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Feature Selection for Recommender Systems with Quantum Computing.

Riccardo Nembrini¹, Maurizio Ferrari Dacrema¹, Paolo Cremonesi¹

¹ContentWise, Politecnico di Milano, Via Privata Simone Schiaffino, 11, 20158 Milano, Italy.

Entropy (Basel, Switzerland)

|August 27, 2021

Summary

This summary is machine-generated.

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Quantum computing, specifically quantum annealing, offers a new approach to feature selection for recommender systems. This hybrid algorithm effectively identifies key features using quantum optimization, demonstrating practical applications for this emerging technology.

Area of Science:

Quantum Computing
Recommender Systems
Optimization Problems

Background:

Quantum computing's potential has been largely theoretical due to hardware limitations.
Small, functional quantum computers are now accessible, enabling practical applications.
Quantum annealing is a paradigm suited for solving NP-hard optimization problems.

Purpose of the Study:

To design a hybrid feature selection algorithm for recommender systems.
To leverage user interaction data and domain knowledge.
To solve feature selection as an optimization problem on a quantum computer.

Main Methods:

Developed a hybrid feature selection algorithm for recommender systems.
Formulated feature selection as an optimization problem.

Keywords:

feature selection quantum annealing quantum computing recommender systems

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Utilized a D-Wave quantum computer for solving the optimization problem.

Main Results:

The proposed hybrid algorithm effectively selects a limited set of important features.
Demonstrated the practical applicability of quantum computers in applied science.
Showcased the potential of quantum annealing for recommender system optimization.

Conclusions:

Quantum computers are maturing for real-world scientific applications.
Hybrid quantum-classical approaches are viable for complex problems like feature selection.
Quantum annealing provides an accessible method to explore quantum computing's capabilities.