Anatomy of a Web-Scale Resale Market: A Data Mining Approach

in Proceedings of the 22nd international conference on World Wide Web (WWW ’13)
Anatomy of a Web-Scale Resale Market: A Data Mining Approach
Yuchen Zhao, Neel Sundaresan, Zeqian Shen, Philip Yu
Abstract

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.

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

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.

Keywords