Information seeking is traditionally conducted in environments where search results are represented at the user interface by a minimal amount of meta-information such as titles an...
Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well kn...
We explore the use of eye movements as a source of implicit relevance feedback information. We construct a controlled information retrieval experiment where the relevance of each t...
Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new...
We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, ...
Although relevance feedback has been extensively studied in content-based image retrieval in the academic area, no commercial web image search engine has employed the idea. There ...
En Cheng, Feng Jing, Mingjing Li, Wei-Ying Ma, Hai...
We report on the effectiveness of language models for personalization of retrieval results based on a searcher’s preference for document genre. In principle, such preferences ca...
Gheorghe Muresan, Catherine L. Smith, Michael Cole...
Our goal in this study was to explore the potentials of extracting features from eye-tracking data that have the potential to improve performance in implicit relevance feedback. We...
Kirsten Kirkegaard Moe, Jeanette M. Jensen, Birger...