Prediction of Members' Return Visit Rates Using a Time Factor

doi:10.1016/j.elerap.2013.06.002

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/97093

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/97093

Title:	Prediction of Members' Return Visit Rates Using a Time Factor
Authors:	Chiang, Rui-Dong;Wang, Yi-Hsin;Chu, Huan-Chen
Contributors:	淡江大學資訊工程學系暨研究所
Keywords:	Behavioral targeting;Customer profile;Time function;Concept drift;Return visit rate prediction
Date:	2013-09-01
Issue Date:	2014-03-17 09:55:40 (UTC+8)
Publisher:	Amsterdam: Elsevier BV
Abstract:	The profit of portal companies in Taiwan is generated by online advertising and e-commerce. Effective advertising requires predicting how users respond to advertisements and then targeting (presenting advertisements) the users in ways that reflect their preferences. The behavioral targeting leverages historical users’ behaviors in order to select for display the ads which are most related to the users’ preferences. Although we may not want to provide advertisements that repeat the same concept, we can exploit predicted return visit rates for the registered members at the specific category of a portal site. However, customers’ preferences change over time. In order to capture the ‘concept drift’, we propose a novel and simple time function to increase/decrease the weight of the old data in evaluating various members’ past behaviors. Then, we construct a member’s Click Preference Index with a Time factor (CPIT) model in order to effectively distinguish the different kinds of member behaviors and predict return visit rates. The marketers of a portal site can thus target the members with high return visit rates and design corresponding marketing strategies. Experimental results with a real dataset have demonstrated that this CPIT model can be practically implemented and provide satisfactory results.
Relation:	Electronic Commerce Research and Applications 12(5), pp.362–371
DOI:	10.1016/j.elerap.2013.06.002
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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