We propose a novel mode of feedback for image search, where a user describes which properties of exemplar images should be adjusted in order to more closely match his/her mental m...
In crowdsourced relevance judging, each crowd worker typically judges only a small number of examples, yielding a sparse and imbalanced set of judgments in which relatively few wo...
We present TOURVIZ, a system that helps its users to interactively visualize and make sense in large network datasets. In particular, it takes as input a set of nodes the user spe...
Duen Horng Chau, Leman Akoglu, Jilles Vreeken, Han...
Abstract. In the last years, cluster based retrieval has been demonstrated as an effective tool for both interactive retrieval and pseudo relevance feedback techniques. In this pa...
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hu...
Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback...
Using relevance feedback can significantly improve (ad hoc) retrieval effectiveness. Yet, if little feedback is available, effectively exploiting it is a challenge. To that end,...
We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
The paper is concerned with the design and the evaluation of the combination of user interaction and informative content features for implicit and pseudo feedback-based document re...
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...