More precise: Stores recommendation under O2O commerce

Abstract

With the popularity of online shopping, Online to Offline (O2O) commerce is a newly business model between company and customers. Online to Offline (O2O) commerce means customers can order some services or products online, and then they go to the corresponding stores to take the services or products. How to recommend useful and precise advertisement for customers is very important under Online to Offline (O2O) commerce environment. If the recommendation work is down well, it will attract more customers to pay online and then get the related products in the real-world stores (offline). In this paper, we proposed a methodology based on the Back Propagation Neural Network algorithm to recommend some real-world stores information for travelers according to their current status such as location, time and budget. Basically, when travelers go to another unfamiliar city, they can download the store recommendations APP by their mobile devices to get the information which is needed by travelers. Furthermore, the results are more precise and close to travelers' desires and current status.