E-commerce Product Search: Personalization, Diversification, and beyond

Tutorial at WWW-2014
E-commerce Product Search: Personalization, Diversification, and beyond
Atish Das Sarma, Nish Parikh, Neel Sundaresan
Abstract

The focus of this tutorial will be e-commerce product search. Several research challenges appear in this context, both from a research standpoint as well as an application standpoint. We will present various approaches adopted in the industry,

review well-known research techniques developed over the last decade, draw parallels to traditional web search highlighting the new challenges in this setting, and dig deep into some of the algorithmic and technical approaches developed for this context.

A specific approach that will involve a deep dive into literature, theoretical techniques, and practical impact is that of identifying most suited results quickly from a large database, with settings various across cold start users, and those for whom personalization is possible.

In this context, top-k and skylines will be discussed specifically as they form a key approach that spans the web, data mining, and database communities and presents a powerful tool for search across multi-dimensional items with clear preferences within each attribute, like product search as opposed to regular web search.

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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