datasets
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the content information may be abundant, but also noisy and expensive to acquire. Thus, selection of particular features that improve cold-start recommendations becomes an important and non-trivial task. In the recent approach by Nembrini et al., the feature selection is driven by the correlational compatibility between collaborative and content-based models. The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave. Inspired by the reported results, we contend the idea that current quantum annealers are superior for this problem and instead focus on classical algorithms. In particular, we tackle QUBO via TTOpt, a recently proposed black-box optimizer based on tensor networks and multilinear algebra. We show the computational feasibility of this method for large problems with thousands of features, and empirically demonstrate that the solutions found are comparable to the ones obtained with D-Wave across all examined datasets.
As the volume of data for classification tasks in machine learning grows, feature selection plays an increasingly crucial role in enhancing the efficiency and effectiveness of these tasks. Existing classical feature selection algorithms often encounter challenges, such as high computational complexity and vulnerability to local optima. Quantum algorithms have been proposed to overcome these limitations. Here, our work proposes a new approach that combines the Quantum Approximate Optimization Algorithm (QAOA) with the classical feature selection algorithm Maximum Relevance-Minimum Redundancy (MRMR). First, we transform the feature selection problem into the Quadratic Unconstrained Binary Optimization (QUBO) problem. Then, we map the QUBO formulation to Hamiltonian with the Ising model. Finally, we employ QAOA with a shallow circuit to search this Hamiltonian's ground state, which matches the specific solution for feature selection. We conducted numerical experiments on eight real-world datasets for classification tasks from diverse domains, including finance, medicine, and natural hazards. Experiment results suggest that QAOA-selected feature subsets size are typically less than half the complete set while achieving comparable or better classification accuracy than classical MRMR, demonstrating its preference for smaller, more efficient feature subsets.