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Web Intelligence and Social Computing

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Extra Credit Assignments

2011-12 Term 1

Lecture I Lecture II Tutorial I Tutorial II

The Golden Rule of XXXX: No member of the XXXX community shall take unfair advantage of any other member of the XXXX community.

Course Description

This course introduces fundamental as well as applied computational techniques for collaborative and collective intelligence of group behaviours on the Internet. The course topics include, but are not limited to: web intelligence, web data mining, knowledge discovery on the web, web analytics, web information retrieval, learning to rank, ranking algorithms, relevance feedback, collaborative filtering, recommender systems, human/social computation, social games, opinion mining, sentiment analysis, models and theories about social networks, large graph and link-based algorithms, social marketing, monetization of the web, security/privacy issues related to web intelligence and social computing, etc.

Learning Objectives

Learning Outcomes

Learning Activities

  1. Lectures
  2. Tutorials
  3. Web resources
  4. Videos
  5. Quizzes
  6. Examinations


Lecturer Tutor Tutor
Name Irwin King Tom Chao Zhou Xin Xin
Email king AT czhou AT xxin AT
Office Rm 908 Room114A Room101
Telephone 2609 8398
Office Hour(s) * M10, Monday 4:30 to 5:30

* T3, Tuesday 10:30 to 11:30
Tuesday 15:30 to 16:30 Tuesday 15:30 to 16:30


The pdf files are created in Acrobat 6.0. Please obtain the correct version of the Acrobat Reader from Adobe.

Week Date Topics Tutorials Homework & Events Resources
1 7/9 Introduction to Web Intelligence and Social Computing
Web 2.0

Tim O'Reilly on Web 2.0, The Economist, 20/3/2009
2 14/9 Introduction to Web Intelligence and Social Computing

Regular Expressions Web Crawler Introduction to Social Networks
3 21/9 Social Networks-Theory
Graph Theory

02-SNA.pdf OLD!
02-SNA-01.pdf OLD!
02-SNA-02.pdf NEW!
Graph Visualization HW #1
(Due on or before 6:30 pm, Friday, 2 October, 2009)
SWT Theory
4 28/9 Graph Mining


Graph Mining Algorithms
Generating Random Graphs
The Clique Algorithm
5 5/10 Link Analysis

04-LinkAnalysis-01.pdf NEW!

PageRank, HITS, etc. HW #2
hw2 sample answer
HW Programming #1 HW Programming #1 Testcases

(Due on or before 6:30 pm, Monday, 19 October, 2009)
Introduction to Information Retrieval
6 12/10 Learning to Rank

05-Learning2Rank-01.pdf OLD!
05-Learning2Rank-02.pdf NEW!

PageRank Project Specification
Movie Dataset
7 19/10 Recommender Systems I

Evaluation Methods
8 26/10 Recommender Systems II
Query Expansion

CIKM2008 Query Suggestion
(Due: Monday,23 November,18:30)
9 2/11 Human Computation/Social Games

07-HumanComputation-01.pdf NEW!
humancomputation.ppt Guest Speaker
10 9/11 Crowdsourcing language model
11 16/11 Q&A
Virtual Communities

Wikis, Blogs, etc. HW #4
(Due: Friday, 4 December 4, 2009, 18:30)
gradings asgn4
hw4 sample answer
12 23/11 Privacy and Security of Information
Education, Policy

13 30/11 Wrap Up

Project Presentations
EduTech on Social Computing in Education
  • Web 2.0
    • Ajax, CSS,
  • Social Media
    • blogs, microblogs, wikis, mashup,

Class Project

Class Project Presentation Schedule

Class Project Presentation Requirements

Examination Matters

Examination Schedule

Time Venue Notes
Midterm Examination
Midterm Examination
Final Examination 9/12/2009 Wed.
9:30 am to 11:30 am
Room 103, John Fulton Centre The final examination covers all materials presented in the class.

Written Midterm Matters

  1. The midterm will test your knowledge of the materials.
  2. Answer all questions using the answer booklet. There will be more available at the venue if needed.
  3. Write legibly. Anything we cannot decipher will be considered incorrect.

Grade Assessment Scheme

Project Report Project Presentation Final Examination
20% 20% 10% 50%
  1. Assignments (20%)
    1. Written assignment
    2. Optional quizzes
  2. Project (30%)
    1. Report (20%)
    2. Presentation (10%)
  3. Final Examination (50%)
  4. Extra Credit (There is no penalty for not doing the extra credit problems. Extra credit will only help you in borderline cases.)

Required Background

  1. Pre-requisites
    1. - CSC 1110 or 1130 or its equivalent. (Not for students who have taken CSC 2520).

Reference Books

Book Sources

  1. Academic & Professional Book Centre, 1H Cheong Ming Bldg., 80-86 Argyle St., Kowloon, 2398-2191, 2391-7430 (fax)
  2. Caves Books (H. K.), 4B Ferry St., G/F., Yaumatei, Kowloon, 2780-0987, 2771-2298
  3. Man Yuen Book Company, 45 Parkes street, Jordan Road, Kowloon, Hong Kong, 2366-0594. Not very large, Asian edition books, fair price, wide range, some 10% discount.
  4. Swindon Book Co. Ltd, 13-15 Lock Road, Tsim Sha Tsiu, Kowloon, 2366-8001. One of the largest book stores in Hong Kong, exchange rate is not favorable.
  5. Hongkong Book Centre, 522-7064. A branch of the Swindon book shop.


  1. Q: What is departmental guideline for plagiarism?
    A: If a student is found plagiarizing, his/her case will be reported to the Department Discipline Committee. If the case is proven after deliberation, the student will automatically fail the course in which he/she committed plagiarism. The definition of plagiarism includes copying of the whole or parts of written assignments, programming exercises, reports, quiz papers, mid-term examinations. The penalty will apply to both the one who copies the work and the one whose work is being copied, unless the latter can prove his/her work has been copied unwittingly. Furthermore, inclusion of others' works or results without citation in assignments and reports is also regarded as plagiarism with similar penalty to the offender. A student caught plagiarizing during tests or examinations will be reported to the Faculty Office and appropriate disciplinary authorities for further action, in addition to failing the course.
  2. Q: What is ACM ICPC?
    A: Association of Computer Machinery International Collegiate Programming Contest. Teams from CUHK have done quite well in the previous years. More information on the CSE's programming team can be found at
  3. Q: What are some of the common mistakes made in online and real-time contest?
    A: There are a few common mistakes. Please check out this site for more information.


Social Networks-Theory Graph Theory

Graph Mining

Link Analysis

Learning to Rank

Recommender Systems

Q & A

Human Computation/Social Games

Opinion Mining/Sentiment Analysis



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