Quantum-enhanced Representation Learning and Matching Learning for Recommendation

Quantum computing is an emerging research area. This paper investigates why and how quantum computing can be integrated into recommender systems. Although some existing recommendation methods explore quantum concepts, they either remain theoretical without empirical validation or provide limited insight into the use of quantum computing for designing core functions in recommendation. To fill these gaps, we first analyze the potential advantages of quantum computing for two key components (i.e., representation learning and matching learning) in recommender algorithms and formulate corresponding hypotheses. Then, based on our analysis and the quantum computing operations, we propose three quantum-enhanced recommendation paradigms. To show the extensibility of our paradigms, we further apply them to the graph-based and social recommendation scenarios. We conduct extensive experiments on the six real-world datasets, comparing our methods with various baselines. Experimental results not only validate our hypotheses but also show the strong performance of our proposed methods.