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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
8 years 5 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
CIKM
2011
Springer
9 years 2 months ago
A probabilistic method for inferring preferences from clicks
Evaluating rankers using implicit feedback, such as clicks on documents in a result list, is an increasingly popular alternative to traditional evaluation methods based on explici...
Katja Hofmann, Shimon Whiteson, Maarten de Rijke
IRFC
2011
Springer
9 years 6 months ago
Combining Interaction and Content for Feedback-Based Ranking
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...
Emanuele Di Buccio, Massimo Melucci, Dawei Song
ECIR
2011
Springer
9 years 6 months ago
Balancing Exploration and Exploitation in Learning to Rank Online
Abstract. As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches...
Katja Hofmann, Shimon Whiteson, Maarten de Rijke
SIGIR
2008
ACM
10 years 2 months ago
Query expansion using gaze-based feedback on the subdocument level
We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document part...
Georg Buscher, Andreas Dengel, Ludger van Elst
SIGIR
2010
ACM
10 years 6 months ago
Comparing click-through data to purchase decisions for retrieval evaluation
Traditional retrieval evaluation uses explicit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data...
Katja Hofmann, Bouke Huurnink, Marc Bron, Maarten ...
SIGIR
2010
ACM
10 years 6 months ago
Learning more powerful test statistics for click-based retrieval evaluation
Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preferen...
Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, T...
CIVR
2010
Springer
276views Image Analysis» more  CIVR 2010»
10 years 7 months ago
Optimizing visual search with implicit user feedback in interactive video retrieval
This paper describes an approach to optimize query by visual example results, by combining visual features and implicit user feedback in interactive video retrieval. To this end, ...
Stefanos Vrochidis, Ioannis Kompatsiaris, Ioannis ...
SIGIR
2004
ACM
10 years 8 months ago
Display time as implicit feedback: understanding task effects
Recent research has had some success using the length of time a user displays a document in their web browser as implicit feedback for document preference. However, most studies h...
Diane Kelly, Nicholas J. Belkin
SIGIR
2004
ACM
10 years 8 months ago
Eye-tracking analysis of user behavior in WWW search
We investigate how users interact with the results page of a WWW search engine using eye-tracking. The goal is to gain into how users browse the presented abstracts and how they s...
Laura A. Granka, Thorsten Joachims, Geri Gay
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