User behavior in zero-recall ecommerce queries

SIGIR 2011: 75-84
User behavior in zero-recall ecommerce queries
Gyanit Singh, Nish Parikh, Neel Sundaresan

User expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documents. User expectation is more specific to find the right product.

The difference in the publisher and searcher vocabulary (in case of product search the seller and the buyer vocabulary) combined with the fact that there are fewer products to search over than web documents result in observable numbers of searches that return no results (zero recall searches).

In this paper we describe a study of zero recall searches. Our study is focused on eCommerce search and uses data from a leading eCommerce site's user click stream logs.

There are 3 main contributions of our study: 1) The cause of zero recall searches; 2) A study of user's reaction and recovery from zero recall; 3) A study of differences in behavior of power users versus novice users to zero recall searches.

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.