Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance. However, producing item suggestions for a given user with a trained FM is time-consuming. It requires a run-time of O((NmlogNm)2), where Nm is the number of items in the dataset. To address this problem, we propose a quadratic unconstrained binary optimization (QUBO) scheme to combine with FM and apply quantum annealing (QA) computation. Compared to classical methods, this hybrid algorithm provides a faster than quadratic speedup in finding good user suggestions. We then demonstrate the aforementioned computational advantage on current NISQ hardware by experimenting with a real example on a D-Wave annealer.
@article{arxiv.2210.12953,
title = {Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer},
author = {Chen-Yu Liu and Hsin-Yu Wang and Pei-Yen Liao and Ching-Jui Lai and Min-Hsiu Hsieh},
journal= {arXiv preprint arXiv:2210.12953},
year = {2023}
}