eBay users form stable groups of common interest

Abstract

Market segmentation of an online auction site is studied by analyzing the users' bidding behavior. The distribution of user activity is investigated and a network of bidders connected by common interest in individual articles is constructed. The network's cluster structure corresponds to the main user groups according to common interest, exhibiting hierarchy and overlap. Key feature of the analysis is its independence of any similarity measure between the articles offered on eBay, as such a measure would only introduce bias in the analysis. Results are compared to null models based on random networks and clusters are validated and interpreted using the taxonomic classifications of eBay categories. We find clear-cut and coherent interest profiles for the bidders in each cluster. The interest profiles of bidder groups are compared to the classification of articles actually bought by these users during the time span 6-9 months after the initial grouping. The interest profiles discovered remain stable, indicating typical interest profiles in society. Our results show how network theory can be applied successfully to problems of market segmentation and sociological milieu studies with sparse, high dimensional data.

Major revision of the manuscript. Methodological improvements and inclusion of analysis of temporal development of user interests. 19 pages, 12 figures, 5 tables

References in corpus (15)

• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Modularity and community structure in networks
• Power laws, Pareto distributions and Zipf's law
• Functional cartography of complex metabolic networks
• Random graphs with arbitrary degree distributions and their applications
• Mixing patterns in networks
• Comparing community structure identification
• Statistical Mechanics of Community Detection
• Modularity from Fluctuations in Random Graphs and Complex Networks
• Problems with Fitting to the Power-Law Distribution
• Detecting fuzzy community structures in complex networks with a Potts model
• Properties of highly clustered networks
• Emerging behavior in electronic bidding
• Bidding process in online auctions and winning strategy:rate equation approach

Cited by in corpus (4)

• Community detection in graphs
• Role models for complex networks
• Equilibrium strategy and population-size effects in lowest unique bid auctions
• Membership in social networks and the application in information filtering