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.
Search Engines for Web and Enterprise Data
Search Engines for Web and Enterprise Data
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作业
16 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有16个模块
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
2个视频• 总计17分钟
- Lecture 2.1 - Search Engine Business Model & Keyword Advertising• 10分钟
- Lecture 2.2 - Search Engine Related Jobs & Charging Methods & Business History• 7分钟
1篇阅读材料• 总计60分钟
- Lecture 2 - Search Engine Business Model• 60分钟
1个作业• 总计30分钟
- Lecture 2 - Search Engine Business Model• 30分钟
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
2个视频• 总计20分钟
- Lecture 3.1 - Retrieval Models• 11分钟
- Lecture 3.2 - TFxIDF• 9分钟
1篇阅读材料• 总计60分钟
- Lecture 3- TFxIDF• 60分钟
1个作业• 总计30分钟
- Lecture 3 - TFxIDF• 30分钟
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
4个视频• 总计34分钟
- Lecture 5.1 - Keyword Index & Postings List• 11分钟
- Lecture 5.2 - Pros and Cons & Extensions• 8分钟
- Lecture 5.3 - Insertion, Deletion and Update• 10分钟
- Lecture 5.4 - Scalability Issues and Possible Solutions• 4分钟
1篇阅读材料• 总计60分钟
- Lecture 5- Inverted Files• 60分钟
1个作业• 总计30分钟
- Lecture 5 - Inverted Files• 30分钟
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
2个视频• 总计16分钟
- Lecture 6.1 - Soft Operators and Observations• 9分钟
- Lecture 6.2 - Soft Operator Visualization & P-norm Model• 6分钟
1篇阅读材料• 总计60分钟
- Lecture 6 - Extended Boolean Model• 60分钟
1个作业• 总计30分钟
- Lecture 6 - Extended Boolean Model• 30分钟
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
3个视频• 总计38分钟
- Lecture 7.1 - HyPursuit and WISE• 13分钟
- Lecture 7.2 - PageRank• 10分钟
- Lecture 7.3 - Other aspects / Applications of PageRank• 15分钟
1篇阅读材料• 总计60分钟
- Lecture 7 - PageRank• 60分钟
1个作业• 总计30分钟
- Lecture 7 - PageRank• 30分钟
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
4个视频• 总计25分钟
- Lecture 8.1 - HITS Algorithm• 11分钟
- Lecture 8.2 - Convergence and Normalization of HITS• 3分钟
- Lecture 8.3 - Integrating PR and HITS in Search Engines• 4分钟
- Lecture 8.4 - Observations of HITS and PR• 8分钟
1篇阅读材料• 总计60分钟
- Lecture 8 - HITS Algorithm• 60分钟
1个作业• 总计30分钟
- Lecture 8 - HITS Algorithm• 30分钟
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
1个视频• 总计12分钟
- Lecture 10 - Benchmarking• 12分钟
1篇阅读材料• 总计60分钟
- Lecture 10 - Benchmarking• 60分钟
1个作业• 总计30分钟
- Lecture 10 - Benchmarking• 30分钟
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
3个视频• 总计28分钟
- Lecture 12.1 - Overview & Manual vs Automatic Feedback & Implicit vs Explicit Feedback• 6分钟
- Lecture 12.2 - Query Modification• 11分钟
- Lecture 12.3 - Document Modification• 11分钟
1篇阅读材料• 总计10分钟
- Lecture 12 - Relevance Feedback• 10分钟
1个作业• 总计30分钟
- Lecture 12 - Relevance Feedback• 30分钟
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
3个视频• 总计25分钟
- Lecture 14.1 - Zipf’s Law• 10分钟
- Lecture 14.2 - Term Discrimination Values• 9分钟
- Lecture 14.3 - Term Discrimination Value vs Document Frequency & Applying DV in Term Selection• 6分钟
1篇阅读材料• 总计60分钟
- Lecture 14 - Index Term Selection• 60分钟
1个作业• 总计30分钟
- Lecture 14 - Index Term Selection• 30分钟
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.
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25分钟
- Lecture 15.1 - N-gram• 9分钟
- Lecture 15.2 - Collocation and Co-occurrence• 9分钟
- Lecture 15.3 - Pointwise Mutual Information• 8分钟
1篇阅读材料• 总计60分钟
- Lecture 15 - Discovering Phrases and Correlated Terms• 60分钟
1个作业• 总计30分钟
- Lecture 15 - Discovering Phrases and Correlated Terms• 30分钟
In this module, you will learn: (1) The challenges of enterprise search. 2) The differences between web search and enterprise search.
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18分钟
- Lecture 16.1 - Enterprise Search and Challenges• 7分钟
- Lecture 16.2 - Enterprise Search Engine• 5分钟
- Lecture 16.3 - Advanced Requirements of Enterprise SE• 6分钟
1篇阅读材料• 总计60分钟
- Lecture 16 - Enterprise Search Engine• 60分钟
1个作业• 总计30分钟
- Lecture 16 - Enterprise Search Engine• 30分钟
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提供方
提供方
The Hong Kong University of Science and Technology
HKUST is a world-class research-intensive university that focuses on science, technology, and business as well as humanities and social science. HKUST offers an international campus, and a holistic and interdisciplinary pedagogy to nurture well-rounded graduates with a global vision, a strong entrepreneurial spirit, and innovative thinking.
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