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 ecommerce 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.