Karthikeyan KK, Ramani Gopal CS and Palaniappan G
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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