Feature Selection for Recommender Systems with Quantum Computing

TL;DR

This paper explores a hybrid quantum computing approach to feature selection in recommender systems, demonstrating its effectiveness and potential for practical application as quantum technology advances.

Contribution

It introduces a novel quantum annealing-based method for feature selection in recommender systems, leveraging real quantum hardware for optimization.

Findings

Effective selection of important features demonstrated

Quantum approach shows promise for practical applications

Quantum hardware is sufficiently powerful for real-world problems

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.