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Online Buying in Virtual Cosmetic Marketplace Using Recommender System

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dc.contributor.author Khine, Win Lei Kay
dc.contributor.author Khine, Thandar
dc.date.accessioned 2019-08-05T13:09:49Z
dc.date.available 2019-08-05T13:09:49Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1788
dc.description.abstract Electronic Commerce (E-commerce) has become one of the essential characteristics in the Internet era. Nowadays, E-commerce is growing rapidly in the Internet, and has become a complement to the usual business activities of corporations and individuals. Recommender systems have evolved in the last years as specialized tools to assist users in a plethora of computer-mediated tasks by providing guidelines or hints. Most recommender systems are aimed at helping users to deal with the problem of information overload by facilitating access to relevant items. In this system, shopping agents like buying agents, selling agents, and mall agents, and then knowledge-based recommender system are used because the World Wide Web information grows explosively in the Internet and people encounter problem to pick some correct things from the set of choice in E-Commerce. All the activities are done in Virtual Marketplace. Virtual Marketplace is a type of E-Marketplace system. In this system, recommender systems help users choose something they actually want or need and limit their search by supplying a list of items. Therefore, the recommender systems get vital role in the Internet. The objective of the system is to give effective products for user when they want to search cosmetic as to which type of cosmetic is suitable for them. Knowledge-based recommender system and implementation with a multi-agent system framework for marketplace system are used in this system, in which buying agent, selling agent and mall agent are presented. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Online Buying in Virtual Cosmetic Marketplace Using Recommender System en_US
dc.type Article en_US

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