Search Engines for Web and Enterprise Data 

Lecture 1.1 - Example of Search Engines & Federated vs Meta Search

Lecture 1.2 - Difficulties & Document Retrieval Model & Evolution of Search Engines

Introduction to Search Engines for Web and Enterprise Data

Lecture 1 - Introduction to Search Engines for Web and Enterprise Data

Lecture 2.1 - Search Engine Business Model & Keyword Advertising

Lecture 2.2 - Search Engine Related Jobs & Charging Methods & Business History

Lecture 2 - Search Engine Business Model

Lecture 2 - Search Engine Business Model

Lecture 3.1 - Retrieval Models

Lecture 3.2 - TFxIDF

Lecture 3- TFxIDF

Lecture 3 - TFxIDF

Lecture 4.1 - Vector Space Model & Similarity

Lecture 4.2 - Interesting Things We Can Do in VSM

Lecture 4.3 - Choices of Similarity Measures & Query Term Weight

Lecture 4.4 - Term Independence Assumption & Synonyms & Unbalanced Property of VSM

Lecture 4 - Vector Space Model

Lecture 4 - Vector Space Model

Lecture 5.1 - Keyword Index & Postings List

Lecture 5.2 - Pros and Cons & Extensions

Lecture 5.3 - Insertion, Deletion and Update

Lecture 5.4 - Scalability Issues and Possible Solutions

Lecture 5- Inverted Files

Lecture 5 - Inverted Files

Lecture 6.1 - Soft Operators and Observations

Lecture 6.2 - Soft Operator Visualization & P-norm Model

Lecture 6 - Extended Boolean Model

Lecture 6 - Extended Boolean Model

Lecture 7.1 - HyPursuit and WISE

Lecture 7.2 - PageRank

Lecture 7.3 - Other aspects / Applications of PageRank

Lecture 7 - PageRank

Lecture 7 - PageRank

Lecture 8.1 - HITS Algorithm

Lecture 8.2 - Convergence and Normalization of HITS

Lecture 8.3 - Integrating PR and HITS in Search Engines

Lecture 8.4 - Observations of HITS and PR

Lecture 8 - HITS Algorithm

Lecture 8 - HITS Algorithm

Lecture 9.1 - Explicit Evaluation & Recall, Precision, and Fallout

Lecture 9.2 - Handling Inconsistency & Finding Relevant Items & Plotting Graphs

Lecture 9.3 - More Performance Measures

Lecture 9 - Performance Evaluation of IR System

Lecture 9 - Performance Evaluation of IR System

Lecture 10 - Benchmarking

Lecture 10 - Benchmarking

Lecture 10 - Benchmarking

Lecture 11.1 - Indexing Process Overview

Lecture 11.2 - Stemming Overview & Affix removal Algorithms

Lecture 11.3 - Corpora Based Statistical Stemming

Lecture 11.4 - Purpose of Obtaining the Stem of a Word

Lecture 11 - Stopword Removal and Stemming

Lecture 11 - Stopword Removal and Stemming

Lecture 12.1 - Overview & Manual vs Automatic Feedback & Implicit vs Explicit Feedback

Lecture 12.2 - Query Modification

Lecture 12.3 - Document Modification

Lecture 12 - Relevance Feedback

Lecture 12 - Relevance Feedback

Lecture 13.1 - Overview of Personalized Web Search

Lecture 13.2 - Eye-tracking Experiment & Clickthrough Analysis

Lecture 13.3 - Preference Mining Strategies

Lecture 13.4 - Apply User Preferences to Ranking

Lecture 13 - Personalized Web Search

Lecture 13 - Personalized Web Search

Lecture 14.1 - Zipf’s Law

Lecture 14.2 - Term Discrimination Values

Lecture 14.3 - Term Discrimination Value vs Document Frequency & Applying DV in Term Selection

Lecture 14 - Index Term Selection

Lecture 14 - Index Term Selection

Lecture 15.1 - N-gram

Lecture 15.2 - Collocation and Co-occurrence

Lecture 15.3 - Pointwise Mutual Information

Lecture 15 - Discovering Phrases and Correlated Terms

Lecture 15 - Discovering Phrases and Correlated Terms

Lecture 16.1 - Enterprise Search and Challenges

Lecture 16.2 - Enterprise Search Engine

Lecture 16.3 - Advanced Requirements of Enterprise SE

Lecture 16 - Enterprise Search Engine

Lecture 16 - Enterprise Search Engine