Performance-Driven QUBO for Recommender Systems on Quantum Annealers
This work addresses improving recommendation accuracy for users by integrating quantum computing with counterfactual analysis, though it appears incremental as it builds on existing quantum annealing methods.
The paper tackles feature selection in recommender systems by proposing CAQUBO, which uses counterfactual analysis to optimize feature combinations for quantum annealers, resulting in superior performance compared to state-of-the-art quantum annealing methods.
We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and feature combinations on model performance and employs the measurements to construct the coefficient matrix for a quantum annealer to select the optimal feature combinations for recommender systems, thereby improving their final recommendation performance. By establishing explicit connections between features and the recommendation performance, the proposed approach demonstrates superior performance compared to the state-of-the-art quantum annealing methods. Extensive experiments indicate that integrating quantum computing with counterfactual analysis holds great promise for addressing these challenges.