alexa Multi Attribute User Profile Inference Model for Improved Customer Relation Management
E-ISSN: 2223-5833

Arabian Journal of Business and Management Review
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Research Article

Multi Attribute User Profile Inference Model for Improved Customer Relation Management

Karthikeyan KK1*, Ramani Gopal CS2 and Palaniappan G2

1Department of Management, Vinayaka Missions University, Salem, Tamil Nadu, India

2Faculty of Management Studies, Vinayaka Mission University, Salem, Tamil Nadu, India

*Corresponding Author:
Karthikeyan KK
Department of management
Vinayaka Missions University
Salem-636308, Tamil Nadu, India
Tel: +914273987000
Fax: +914272477903
E-mail: [email protected]

Received date: October 14, 2016; Accepted date: November 21, 2016; Published date: December 28, 2016

Citation: Karthikeyan KK, Ramani Gopal CS, Palaniappan G (2016) Multi Attribute User Profile Inference Model for Improved Customer Relation Management. Arabian J Bus Manag Review S3:011. 

Copyright: © 2016 Karthikeyan KK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



The market analysis has become more important where the organizations has the responsibility to maintain the relation with their customers. The marketing organizations have different customers from various part of the country. The customers have different type of interest and to maintain the bond with them, the organizations must find statistical solutions. There are number of approaches available for the detection of user interest but suffers to achieve the performance. To overcome the problem, a multi attribute user profile inference model has been proposed. The method identifies the user set who has purchased in earlier times and identifies the related items purchased by others. For each user from the user group, the method identifies their profile like finance, education, frequency of product purchase, and job profile. Using all this information the method identifies a subset of users to produce recommendations. Finally the method identifies the list of similar interested user groups and propagate the recommendations. The method improves the performance of customer relation management and improves the market growth.


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