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Web recommendations systems

Venugopal, K. R.

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Web recommendations systems

Record Type:	Electronic resources : 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.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xxi, 164 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Recommender systems (Information filtering) -
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. - Singapore :Springer Singapore :2020. - xxi, 164 p. :ill., digital ;24 cm.

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:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084 / .V45 2020

Dewey Class. No.: 005.56

Web recommendations systems
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245 1 0 $a Web recommendations systems $h [electronic resource] / $c by K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkar.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xxi, 164 p. : $b ill., digital ; $c 24 cm.
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 Recommender systems (Information filtering) $3 1002434
650 1 4 $a Web Development. $3 3134872
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Data Structures. $3 891009
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Srikantaiah, K. C. $3 3381682
700 1 $a Santosh Nimbhorkar, Sejal. $3 3448133
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-2513-1
950 $a Computer Science (Springer-11645)