In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from se...
Ryen W. White, Joemon M. Jose, C. J. van Rijsberge...
This paper extends previous work on document retrieval and document type classification, addressing the problem of ‘typed search’. Specifically, given a query and a designated ...
Jun Xu, Yunbo Cao, Hang Li, Nick Craswell, Yalou H...
We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model t...
Dustin Hillard, Stefan Schroedl, Eren Manavoglu, H...
In this project (VIRSI) we investigate the promising contentbased retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of syn...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
Modeling the beyond-topical aspects of relevance are currently gaining popularity in IR evaluation. For example, the discounted cumulated gain (DCG) measure implicitly models some...