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 Algorithm
9.3.1 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
