eBay: an E-commerce Marketplace as a Complex Network

in Proceedings of the fourth ACM international conference on Web search and data mining (WSDM), 2011.
eBay: an E-commerce Marketplace as a Complex Network
Zeqian Shen, Neel Sundaresan, Zeqian Shen, Neel Sundaresan

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.

Another publication from the same category: Machine Learning and Data Science

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David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.