Application of the Information Bottleneck method to discover user profiles in a Web store. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Iwański, Jacek; Suchacka, Grażyna; Chodak, Grzegorz
• Is Part Of: Journal of organizational computing and electronic commerce. Volume 28:Issue 2(2018)
• Page Start: 98
• Page End: 121
• Publication Date: 2018-04-03
• Subjects: Agglomerative Information Bottleneck -- unsupervised classification -- clustering -- machine learning -- data mining -- e-commerce -- Web store -- user profile -- customer profile; Management -- Data processing -- Periodicals; Electronic commerce -- Periodicals; Electronic data interchange -- Periodicals; Business enterprises -- Computer networks -- Periodicals; 658.0505
• DOI: 10.1080/10919392.2018.1444340
• Languages: English
• ISSNs: 1091-9392

Abstract

ABSTRACT: The paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session samples showed that the method is capable of separating both types of sessions to a large extent. A detailed analysis was performed for the number of clusters ranging from two to seven, and the results were compared to those achieved by applying the most common clustering algorithm, k -means. Increasing the number of clusters generally leads to better results for both algorithms. However, IB demonstrated much higher average efficiency than k -means for the corresponding number of clusters, and this superiority was especially clear for lower number of clusters. The IB-based division of user sessions into seven clusters gives the mean entropy value of 0.28, which means the 95% separation of sessions of both types. Furthermore, a big advantage of our approach is that it gives a possibility to analyze the probability distribution of session attributes in individual clusters, which allows one to discover hidden knowledge about common characteristics of various user profiles and use this knowledge to support managerial decisions.