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Active learning to maximize accuracy vs. effort in interactive information retrieval

Aibo Tian, Matthew Lease
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it.  ...  Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user.  ...  Acknowledgments We would like to thank the anonymous reviewers for their valuable feedback. This work was partially supported by a John P. Commons Fellowship for the second author.  ... 
doi:10.1145/2009916.2009939 dblp:conf/sigir/TianL11 fatcat:roclqbwvbffohlp4sx6fplg5qi

Negative Relevance Feedback for Exploratory Search with Visual Interactive Intent Modeling

Jaakko Peltonen, Jonathan Strahl, Patrik Floréen
2017 Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI '17  
In difficult information seeking tasks, the majority of topranked documents for an initial query may be non-relevant, and negative relevance feedback may then help find relevant documents.  ...  negative feedback significantly improves the quality of retrieved information and user satisfaction for difficult tasks.  ...  ACKNOWLEDGMENTS Authors belong to Helsinki Insitute for Information Technology HIIT and the COIN centre of excellence.  ... 
doi:10.1145/3025171.3025222 dblp:conf/iui/PeltonenSF17 fatcat:2csoguq33jgyhcmazruj67abqa

Interactive Content Based Image Retrieval using Multiuser Feedback

M. Premkumar, R. Sowmya
2017 JOIV: International Journal on Informatics Visualization  
It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not.  ...  feedback techniques are activated to retrieve more relevant images.  ...  ACKNOWLEDGMENT We would like to thank Causal Productions for permits to use and revise the template provided by Causal Productions.  ... 
doi:10.30630/joiv.1.4.57 fatcat:2lymlkxdmzdbpid3s5i7l26ui4

Implicit Negative Feedback in Clinical Information Retrieval

Lorenz Kuhn, Carsten Eickhoff
2016 Swiss Medical Informatics  
For queries that make excessive use of negations, we were able to achieve up to 300% relative improvement in early precision.  ...  In this paper, we reflect on ways to improve medical information retrieval accuracy by drawing implicit negative feedback from negated information in noisy natural language search queries.  ...  [9] investigated different methods to improve retrieval accuracy for difficult search queries, using negative feedback.  ... 
doi:10.4414/smi.32.00355 fatcat:ndnrsy7mk5eu7fqma4nnrubrmi

Enhancing relevance feedback in image retrieval using unlabeled data

Zhi-Hua Zhou, Ke-Jia Chen, Hong-Bin Dai
2006 ACM Transactions on Information Systems  
In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images from user query and user feedback.  ...  Concretely, this paper integrates the merits of semi-supervised learning and active learning into the relevance feedback process.  ...  As for Svm Active , an Rbf kernel with γ = 1 is used. For each compared method, after obtaining a query, five rounds of relevance feedback are performed.  ... 
doi:10.1145/1148020.1148023 fatcat:mbxku4ydmje63cj7ci4aeg67wy

Exploiting Unlabeled Data in Content-Based Image Retrieval [chapter]

Zhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang
2004 Lecture Notes in Computer Science  
In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images from user query and user feedback.  ...  Images judged to be relevant with high confidence are returned as the retrieval result, while these judged with low confidence are put into the pool which is used in the next round of relevance feedback  ...  the display of the pooled images for relevance feedback.  ... 
doi:10.1007/978-3-540-30115-8_48 fatcat:wzkxowm2jneybm2kpz3w4fjuci

A Novel Architecture of Perception Oriented Web Search Engine Based on Decision Theory

Vinit Kumar, Niraj Singhal, Ashutosh Dixit, A. K. Sharma
2015 Indian Journal of Science and Technology  
The number of active web pages increases exponentially. According to the survey, the web has 14.3 trillion active web pages.  ...  The problem faced by present search engines is difficulty in returning relevant information.  ...  Then system re-computes itself to determine which pages are relevant or not based on user feedback. The Rocchio algorithm1 for relevance feedback is used for this purpose.  ... 
doi:10.17485/ijst/2015/v8i7/65156 fatcat:hkoxlnyjofa6dndakyk3celldi

Automatic relevance feedback for video retrieval

P. Muneesawang, L. Guan
2003 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)  
This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database.  ...  Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.  ...  INTRODUCTION Incorporating relevance feedback (RF) for improving retrieval accuracy is increasingly important for multimedia application [1] [2] [8] .  ... 
doi:10.1109/icme.2003.1221631 dblp:conf/icmcs/MuneesawangG03a fatcat:mdinntfbhvcxbpp275sf55awoq

