Search Engines for Web and Enterprise Data
Master web and enterprise search technologies. Learn indexing, ranking, and evaluation methods for building effective search systems.
Master web and enterprise search technologies. Learn indexing, ranking, and evaluation methods for building effective search systems.
This comprehensive course covers the technologies behind web and enterprise search engines. Over 30 hours, you'll explore document indexing, searching, and ranking techniques. The curriculum includes advanced topics like TFxIDF, vector space models, PageRank, HITS algorithm, and personalized search. You'll also learn about performance evaluation, relevance feedback, and enterprise search challenges. Through real-life examples and case studies, you'll gain practical insights into building effective search systems for both web and enterprise environments.
Instructors:
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What you'll learn
Understand the major tasks and challenges in web and enterprise search
Master document indexing techniques including inverted files and term selection
Learn various retrieval models: Boolean, Vector Space, and Extended Boolean
Understand link-based ranking algorithms like PageRank and HITS
Gain proficiency in evaluating search engine performance using metrics like precision and recall
Explore personalized search techniques and relevance feedback mechanisms
Skills you'll gain
This course includes:
371 Minutes PreRecorded video
16 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 16 modules in this course
This course provides a comprehensive exploration of search engine technologies for both web and enterprise data. The curriculum is divided into 16 modules, covering fundamental concepts to advanced techniques in information retrieval. Key topics include document indexing, TFxIDF, vector space models, inverted files, link-based ranking methods like PageRank and HITS, performance evaluation metrics, relevance feedback, and personalized search. The course also addresses enterprise search challenges and solutions. Each module includes video lectures, readings, and quizzes to reinforce learning. By the end of the course, students will have a deep understanding of search engine algorithms and be able to apply these concepts to real-world search problems.
Introduction to Search Engines for Web and Enterprise Data
Module 1 · 1 Hours to complete
Search Engine Business Model
Module 2 · 1 Hours to complete
TFxIDF
Module 3 · 1 Hours to complete
Vector Space Model
Module 4 · 2 Hours to complete
Inverted Files
Module 5 · 2 Hours to complete
Extended Boolean Model
Module 6 · 1 Hours to complete
PageRank
Module 7 · 2 Hours to complete
HITS Algorithm
Module 8 · 1 Hours to complete
Performance Evaluation of Information Retrieval System
Module 9 · 2 Hours to complete
Benchmarking
Module 10 · 1 Hours to complete
Stopword removal and Stemming
Module 11 · 2 Hours to complete
Relevance Feedback
Module 12 · 1 Hours to complete
Personalized Web Search
Module 13 · 2 Hours to complete
Index Term Selection
Module 14 · 1 Hours to complete
Discovering Phrases and Correlated Terms
Module 15 · 1 Hours to complete
Enterprise Search Engine
Module 16 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Expert in Search Engine Technologies at HKUST
Dik Lun Lee is a Professor at The Hong Kong University of Science and Technology, specializing in search engines for web and enterprise data. He teaches the course "Search Engines for Web and Enterprise Data," which focuses on the design, implementation, and optimization of search technologies. Professor Lee's research interests include information retrieval, data mining, and the development of efficient algorithms for processing large datasets. His expertise contributes significantly to advancements in search engine technologies and their applications in various domains.
Expert in Engineering Education at HKUST
Kenneth W. T. Leung is an Assistant Professor of Engineering Education in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST).
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