Application of the Information Bottleneck method to discover user profiles in a Web store
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 sampl…
Cost-Oriented Recommendation Model for E-Commerce
Contemporary Web stores offer a wide range of products to e-customers. However, online sales are strongly dominated by a limited number of bestsellers whereas other, less popular or niche products are stored in inventory for a long time. Thus, they contribute to the problem of frozen capital and high inventory costs. To cope with this problem, we propose using information on product cost in a recommender system for a Web store. We discuss the proposed recommendation model, in which two criteria have been included: a predicted degree of meeting customer’s needs by a product and the product cost.
Practical Aspects of Log File Analysis for E-Commerce
The paper concerns Web server log file analysis to discover knowledge useful for online retailers. Data for one month of the online bookstore operation was analyzed with respect to the probability of making a purchase by e-customers. Key states and characteristics of user sessions were distinguished and their relations to the session state connected with purchase confirmation were analyzed. Results allow identification of factors increasing the probability of making a purchase in a given Web store and thus, determination of user sessions which are more valuable in terms of e-business profitability. Such results may be then applied in practice, e.g. in a method for personalized or prioritize…
HTTP-level e-commerce data based on server access logs for an online store
Abstract Web server logs have been extensively used as a source of data on the characteristics of Web traffic and users’ navigational patterns. In particular, Web bot detection and online purchase prediction using methods from artificial intelligence (AI) are currently key areas of research. However, in reality, it is hard to obtain logs from actual online stores and there is no common dataset that can be used across different studies. Moreover, there is a lack of studies exploring Web traffic over a longer period of time, due to the unavailability of long-term data from server logs. The need to develop reliable models of Web traffic, Web user navigation, and e-customer behaviour calls for …
An Experiment with Facebook as an Advertising Channel for Books and Audiobooks
The paper addresses the problem of using social media to promote innovative products available in online stores. Motivated by the fast development of the audiobook market, on the one hand, and the efficiency of social media marketing, on the other hand, we conducted an experiment with a marketing campaign of books and audiobooks on the most popular social networking site, Facebook, and discussed it in the paper. The goal of the experiment was exploring possible differences in FB users’ reaction to FB advertisements of traditional books and audiobooks. The experiment was implemented by using a real Facebook fanpage of a Polish publishing house having its own online bookstore. Results show so…
Using association rules to assess purchase probability in online stores
The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formu…
Characterizing Web sessions of e-customers interested in traditional and innovative products
Web traffic characterization and modelling is currently a hot research issue. Low-level analysis of HTTP traffic on the server allows one to build adequate traffic models to be used in server benchmarking. High-level analysis of Web user behavior allows one to optimize website structure and develop personalized service strategies. In this paper, analysis of customer sessions in an online store is performed using Web server log data. The goal is to explore possible differences between sessions of customers viewing and purchasing innovative products, and customers only interested in traditional products.