國立虎尾科技大學 |

Web Recommendations Systems

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Web Recommendations Systems/ by K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkar.
Author:	Venugopal, K. R.
other author:	Srikantaiah, K. C.
Description:	XXI, 164 p. 43 illus., 4 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer programming. -
Online resource:	https://doi.org/10.1007/978-981-15-2513-1
ISBN:	9789811525131

Web Recommendations Systems
Venugopal, K. R.

Web Recommendations Systems[electronic resource] /by K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkar. - 1st ed. 2020. - XXI, 164 p. 43 illus., 4 illus. in color.online resource.

1 Introduction -- 2 Web Data Extraction and Integration System for Search Engine Result Pages -- 3 Mining and Analysis of Web Sequential Patterns -- 4 Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web -- 5 Construction of Topic Directories using Levenshtein Similarity Weight -- 6 Related Search Recommendation with User Feedback Session -- 7 Webpage Recommendations based Web Navigation Prediction. .

This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems.

ISBN: 9789811525131

Standard No.: 10.1007/978-981-15-2513-1doiSubjects--Topical Terms:

527822
Computer programming.

LC Class. No.: QA76.6-76.66

Dewey Class. No.: 006.76

Web Recommendations Systems
LDR:02616nam a22003975i 4500 001 1019527
003 DE-He213
005 20200702132403.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811525131 $9 978-981-15-2513-1
024 7 $a 10.1007/978-981-15-2513-1 $2 doi
035 $a 978-981-15-2513-1
050 4 $a QA76.6-76.66
072 7 $a UMW $2 bicssc
072 7 $a COM060160 $2 bisacsh
072 7 $a UMW $2 thema
082 0 4 $a 006.76 $2 23
100 1 $a Venugopal, K. R. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 786354
245 1 0 $a Web Recommendations Systems $h [electronic resource] / $c by K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkar.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XXI, 164 p. 43 illus., 4 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 1 Introduction -- 2 Web Data Extraction and Integration System for Search Engine Result Pages -- 3 Mining and Analysis of Web Sequential Patterns -- 4 Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web -- 5 Construction of Topic Directories using Levenshtein Similarity Weight -- 6 Related Search Recommendation with User Feedback Session -- 7 Webpage Recommendations based Web Navigation Prediction. .
520 $a This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems.
650 0 $a Computer programming. $3 527822
650 0 $a Optical data processing. $3 639187
650 0 $a Data structures (Computer science). $3 680370
650 0 $a Data mining. $3 528622
650 1 4 $a Web Development. $3 1114136
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Data Structures. $3 669824
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Srikantaiah, K. C. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1314819
700 1 $a Santosh Nimbhorkar, Sejal. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1314820
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811525124
776 0 8 $i Printed edition: $z 9789811525148
776 0 8 $i Printed edition: $z 9789811525155
856 4 0 $u https://doi.org/10.1007/978-981-15-2513-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)