Venugopal / Srikantaiah / Santosh Nimbhorkar Web Recommendations Systems

1. Auflage 2020
ISBN: 978-981-15-2513-1
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 164 Seiten, eBook

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.

Zielgruppe

Research

Autoren/Hrsg.

Venugopal, K. R.

Srikantaiah, K. C.

Santosh Nimbhorkar, Sejal

Inhaltsverzeichnis

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 Algorithm4.4.4 Example4.5 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 Algorithm5.5 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 
7 Construction of Topic Directories 
7.1 Introduction 7.2 Related Works 7.3 System Architecture 7.3.1 Problem Definition 7.4 Mathematical Model and Algorithm 7.4.1 Hashing 7.4.2 Levenshtein Distance (LD) 7.4.3 Levenshtein Similarity Weight (LSW) 7.4.4 Similarity betweenWebpage and Category in Web Directory 7.4.5 Mapping of Pages onto Categories 7.4.6 Algorithm MPCLSW7.5 Experiments 7.5.1 Execution Time 7.5.2 Accuracy 7.5.3 Precision 7.5.4 Recall 7.5.5 F-score 7.6 Summary References 
8 Query Relevance Graph for Query Recommendations 
8.1 Introduction 8.2 Related Works 8.2.1 Query Expansion 8.2.2 Snippet based Query Recommendations 8.2.3 Graph based Query Recommendations 8.2.4 Recommendation Applications 8.3 Query Relevance Model and QRGQR Algorithm 8.3.1 Problem Definition 8.3.2 Query Click Graph 8.3.3 Query Text Similarity Graph 8.3.4 Query Relevance Graph 8.3.5 QRGQR Algorithm 8.4 Experiments 8.4.1 Data Collection 8.4.2 Data Cleaning 8.4.3 Varying of Parameter-Jaccard Coefficient 8.4.4 Query Recommendation results 8.4.5 Performance Analysis 8.4.6 Efficiency 8.4.7 Image Recommendation 8.5 Summary References 
9 Related Search Recommendation with User Feedback Session 
9.1 Introduction 9.2 Related Works 9.2.1 Measuring Similarity between Two Words 9.2.2 Query Recommendation Techniques 9.3 Related Search Recommendation Framework and RSR Algorithm9.3.1 Problem Definition 9.3.2 Co-occurrenceMeasures to Compute Semantic Similarity9.3.3 WordNet based Semantic Similarity9.3.4 Rocchio’s Model 9.3.5 Snippet Click Model 9.3.6 RSR Algorithm 9.4 Experiments 9.4.1 Data Collection 9.4.2 Experiment Setup 9.4.3 Query Recommendation Results 9.4.4 Performance Analysis 9.5 Summary References
10 Webpage Recommendations based Web Navigation Prediction 
10.1 Introduction 10.2 Related Works 10.2.1 Web Page Prediction 10.2.2 Prediction Applications 10.3 Web Navigation Prediction Framework and WNPWR Algorithm 10.3.1 Problem Definition 10.3.2 Session Identification Method with Average Time of Visiting Web Pages 10.3.3 Prediction Models 10.3.4 Two-Tier Prediction Framework 10.3.5 WNPWR Algorithm10.4 Experiments 10.4.1 Data Collection 10.4.2 User and Session Identification 10.4.3 Experiment Setup 10.4.4 Results Comparison 10.5 Summary References 
11 Webpage Recommendations based on User Session Graph 
11.1 Introduction 11.2 Related Works 11.3 Webpage Recommendations Framework andWRUSG Algorithm11.3.1 Problem Definition 11.3.2 Webpage Recommendations Framework 11.3.3 WRUSG Algorithm 11.4 Experiments 11.4.1 Data Collection 11.4.2 Experiment Set-up 11.4.3 PerformanceMetrics 11.4.4 Performance Evaluation 11.5 Summary References 
12 Advertisement Recommendations using ExpectationMaximization
12.1 Introduction 12.2 Related Works 12.3 Prediction Conversion in Advertising using Expectation Maximization Model and PCAEM Algorithm 12.3.1 Problem Definition 12.3.2 Prediction Conversion in Advertising using Expectation Maximization Model 12.3.3 PCAEM Algorithm 12.4 Experiments 12.4.1 Data Collection 12.4.2 Experiment Setup 12.4.3 PerformanceMetrics 12.4.4 Performance Evaluation 12.5 Summary References 
13 Image Recommendations with Absorbing Markov Chain 
13.1 Introduction 13.2 Related Works 13.2.1 Content Based Image Retrieval 13.2.2 Annotation Based Image Retrieval 13.3 Image Recommendation Framework and IRAbMC Algorithm 13.3.1 Problem Definition 13.3.2 Image Recommendation Framework 13.3.3 IRAbMC Algorithm 13.4 Experiments 13.4.1 Data Collection 13.4.2 Experiment Setup 13.4.3 Performance Evaluation 13.5 Summary References 
14 Image Recommendation with User Relevance Feedback Session 
14.1 Introduction 14.2 Related Works 14.3 Image Recommendation Framework and IR URFS VF Algorithm14.3.1 Problem Definition 14.3.2 Image Recommendation Framework 14.3.3 IR URFS VF Algorithm 14.4 Experiments14.4.1 Data Collection 14.4.2 Experiment Setup 14.4.3 Performance Evaluation 14.5 Summary References 
15 Image Recommendation by ANOVA Cosine Similarity 
15.1 Introduction 15.2 Related Works 15.2.1 Content Based Image Retrieval (CBIR) 15.2.2 Annotation Based Image Retrieval (ABIR) 15.2.3 Text + Visual (Hybrid) method for image search 15.2.4 Image Search with reduced Semantic Gap 15.3 ACSIR Framework and Algorithm 15.3.1 Problem Definition 15.3.2 ACSIR Framework 15.3.3 ACSIR Algorithm15.4 Experiments 15.4.1 Data Collection 15.4.2 Experiment Set-up 15.4.3 Performance Evaluation 15.5 Summary References 
Index 

Über Autor(innen)

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.