Search Engines for Web and Enterprise Data

The Hong Kong University of Science and Technology

Search Engines for Web and Enterprise Data

Instructors: Kenneth Wai-Ting Leung

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2,098 already enrolled

16 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

16 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

16 assignments

Taught in English

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There are 16 modules in this course

This course introduces the technologies behind web and search engines, including document indexing, searching and ranking. You will also learn different performance metrics for evaluating search quality, methods for understanding user intent and document semantics, and advanced applications including recommendation systems and summarization. Real-life examples and case studies are provided to reinforce the understanding of search algorithms.

Welcome to the first module of this course! In this module, you will learn: (1) The major tasks involved in web search. (2) The history, evolution, impacts and challenges of web search engine.

What's included

2 videos1 reading1 assignment

2 videos Total 22 minutes

Lecture 1.1 - Example of Search Engines & Federated vs Meta Search 12 minutes
Lecture 1.2 - Difficulties & Document Retrieval Model & Evolution of Search Engines 10 minutes

1 reading Total 60 minutes

Introduction to Search Engines for Web and Enterprise Data 60 minutes

1 assignment Total 30 minutes

Lecture 1 - Introduction to Search Engines for Web and Enterprise Data 30 minutes

In this module, you will learn: (1) Different business models of web search engine.

What's included

2 videos1 reading1 assignment

In this module, you will learn: (1) Different information retrieval models, Boolean Models and Statistical models. (2) How to determine important words in a document using TFxIDF.

What's included

2 videos1 reading1 assignment

In this module, you will learn: (1) How to represent a document/query as a vector of keywords. 2) How to determine the degree of similarity between a pair of vectors using different similarity measures, including Inner Product, Cosine Similarity, Jaccard Coefficient, Dice Coefficient.

What's included

4 videos1 reading1 assignment

4 videos Total 32 minutes

Lecture 4.1 - Vector Space Model & Similarity 12 minutes
Lecture 4.2 - Interesting Things We Can Do in VSM 5 minutes
Lecture 4.3 - Choices of Similarity Measures & Query Term Weight 6 minutes
Lecture 4.4 - Term Independence Assumption & Synonyms & Unbalanced Property of VSM 9 minutes

1 reading Total 60 minutes

Lecture 4 - Vector Space Model 60 minutes

1 assignment Total 30 minutes

Lecture 4 - Vector Space Model 30 minutes

In this module, you will learn: (1) How to index documents using inverted files. 2) How to perform update and deletion on inverted files.

What's included

4 videos1 reading1 assignment

4 videos Total 34 minutes

Lecture 5.1 - Keyword Index & Postings List 11 minutes
Lecture 5.2 - Pros and Cons & Extensions 8 minutes
Lecture 5.3 - Insertion, Deletion and Update 10 minutes
Lecture 5.4 - Scalability Issues and Possible Solutions 4 minutes

1 reading Total 60 minutes

Lecture 5- Inverted Files 60 minutes

1 assignment Total 30 minutes

Lecture 5 - Inverted Files 30 minutes

In this module, you will learn: (1) How to use Extended Boolean Model to rank documents. 2) How to evaluate conjunctive and disjunctive queries using Extended Boolean Model.

What's included

2 videos1 reading1 assignment

In this module, you will learn: (1) The history and evolution of link-based ranking methods. 2) How to determine query/document similarities using HyPursuit, WISE, and PageRank. 3) Possible extensions that can be applied to Pagerank.

What's included

3 videos1 reading1 assignment

In this module, you will learn: (1) How to calculate hub and authority scores of web documents using HITS algorithm. 2) Understand the re-ranking process involved in HITS algorithm.

What's included

4 videos1 reading1 assignment

In this module, you will learn: (1) How to evaluate retrieval effectiveness of an information retrieval using Precision, Recall, F-Measure, Average-Precision, DCG, and NDCG. 2) What are the subjective relevance measures to be used on an information retrieval system.

What's included

3 videos1 reading1 assignment

3 videos Total 36 minutes

Lecture 9.1 - Explicit Evaluation & Recall, Precision, and Fallout 13 minutes
Lecture 9.2 - Handling Inconsistency & Finding Relevant Items & Plotting Graphs 12 minutes
Lecture 9.3 - More Performance Measures 11 minutes

1 reading Total 60 minutes

Lecture 9 - Performance Evaluation of IR System 60 minutes

1 assignment Total 30 minutes

Lecture 9 - Performance Evaluation of IR System 30 minutes

In this module, you will learn: (1) How to use the TREC collection for benchmarking. 2) The characteristics of the TREC collection.

What's included

1 video1 reading1 assignment

In this module, you will learn: (1) What is stemming. 2) Different Content-Sensitive and Context-Free stemming algorithms. 3) How to calculate Successor Variety and Entropy for stemming.

What's included

4 videos1 reading1 assignment

4 videos Total 33 minutes

Lecture 11.1 - Indexing Process Overview 8 minutes
Lecture 11.2 - Stemming Overview & Affix removal Algorithms 10 minutes
Lecture 11.3 - Corpora Based Statistical Stemming 10 minutes
Lecture 11.4 - Purpose of Obtaining the Stem of a Word 4 minutes

1 reading Total 60 minutes

Lecture 11 - Stopword Removal and Stemming 60 minutes

1 assignment Total 30 minutes

Lecture 11 - Stopword Removal and Stemming 30 minutes

In this module, you will learn: (1) How to perform document space modification using relevance feedback. 2) How to perform query modification using relevance feedback.

What's included

3 videos1 reading1 assignment

In this module, you will learn: (1) Relative preference is more useful than absolute preference in personalization. 2) The importance of eye-tracking user study in personalized web search. 3) How to model preferences as a weighted vector.

What's included

4 videos1 reading1 assignment

4 videos Total 32 minutes

Lecture 13.1 - Overview of Personalized Web Search 6 minutes
Lecture 13.2 - Eye-tracking Experiment & Clickthrough Analysis 6 minutes
Lecture 13.3 - Preference Mining Strategies 7 minutes
Lecture 13.4 - Apply User Preferences to Ranking 13 minutes

1 reading Total 60 minutes

Lecture 13 - Personalized Web Search 60 minutes

1 assignment Total 30 minutes

Lecture 13 - Personalized Web Search 30 minutes

In this module, you will learn: (1) How to calculate discrimination value for index term selection. 2) The importance of word usage in documents in search engine design.

What's included

3 videos1 reading1 assignment

In this module, you will learn: (1) How to use collocated terms in lieu of strict phrases in search. 2) How to identify collocated terms using Pointwise Mutual Information (PMI). 3) How to utilize N-grams for search.