Building a Network of E-commerce Concepts

Lightning Talk and Poster @ Extremely Large Databases XLDB 2013.
Building a Network of E-commerce Concepts
Sandip Gaikwad, Sanjay Ghatare, Nish Parikh, Rajendra Shinde
Abstract

We present a method for developing a network of e-commerce concepts. We define concepts as collection of terms that represent product entities or commerce ideas that users are interested in. We start by looking at large corpora (Billions) of historical eBay buyer queries and seller item titles.

We approach the problem of concept extraction from corpora as a market-baskets problem by adapting statistical measures of support and confidence. The concept-centric meta-data extraction pipeline is built over a map-reduce framework. We constrain the concepts to be both popular and concise.

Evaluation of our algorithm shows that high precision concept sets can be automatically mined. The system mines the full spectrum of precise e-commerce concepts ranging all the way from "ipod nano" to "I'm not a plastic bag" and from "wakizashi sword" to "mastodon skeleton".

Once the concepts are detected, they are linked into a network using different metrics of semantic similarity between concepts. This leads to a rich network of e-commerce vocabulary. Such a network of concepts can be the basis of enabling powerful applications like e-commerce search and discover as well as automatic e-commerce taxonomy generation. We present details about the extraction platform, and algorithms for segmentation of short snippets of e-commerce text as well as detection and linking of concepts.

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.

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