Adaptive video indexing and automatic/semi-automatic relevance feedback

P. Munesawang, Ling Guan
2005 IEEE transactions on circuits and systems for video technology (Print)  
AVI takes into account spatio-temporal information for relevance feedback analysis of the dynamic content of video data.  ...  Experimentally, we demonstrated the proposed indexing technique and automatic relevance feedback for retrieval of CNN news videos.  ...  Since feedback samples are usually required for the adaptation of relevance feedback systems, it is difficult to apply many cycles of the relevance feedback to network-based video databases, considering  ... 
doi:10.1109/tcsvt.2005.852412 fatcat:c2ldxbacfzemzn63ufl6s7fqmu

PBIR-MM

Wei-Cheng Lai, Chengwei Chang, Edward Chang, Kwang-Ting Cheng, Michael Crandell
2002 Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02  
The system combines the strengths of content-based soft annotation (CBSA), multimodal relevance feedback through active learning, and perceptual distance formulation and indexing.  ...  We demonstrate PBIR-MM, an integrated system that we have built for conducting multimodal image retrieval.  ...  for the active learning algorithm to determine their membership to keyword W , and use those to solicit user feedback.  ... 
doi:10.1145/641091.641097 fatcat:2sp5pvyfxjhilhwcz5ca7r2wt4

PBIR-MM

Wei-Cheng Lai, Chengwei Chang, Edward Chang, Kwang-Ting Cheng, Michael Crandell
2002 Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02  
The system combines the strengths of content-based soft annotation (CBSA), multimodal relevance feedback through active learning, and perceptual distance formulation and indexing.  ...  We demonstrate PBIR-MM, an integrated system that we have built for conducting multimodal image retrieval.  ...  for the active learning algorithm to determine their membership to keyword W , and use those to solicit user feedback.  ... 
doi:10.1145/641007.641097 dblp:conf/mm/LaiCCCC02 fatcat:ondyncvqvvesnp2f3b25cji5xm

Adaptive multiple feedback strategies for interactive video search

Huanbo Luan, Yantao Zheng, Shi-Yong Neo, Yongdong Zhang, Shouxun Lin, Tat-Seng Chua
2008 Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08  
We first segregate interactive feedback into 3 distinct types (recall-driven relevance feedback, precision-driven active learning and locality-driven relevance feedback) so that a generic interaction mechanism  ...  In this paper, we propose adaptive multiple feedback strategies for interactive video retrieval.  ...  Thus, we propose to segregate the interactive feedback into three distinct types: a) Recall-driven Relevance Feedback (RRF); b) Precision-driven Active Learning (PAL) and c) Locality-driven Relevance Feedback  ... 
doi:10.1145/1386352.1386411 dblp:conf/civr/LuanZNZLC08 fatcat:vs45pml57nc7lk2aufxpywcbyu

Interactive information seeking via selective application of contextual knowledge

Gene Golovchinsky, Jeremy Pickens
2010 Proceeding of the third symposium on Information interaction in context - IIiX '10  
Exploratory search is a difficult activity that requires iterative interaction. This iterative process helps the searcher to understand and to refine the information need.  ...  We describe a framework for unifying transitions among various stages of exploratory search, and show how context from one stage can be applied to the next.  ...  These activities include the queries each user issued, the documents (and their scores and ranks) retrieved for each query, and relevance judgments.  ... 
doi:10.1145/1840784.1840806 dblp:conf/iiix/GolovchinskyP10 fatcat:egkhhhfu3fg4jbme57wlxvgidi

Relevance Feedback in Conceptual Image Retrieval: A User Evaluation [article]

Jose Torres, Luis Paulo Reis
2008 arXiv   pre-print
VOIR uses region-based relevance feedback to improve the quality of the results in each query session and to discover new associations between text and image.  ...  only at image level; and a third version not supporting relevance feedback at all.  ...  The two more popular approaches for relevance feedback presented below are classified in Ishikawa et al. (1998) as query-point movement and re-weighting.  ... 
arXiv:0809.4834v1 fatcat:f34fi2v6n5gp7ifuntv5pgetji

A bayesian logistic regression model for active relevance feedback

Zuobing Xu, Ram Akella
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance.  ...  To reduce the human evaluation effort in ascertaining relevance, we introduce a new active learning algorithm based on variance reduction to actively select documents for user evaluation.  ...  high for easy queries.  ... 
doi:10.1145/1390334.1390375 dblp:conf/sigir/XuA08 fatcat:qu6alsscqngozgqfocau7fvmuy
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