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Data Mining Methodology for Predicting Defection Process using Sequential Behavior Data
최준연 한국경영공학회 2009 한국경영공학회지 Vol.14 No.2
Predicting customers' defection, switching from one service provider to another, is an important issue in data mining research. This paper proposes an integrated methodology that identifies sequential patterns of defection through mining their behavioral data for predicting each user's defection. By means of the proposed methodology, we can find the sequential pattern of gradual defection and help prevent any current customer's defection. For this purpose, we suggest a 3-stage methodology that integrates moving average detection and sequential pattern mining. The methodology can identify crucial changes of behavior without a predefined time period. Therefore an active monitoring that gives an 'alarm' when a potential defector pattern appears can be implemented. To enhance performance of discovering rules, we propose an efficient algorithm which finds frequent defection patterns and a method to measure the strength of each rule. Evaluation of our method was conducted using the lift factor. The empirical results show that there is a high degree of predictive confidence.
A Personalized Approach for Recommending Useful Product Reviews Based on Information Gain
최준연,이홍주,박성주 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
Customer product reviews have become great influencers of purchase decision making. To assist potential customers, online stores provide various ways to sort customer reviews. Different methods have been developed to identify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most of the methods consider the preferences of all users to determine whether reviews are helpful, and all users receive the same recommendations. In this paper, we assessed methods for generating personalized recommendations based on information gain. The information gain approach was extended to consider each individual’s preference together with votes of other users. A total of 172 respondents rated 48 reviews selected from Amazon.com using a 7-point Likert scale. The performance of the devised methods was measured by varying the ratio of training sets and number of recommendations for the data collected. The personalized methods outperformed the existing information gain method, which takes into account the votes from all users. The greatest precision was achieved by the personalized method and a method employing selective use of predictions from the personalized method combined with the existing method based on all users’ reviews. However, the personalized method, which classified helpful reviews based on each user’s threshold value, showed statistically better performance.