Web Recommendations Systems
Autor K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkaren Limba Engleză Paperback – 3 mar 2021
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.
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Specificații
ISBN-13: 9789811525155
ISBN-10: 9811525153
Pagini: 164
Ilustrații: XXI, 164 p. 43 illus., 4 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811525153
Pagini: 164
Ilustrații: XXI, 164 p. 43 illus., 4 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
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.
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.
Notă biografică
Dr. K R Venugopal is the Vice Chancellor of Bangalore University. He holds eleven degrees, including a Ph.D. in Computer Science Engineering from IIT-Madras, Chennai and a Ph.D. in Economics from Bangalore University. He also has degrees in Law, Mass Communication, Electronics, Economics, Business Finance, Computer Science, Public Relations and Industrial Relations. He has authored and edited 68 books and published more than 800 papers in refereed international journals and international conferences. Dr. Venugopal was a postdoctoral research scholar at the University of Southern California, USA. He has been conferred with IEEE fellow and ACM Distinguished Educator for his contributions to computer science engineering and electrical engineering education.
Dr. K C Srikantaiah is a Professor at the Department of Computer Science and Engineering at SJB Institute of Technology, Bangalore, India. He received his B.E. from Bangalore Institute of Technology, M.E.from University Visvesvaraya College of Engineering, Bangalore, in 2002 and Ph.D. degree in Computer Science and Engineering from Bangalore University in 2014. He has published 20 research papers and authored a book on Web mining algorithms. His research interests include data mining, Web mining, big data analytics, cloud analytics and the Semantic Web.
Dr. Sejal Santosh Nimbhorkar is an Associate Professor at B N M Institute of Technology. She has more than 15 years of industry, research and teaching experience. She holds M.E. and B.E. degrees in Computer Science and Engineering from University Visvesvaraya College of Engineering and Gujarat University, respectively. She has published 18 papers in refereed international journals and international conferences. She received an outstanding paper award at the 2015 European Conference on Data Mining. Dr. Nimbhorkar has also received project grants from Karnataka State Council for Science and Technology (KSCST). Her research interests include mining, Web mining, sentiment analysis and IoT.
Textul de pe ultima copertă
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.
Caracteristici
Covers the topic of Web recommender systems Presents algorithmic approaches in the field of Web recommendations Discusses how to measure the effectiveness of recommender systems and illustrates the methods Strikes a balance between fundamental concepts and state-of-the-art technologies