coursera课程Text Retrieval and Search Engines之Week 4 Overview

Week 4 OverviewHelp Center

Week 4

On this page:

• Instructional Activities
• Time
• Goals and Objectives
• Key Phrases/Concepts
• Guiding Questions
• Readings and Resources
• Video Lectures
• Tips for Success
• Getting and Giving Help

Instructional Activities

Below is a list of the activities and assignments available to you this week. See the How to Pass the Class page to know which assignments pertain to the badge or badges you are pursuing. Click on the name of each activity for more detailed instructions.

Relevant Badges	Activity	Due Date*	Estimated Time Required
	Week 4 Video Lectures	Sunday, April 19 (suggested)	3 hours
	Programming Assignment 2	Sunday, April 26	2–3 hours
	Week 4 Quiz	Sunday, April 19	~0.5 hours

* All deadlines are at 11:55 PM Central Time (time zone conversion) unless otherwise noted.

Time

This module will last 7 days and should take approximately 6 hours of dedicated time to complete, with its readings and assignments.

Goals and Objectives

After you actively engage in the learning experiences in this module, you should be able to:

• Explain some of the main general challenges in creating a web search engine.
• Explain what a web crawler is and what factors have to be considered when designing a web crawler.
• Explain the basic idea of Google File System (GFS).
• Explain the basic idea of MapReduce and how we can use it to build an inverted index in parallel.
• Explain how links on the web can be leveraged to improve search results.
• Explain how PageRank and HITS algorithms work.
• Explain the basic idea of using machine learning to combine multiple features for ranking documents (aka learning to rank).
• Explain how we can extend a retrieval system to perform content-based information filtering (recommendation).
• Explain how we can use a linear utility function to evaluate an information filtering system.
• Explain the basic idea of collaborative filtering.
• Explain how the memory-based collaborative filtering algorithm works.

Key Phrases/Concepts

Keep your eyes open for the following key terms or phrases as you complete the readings and interact with the lectures. These topics will help you better understand the content in this module.

• Scalability, efficiency
• Spam
• Crawler, focused crawling, incremental crawling
• Google File System (GFS)
• MapReduce
• Link analysis, anchor text
• PageRank, HITS
• Learning to rank, features, logistic regression
• Content-based filtering
• Collaborative filtering
• Beta-gamma threshold learning
• Linear utility
• User profile
• Exploration-exploitation tradeoff
• Memory-based collaborative filtering
• Cold start

Guiding Questions

Develop your answers to the following guiding questions while completing the readings and working on assignments throughout the week.

• What are some of the general challenges in building a web search engine?
• What is a crawler? How can we implement a simple crawler?
• What is focused crawling? What is incremental crawling?
• What kind of pages should have a higher priority for recrawling in incremental crawling?
• What can we do if the inverted index doesn’t fit in any single machine?
• What’s the basic idea of Google File System (GFS)?
• How does MapReduce work? What are the two key functions that a programmer needs to implement when programming with a MapReduce framework?
• How can we use MapReduce to build an inverted index in parallel?
• What is anchor text? Why is it useful for improving search accuracy?
• What is a hub page? What is an authority page?
• What kind of web pages tend to receive high scores from PageRank?
• How can we interpret PageRank from the perspective of a random surfer “walking” on the web?
• How exactly do you compute PageRank scores?
• How does the HITS algorithm work?
• What’s the basic idea of learning to rank?
• How can logistic regression be used to combine multiple features for improving ranking accuracy of a search engine?
• What is content-based information filtering?
• How can we use a linear utility function to evaluate a filtering system? How should we set the coefficients in such a linear utility function?
• How can we extend a retrieval system to perform content-based information filtering?
• What is exploration-exploitation tradeoff?
• How does the beta-gamma threshold learning algorithm work?
• What is the basic idea of collaborative filtering?
• How does the memory-based collaborative filtering algorithm work?
• What is the “cold start” problem in collaborative filtering?

