Search Engines for Web and Enterprise Data

The Hong Kong University of Science and Technology

Search Engines for Web and Enterprise Data

位教师：Kenneth Wai-Ting Leung

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16个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

16个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

3 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将获得的技能

要了解的详细信息

可分享的证书

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作业

16 项作业

授课语言：英语（English）

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有16个模块

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.

涵盖的内容

2个视频1篇阅读材料1个作业

2个视频总计22分钟

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

1篇阅读材料总计60分钟

Introduction to Search Engines for Web and Enterprise Data 60分钟

1个作业总计30分钟

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

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

涵盖的内容

2个视频1篇阅读材料1个作业

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.

涵盖的内容

2个视频1篇阅读材料1个作业

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.

涵盖的内容

4个视频1篇阅读材料1个作业

4个视频总计32分钟

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

1篇阅读材料总计60分钟

Lecture 4 - Vector Space Model 60分钟

1个作业总计30分钟

Lecture 4 - Vector Space Model 30分钟

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

涵盖的内容

4个视频1篇阅读材料1个作业

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.

涵盖的内容

2个视频1篇阅读材料1个作业

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.

涵盖的内容

3个视频1篇阅读材料1个作业

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.

涵盖的内容

4个视频1篇阅读材料1个作业

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.

涵盖的内容

3个视频1篇阅读材料1个作业

3个视频总计36分钟

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

1篇阅读材料总计60分钟

Lecture 9 - Performance Evaluation of IR System 60分钟

1个作业总计30分钟

Lecture 9 - Performance Evaluation of IR System 30分钟

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

涵盖的内容

1个视频1篇阅读材料1个作业

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.

涵盖的内容

4个视频1篇阅读材料1个作业

4个视频总计33分钟

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

1篇阅读材料总计60分钟

Lecture 11 - Stopword Removal and Stemming 60分钟

1个作业总计30分钟

Lecture 11 - Stopword Removal and Stemming 30分钟

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.

涵盖的内容

3个视频1篇阅读材料1个作业

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.

涵盖的内容

4个视频1篇阅读材料1个作业

4个视频总计32分钟

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

1篇阅读材料总计60分钟

Lecture 13 - Personalized Web Search 60分钟

1个作业总计30分钟

Lecture 13 - Personalized Web Search 30分钟

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.

涵盖的内容

3个视频1篇阅读材料1个作业

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.