Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer

• URL: http://arxiv.org/abs/2210.12953v2
• Date: Wed, 8 Nov 2023 06:58:03 GMT
• Title: Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer
• Authors: Chen-Yu Liu, Hsin-Yu Wang, Pei-Yen Liao, Ching-Jui Lai, Min-Hsiu Hsieh
• Abstract summary: Factorization Machine (FM) is the most commonly used model to build a recommendation system. It requires a run-time of $O((N_m log N_m)2)$, where $N_m$ is the number of items in the dataset.
• Score: 9.199971808336006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 $O((N_m \log N_m)^2)$, where $N_m$ 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.

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