Referral Web: combining social networks and collaborative filtering
Communications of the ACM
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Science of Computer Programming
Empirical comparison of algorithms for network community detection
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Efficient parallel set-similarity joins using MapReduce
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eBay: an E-commerce marketplace as a complex network
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Proceedings of the 12th ACM conference on Electronic commerce
Outlier detection in graph streams
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Positive and Unlabeled Learning for Graph Classification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Learning from the past: answering new questions with past answers
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Inferring social roles and statuses in social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Reuse and remarketing of content and products is an integral part of the internet. As E-commerce has grown, online resale and secondary markets form a significant part of the commerce space. The intentions and methods for reselling are diverse. In this paper, we study an instance of such markets that affords interesting data at large scale for mining purposes to understand the properties and patterns of this online market.As part of knowledge discovery of such a market, we first formally propose criteria to reveal unseen resale behaviors by elastic matching identification (EMI) based on the account transfer and item similarity properties of transactions. Then, we present a large-scale system that leverages MapReduce paradigm to mine millions of online resale activities from petabyte scale heterogeneous e-commerce data. With the collected data, we show that the number of resale activities leads to a power law distribution with a 'long tail', where a significant share of users only resell in very low numbers and a large portion of resales come from a small number of highly active resellers. We further conduct a comprehensive empirical study from different aspects of resales, including the temporal, spatial patterns, user demographics, reputation and the content of sale postings. Based on these observations, we explore the features related to "successful" resale transactions and evaluate if they can be predictable. We also discuss uses of this information mining for business insights and user experience on a real-world online marketplace.