東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A learning approach for relevance an...

Hong, Dung T.

Linked to FindBook

Google Book

Amazon

博客來

A learning approach for relevance and diversity in federated search.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A learning approach for relevance and diversity in federated search./
Author:	Hong, Dung T.
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3636128
ISBN:	9781321175455

A learning approach for relevance and diversity in federated search.
Hong, Dung T.

A learning approach for relevance and diversity in federated search. - 146 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2014.

This item must not be sold to any third party vendors.

Federated search allows the simultaneous searching of multiple information sources. It is highly useful in the hidden web environment, which consists of documents that are difficult to obtain by traditional search engines such as Google or Bing. The fact that the hidden web is huge and contains valuable information has challenged federated search to look beyond the traditional searching model, in order to return a comprehensive list of documents using a limited number of information sources, while balancing document relevance and novelty.

ISBN: 9781321175455Subjects--Topical Terms:

626642
Computer Science.

A learning approach for relevance and diversity in federated search.
LDR:02722nmm a2200313 4500 001 2056388
005 20150526083647.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781321175455
035 $a (MiAaPQ)AAI3636128
035 $a AAI3636128
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hong, Dung T. $3 3170142
245 1 2 $a A learning approach for relevance and diversity in federated search.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Luo Si.
502 $a Thesis (Ph.D.)--Purdue University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Federated search allows the simultaneous searching of multiple information sources. It is highly useful in the hidden web environment, which consists of documents that are difficult to obtain by traditional search engines such as Google or Bing. The fact that the hidden web is huge and contains valuable information has challenged federated search to look beyond the traditional searching model, in order to return a comprehensive list of documents using a limited number of information sources, while balancing document relevance and novelty.
520 $a Existing research on federated search mostly focuses on document relevance using unsupervised and semi-supervised algorithms, with limited work on using machine learning approach for major problems such as resource selection and result merging. Furthermore, as modern information retrieval has gradually evolved to provide more utility in the final ranked list presented to users, the balance between relevance and coverage of different query aspects becomes more important. Yet, few learning models have been studied for relevance and diversity in federated search.
520 $a This thesis focuses on relevance and diversity in two major stages of federated search (resource selection and result merging) with a novel machine learning approach. New algorithms include a probabilistic joint model and query expansion that are more effective in selecting relevant information sources. We also propose a learning approach for better merging documents from individual sources, and tackle the problem of relevance-novelty balancing in resource selection by combining multiple diversification methods with some training queries. The effectiveness of the new research is demonstrated by an extensive set of experiments over multiple datasets.
590 $a School code: 0183.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Computer. $3 1669061
690 $a 0984
690 $a 0464
710 2 $a Purdue University. $b Computer Sciences. $3 1019069
773 0 $t Dissertation Abstracts International $g 76-01B(E).
790 $a 0183
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3636128