NDLI: Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries

Content Provider	IEEE Xplore Digital Library
Author	Neubauer, Nicolas Scheel, Christian Albayrak, Sahin Obermayer, Klaus
Copyright Year	2007
Abstract	Feedback on past queries is a valuable resource for improving retrieval performance on new queries. We introduce a modular approach to incorporating feedback information into given retrieval architectures. We propose to fusion the original ranking with those returned by rerankers, each of which trained on feedback given for a distinct, single query. Here, we examine the basic case of improving a query's original ranking qtest by only using one reranker: the one trained on feedback on the "closest" query qtrain. We examine the use of various distance measures between queries to first identify qtrain and then determine the best linear combination of the original and the reranker's ratings, that is: to find out which feedback to learn from, and how strongly to use it. We show the cosine distance between the term vectors of the two queries, each enriched by representations of the top N originally returned documents, to reliably answer both questions. The fusion performs equally well or better than a) always using only the original ranker or the reranker, b) selecting a hard distance threshold to decide between the two, or c) fusioning results with a ratio that is globally optimized, but fixed across all tested queries.
Starting Page	607
Ending Page	613
File Size	545112
Page Count	7
File Format	PDF
ISBN	9780769530260
DOI	10.1109/WI.2007.65
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Support vector machines Feedback Humans Information processing Extraterrestrial measurements Information retrieval Frequency estimation Neurofeedback Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries

Image retrieval using noisy query

Summarizing inter-query learning in content-based image retrieval via incremental semantic clustering

Performance evaluation of Euclidean/correlation-based relevance feedback algorithms in content-based image retrieval systems

Relevance feedback document retrieval using support vector machines

Incremental query evaluation for support vector machines

Comparison of Performance for SVM Based Relevance Feedback Document Retrieval in Several Vector Space Models

The use of unlabeled data in image retrieval with relevance feedback

Semisupervised Query Expansion with Minimal Feedback

Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries

Similar Documents

Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries

Image retrieval using noisy query

Summarizing inter-query learning in content-based image retrieval via incremental semantic clustering

Performance evaluation of Euclidean/correlation-based relevance feedback algorithms in content-based image retrieval systems

Relevance feedback document retrieval using support vector machines

Incremental query evaluation for support vector machines

Comparison of Performance for SVM Based Relevance Feedback Document Retrieval in Several Vector Space Models

The use of unlabeled data in image retrieval with relevance feedback

Semisupervised Query Expansion with Minimal Feedback

Distance Measures in Query Space: How Strongly to Use Feedback From Past Queries