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

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

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.

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