NDLI: Boosting the ranking function learning process using clustering

Please wait, while we are loading the content...

Event detection with common user interests

A comparison of techniques for estimating IDF values to generate lexical signatures for the web

From Web 1.0 to Web 2.0 and back -: how did your grandma use to tag?

Quantify music artist similarity based on style and mood

Automatic wrapper induction from hidden-web sources with domain knowledge

High-performance priority queues for parallel crawlers

Modeling the mashup space

Boosting the ranking function learning process using clustering

PIXSAR: incremental reclustering of augmented XML trees

Information retrieval from digital libraries in SQL

Nereau: a social approach to query expansion

Personalized ranking for digital libraries based on log analysis

A study of the relationship between ad hoc retrieval and expert finding in enterprise environment

HiPPIS: an online P2P system for efficient lookups on d-dimensional hierarchies

Web content summarization using social bookmarks: a new approach for social summarization

Online selection of parameters in the rocchio algorithm for identifying interesting news articles

The effect of title term suggestion on e-commerce sites

Approximating query completeness by predicting the number of answers in DHT-based web applications

Granular modeling of web documents: impact on information retrieval systems

Supporting the automatic construction of entity aware search engines

Boosting the ranking function learning process using clustering

Content Provider	ACM Digital Library
Author	Eirinaki, Magdalini Sellis, Timos Dalamagas, Theodore Giannopoulos, Giorgos
Abstract	As the Web continuously grows, the results returned by search engines are too many to review. Lately, the problem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present a method that overcomes this issue by exploiting all search results, both rated and unrated, in order to train a ranking function. Given a small initial set of user feedback for some search results, we first perform clustering on all results returned by the search. Based on the clusters created, we extend the initial set of rated results, including new, unrated results. Then, we use a popular training method (Ranking SVM) to train a ranking function using the expanded set of results. The experiments show that our method approximates sufficiently the results of an "ideal" system where all results of each query should be rated in order to be used as training data, something that is not feasible in a real-world scenario.
Starting Page	125
Ending Page	132
Page Count	8
File Format	PDF
ISBN	9781605582603
DOI	10.1145/1458502.1458523
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Training Ranking Search engine Clustering Clickthrough data Relevance judgement
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

Learning to rank user intent

Boosting the ranking function learning process using clustering.

Query Chains: Learning to Rank from Implicit Feedback (2005)

Optimizing Search Engines using Clickthrough Data (2002)

Ranking Function Adaptation with Boosting Trees (2011)

Evaluating Search Engines using Clickthrough Data (2002)

A regression framework for learning ranking functions using relative relevance judgments (2007)

Optimizing Search Engines using Clickthrough Data (2002)

Query chains: Learning to rank from implicit feedback (2005)

Boosting the ranking function learning process using clustering

Similar Documents

Learning to rank user intent

Boosting the ranking function learning process using clustering.

Query Chains: Learning to Rank from Implicit Feedback (2005)

Optimizing Search Engines using Clickthrough Data (2002)

Ranking Function Adaptation with Boosting Trees (2011)

Evaluating Search Engines using Clickthrough Data (2002)

A regression framework for learning ranking functions using relative relevance judgments (2007)

Optimizing Search Engines using Clickthrough Data (2002)

Query chains: Learning to rank from implicit feedback (2005)

Boosting the ranking function learning process using clustering