Feature Selection for Recommender Systems with Quantum Computing

Abstract

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.

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Cited by in corpus (5)

• Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
• An Advantage Using Feature Selection with a Quantum Annealer
• Analyzing the Effectiveness of Quantum Annealing with Meta-Learning
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications
• Minor Embedding for Quantum Annealing with Reinforcement Learning