Zeqian Shen

Visual cluster exploration of web clickstream data

Web clickstream data are routinely collected to study how users browse the web or use a service. It is clear that the ability to recognize and summarize user behavior patterns from such data is valuable to e-commerce companies. In this paper, we introduce a visual analytics system to explore the various user behavior patterns reflected by distinct clickstream clusters. In a practical analysis scenario, the system first presents an overview of clickstream clusters using a Self-Organizing Map with Markov chain models. Then the analyst can interactively explore the clusters through an intuitive user interface. He can either obtain summarization of a selected group of data or further refine the clustering result. We evaluated our system using two different datasets from eBay. Analysts who were working on the same data have confirmed the systems effectiveness in extracting user behavior patterns from complex datasets and enhancing their ability to reason.

eBay: an E-commerce marketplace as a complex network

Commerce networks involve buying and selling activities among individuals or organizations. As the growing of the Internet and e-commerce, it brings opportunities for obtaining real world online commerce networks, which are magnitude larger than before. Getting a deeper understanding of e-commerce networks, such as the eBay marketplace, in terms of what structure they have, what kind of interactions they afford, what trust and reputation measures exist, and how they evolve has tremendous value in suggesting business opportunities and building effective user applications. In this paper, we modeled the eBay network as a complex network. We analyzed the macroscopic shape of the network using degree distribution and the bow-tie model. Networks of different eBay categories are also compared. The results suggest that the categories vary from collector networks to retail networks. We also studied the local structures of the networks using motif profiling. Finally, patterns of preferential connections are visually analyzed using Auroral diagrams.

Reputation and Regulations: Evidence from eBay

To mitigate inefficiencies arising from asymmetric information, some markets rely on government interventions, whereas others rely on reputation systems, warranties, or guarantees. This paper explores the impact of two mechanisms, namely, reputation badges and buyer protection programs, and their interaction on eBay’s marketplace. Adding buyer protection reduces the premium for the reputation badge and increases efficiency in the marketplace. These efficiency gains are achieved by reducing moral hazard through an increase in sellers´ quality and by reducing adverse selection through a higher exit rate for low-quality sellers. Our estimates suggest buyer protection increases the total welfare by 2.9%. This paper was accepted by Matt Shum, marketing .

Modeling Seller Listing Strategies

Online markets have enjoyed explosive growths and emerged as an important research topic in the field of electronic commerce. Researchers have mostly focused on studying consumer behavior and experience, while largely neglecting the seller side of these markets. Our research addresses the problem of examining strategies sellers employ in listing their products on online marketplaces. In particular, we introduce a Markov Chain model that captures and predicts seller listing behavior based on their present and past actions, their relative positions in the market, and market conditions. These features distinguish our approach from existing models that usually overlook the importance of historical information, as well as sellers’ interactions. We choose to examine successful sellers on eBay, one of the most prominent online marketplaces, and empirically test our model framework using eBay’s data for fixed-priced items collected over a period of four and a half months. This empirical study entails comparing our most complex history-dependent model’s predictive power against that of a semi-random behavior baseline model and our own history-independent model. The outcomes exhibit differences between different sellers in their listing strategies for different products, and validate our models’ capability in capturing seller behavior. Furthermore, the incorporation of historical information on seller actions in our model proves to improve its predictions of future behavior.

RepRank: reputation in a peer-to-peer online system

Peer-to-peer e-commerce networks exemplify online lemon markets. Trust is key to sustaining these networks. We present a reputation system named RepRank that approaches trust with an intuition that in the peer-to-peer e-commerce world consisting of buyers and sellers, good buyers are those who buy from good sellers, and good sellers are those from whom good buyers buy. We propagate trust and distrust in a network using this mutually recursive definition. We discuss the algorithms and present the evaluation results.