QRU-1: A Public Dataset for Promoting Query Representation and Understanding Research by Hang Li, Gu Xu, W. Bruce Croft, Michael Bendersky, Ziqi Wang and Evelyne Viegas.
ABSTRACT
A new public dataset for promoting query representation and understanding research, referred to as QRU-1, was recently released by Microsoft Research. The QRU-1 dataset contains reformulations of Web TREC topics that are automatically generated using a large-scale proprietary web search log, without compromising user privacy. In this paper, we describe the content of this dataset and the process of its creation. We also discuss the potential uses of the dataset, including a detailed description of a query reformulation experiment.
And the data set:
Query Representation and Understanding Set
The Query Representation and Understanding (QRU) data set contains a set of similar queries that can be used in web research such as query transformation and relevance ranking. QRU contains similar queries that are related to existing benchmark data sets, such as TREC query sets. The QRU data set was created by extracting 100 TREC queries, training a query-generation model and a commercial search engine, generating similar queries from TREC queries with the model, and removal of mistakenly generated queries.
Are query reformulations in essence different identifications of the subject of a search?
But the issue isn’t “more” search results but rather higher quality search results.
Why search engines bother (other than bragging rights) to report “hits” beyond the ones displayed isn’t clear. Just have a “next N hits” button.
You could consider the number of “hits” you don’t look at as a measure of your search engine’s quality. The higher the number…., well, you know. Could be gold in those “hits” but you will never know. And your current search engine will never say.