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Web Recommendations Systems
ISBN-13: 9789811525124 / Angielski / Twarda / 2020 / 164 str.
Web Recommendations Systems
ISBN-13: 9789811525124 / Angielski / Twarda / 2020 / 164 str.
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(netto: 521,18 VAT: 5%)
Najniższa cena z 30 dni: 539,74 zł
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Kategorie:
Kategorie BISAC:
Wydawca:
Springer
Język:
Angielski
ISBN-13:
9789811525124
Rok wydania:
2020
Wydanie:
2020
Ilość stron:
164
Waga:
0.45 kg
Wymiary:
23.62 x 16.51 x 1.27
Oprawa:
Twarda
Wolumenów:
01
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
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 MPCLSW
7.5 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 Similarity
9.3.3 WordNet based Semantic Similarity
9.3.4 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 Algorithm
10.4 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 Algorithm
11.3.1 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 Algorithm
14.3.1 Problem Definition
14.3.2 Image Recommendation Framework
14.3.3 IR URFS VF Algorithm
14.4 Experiments
14.4.1 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 Algorithm
15.4 Experiments
15.4.1 Data Collection
15.4.2 Experiment Set-up
15.4.3 Performance Evaluation
15.5 Summary
References
Index
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.
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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