Readings and Resources

All the readings are available online

1. For web search, read chapters 19, 20, and 21 of the following book: 
   Introduction to Information Retrieval, by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schuetze, Cambridge University Press, 2007.

1. For beta-gamma threshold learning, read the following paper:
   Threshold Calibration in CLARIT Adaptive Filtering, by ChengXiang Zhai, Peter Jansen, Emilia Stoica, Norbert Grot, David A. Evans, Proceedings of TREC 1998.

1. For content-based filtering in general and memory-based collaborative filtering, read Chapters 3 & 4 of the following book:
   Recommender Systems Handbook, by Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor,  Springer 2011.

Video Lectures

Video Lecture	Lecture Notes	Transcript	Video Download	SRT Caption File	Forum
4.1. Web Search: Introduction & Web Crawler(00:11:05)			(15.4 MB)	Forthcoming...
4.2. Web Search: Web Indexing(00:17:19)			(23.8 MB)	Forthcoming...
4.3. Web Search: Link Analysis – Part 1(00:09:16)			(12.4 MB)	Forthcoming...
4.3. Web Search: Link Analysis – Part 2(00:17:30)			(24.4 MB)	Forthcoming...
4.3. Web Search: Link Analysis – Part 3(00:05:59)			(8.1 MB)	Forthcoming...
4.4. Web Search: Learning to Rank – Part 1(00:05:54)			(8.8 MB)	Forthcoming...
4.4. Web Search: Learning to Rank – Part 2(00:10:23)			(14.3 MB)	Forthcoming...
4.4. Web Search: Learning to Rank – Part 3(00:04:58)			(7.3 MB)	Forthcoming...
4.5. Web Search: Future of Web Search(00:13:09)			(18.1 MB)	Forthcoming...
4.6. Recommender Systems: Content-Based Filtering – Part 1 (00:12:55)			(17.4 MB)	Forthcoming...
4.6. Recommender Systems: Content-Based Filtering – Part 2(00:10:42)			(14.5 MB)	Forthcoming...
4.7. Recommender Systems: Collaborative Filtering - Part 1(00:06:20)			(8.8 MB)	Forthcoming...
4.7. Recommender Systems: Collaborative Filtering - Part 2(00:12:09)			(16.7 MB)	Forthcoming...
4.7. Recommender Systems: Collaborative Filtering - Part 3(00:04:45)			(7.1 MB)	Forthcoming...
4.8. Course Summary(00:09:48)			(13.9 MB)	Forthcoming...

Tips for Success

To do well this week, I recommend that you do the following:

• Review the video lectures a number of times to gain a solid understanding of the key questions and concepts introduced this week.
• When possible, provide tips and suggestions to your peers in this class. As a learning community, we can help each other learn and grow. One way of doing this is by helping to address the questions that your peers pose. By engaging with each other, we’ll all learn better.
• It’s always a good idea to refer to the video lectures and chapter readings we've read during this week and reference them in your responses. When appropriate, critique the information presented.
• Take notes while you read the materials and watch the lectures for this week. By taking notes, you are interacting with the material and will find that it is easier to remember and to understand. With your notes, you’ll also find that it’s easier to complete your assignments. So, go ahead, do yourself a favor; take some notes!

Getting and Giving Help

You can get/give help via the following means:

• Use the Learner Help Center to find information regarding specific technical problems. For example, technical problems would include error messages, difficulty submitting assignments, or problems with video playback. You can access the Help Center by clicking on theHelp link at the top right of any course page. If you can not find an answer in the documentation, you can also report your problem to the Coursera staff by clicking on the Contact Us! link available on each topic's page within the Learner Help Center.
• Use the Content Issues forum to report errors in lecture video content, assignment questions and answers, assignment grading, text and links on course pages, or the content of other course materials. University of Illinois staff and Community TAs will monitor this forum and respond to issues.

As a reminder, the instructor is not able to answer emails sent directly to his account. Rather, all questions should be reported as described above.

from: https://class.coursera.org/textretrieval-001/wiki/Week4Overview