Exploiting long‐term and short‐term preferences and RFID trajectories in shop recommendation

Summary Shop recommendation in large shopping malls is useful in the mobile internet era. With the maturity of indoor positioning technology, customers' indoor trajectories can be captured by radio frequency identification devices readers, which provides a new way to analyze customers' potential preferences. In this paper, we design three methods for the top-N shop recommendation problem. The first method is an improved matrix factorization method fusing estimated prior customer preference matrix that is constructed by Session-based Temporal Graph computing. The second method is a Bayesian personalized ranking method based on the first method. The third method is by tensor decomposition combined with Session-based Temporal Graph. Besides, we exploit customer history radio frequency identification devices trajectory information to find customers' frequent paths and revise predicted rating values to improve recommendation accuracy. Our methods are effective in modeling customers' temporal dynamics. At the same time, our approach considers repeated recommendation of the same shop by designing rating update rules. The test dataset is formed by JoyCity customer behavior records. JoyCity is a large-scale modern shopping center in downtown Shanghai, China. The results show that our approaches are effective and outperform previous state-of-the-art approaches. Copyright © 2016 John Wiley & Sons, Ltd.