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.
Autorentext
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.
Inhalt
1 Introduction
1.1 World Wide Web
1.2 Web Mining
1.2.1 Issues in Web Mining
1.3 Web Recommendations
1.4 Classification of Recommender system
1.4.1 Query Recommendations
1.4.2 Webpage Recommendations
1.4.3 Image Recommendations
References
2 Web Data Extraction and Integration System for Search Engine Result Pages
2.1 Introduction
2.2 Related Works
2.3 System Architecture
2.3.1 Problem Definition
2.4 Mathematical Model and Algorithms
2.4.1 Web Data Extraction using Similarity Function(WDES)
2.4.2 Web Data Integration using Cosine Similarity(WDICS)
2.5 Experiments
2.5.1 Precision and Recall Vs. Attributes
2.6 Summary
References
3 Mining and Analysis ofWeb Sequential Patterns
3.1 Introduction
3.2 Related Works
3.3 System Architecture
3.3.1 Problem Definition
3.4 BGCAP Algorithm
3.5 Experiments
3.5.1 Data Size Vs. Run Time
3.5.2 Threshold Vs. Run Time
3.5.3 Threshold Vs. Number of Patterns 3.6 Summary
References
4 Web Caching and Prefetching
4.1 Introduction
4.2 Related Works
4.3 System Architecture
4.3.1 Problem Definition
4.3.2 Basic Definitions
4.4 Mathematical Model
4.4.1 Finding Prefetching Rules using Periodicity
4.4.2 Profit Function
4.4.3 WCP-CMA Algorithm
4.4.4 Example
4.5 Experiments
4.5.1 Cache Hit Ratio
4.5.2 Delay
4.5.3 Effect of Periodicity
4.5.4 Effect of Cyclic Behaviour
4.5.5 Execution Time
4.6 Summary
References
5 Discovery of Synonyms from the Web
5.1 Introduction
5.2 Related Works
5.3 System Architecture
5.3.1 Problem Definition 5.4 System Model and Algorithm
5.4.1 Generation of Candidate Synonyms
5.4.2 Ranking of Candidate Synonyms
5.4.3 ASWAT Algorithm
5.5 Experiments
5.6 Summary
References 6 Ranking Search Engine Result Pages of a Specialty Search Engine
6.1 Introduction
6.2 Related Works
6.3 System Architecture
6.3.1 Problem Definition
6.4 Mathematical Model
6.4.1 Probability of Correctness of Facts (PCF)
6.4.2 Implication Between Facts
6.4.3 SIM (TF,F0 ) for Books Domain
6.5 Complexity Analysis
6.5.1 Time Complexity
6.5.2 Space Complexity
6.6 Experiments
6.7 Summary
References
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Titel
Web Recommendations Systems
EAN
9789811525131
Format
E-Book (pdf)
Hersteller
Veröffentlichung
02.03.2020
Digitaler Kopierschutz
Wasserzeichen
Dateigrösse
4.65 MB
Anzahl Seiten
164
Unerwartete Verzögerung
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