The Hong Kong University of Science and Technology
Search Engines for Web and Enterprise Data
The Hong Kong University of Science and Technology

Search Engines for Web and Enterprise Data

Kenneth W T Leung
Dik Lun LEE

Instructors: Kenneth W T Leung

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Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
30 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
30 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace

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Assessments

16 assignments

Taught in English

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There are 16 modules in this course

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

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

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

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

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

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

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.

What's included

3 videos1 reading1 assignment

In this module, you will learn: (1) The challenges of enterprise search. 2) The differences between web search and enterprise search.

What's included

3 videos1 reading1 assignment

Instructors

Kenneth W T Leung
The Hong Kong University of Science and Technology
7 Courses194,380 learners
Dik Lun LEE
The Hong Kong University of Science and Technology
1 Course1,110 learners

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