University of Bahrain
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More Precise: Stores Recommendation under O2O Commerce

Show simple item record Hsieh, Hui-Ching Chen, Yen-Chiu Lin, He-Chih 2018-07-08T07:41:17Z 2018-07-08T07:41:17Z 2014
dc.identifier.issn 2210-142X
dc.description.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 en_US
dc.language.iso en_US en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject online to offline en_US
dc.subject O2O en_US
dc.subject recommendation system en_US
dc.subject neural network en_US
dc.title More Precise: Stores Recommendation under O2O Commerce en_US
dc.type Article en_US
dc.volume 03
dc.issue 02
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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