Abstract
After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.
Original language | English |
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Title of host publication | RecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems |
Publisher | Association for Computing Machinery ACM |
Pages | 579-585 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-4503-9278-5 |
DOIs | |
Publication status | Published - 13 Sept 2022 |
MoE publication type | A4 Article in a conference publication |
Event | 16th ACM Conference on Recommender Systems, RecSys '22 - Seattle, United States Duration: 18 Sept 2022 → 23 Sept 2022 |
Conference
Conference | 16th ACM Conference on Recommender Systems, RecSys '22 |
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Country/Territory | United States |
City | Seattle |
Period | 18/09/22 → 23/09/22 |
Keywords
- Community Detection
- Quantum Annealing
- Quantum Computing
- Recommender Systems