1. This work is by Georgia Koutrika, published on CIDR'09 All the figures & tables in these slides are from that paper

2. Outline Motivation CourseRank Unique features Lessons Learnt so Far Interaction with rich data Conclusion

3. Motivation – CourseRank

4. Motivation Social Web Site FaceBook, del.icio.us, Y! Answer, Flickr, MySpace Great success Is it interesting for research community? Are there any interesting challenges to researchers? Can we do more than just poke friends?

5. Motivation Social Web Site V.S. Traditional Open Web V.S. Database Social Web Site - mostly unstructured - Centrally stored - Users-to-Users Access Control Traditional Open Web - Unstructured - highly distributed in storage - Many provider and consumers without access control Database - Structured - Centrally stored - 1 provider, many consumers

6. Motivation Social Web Site V.S. Traditional Open Web V.S. Database

7. Motivation Research topics in database Research topics in Web search What is important for social website What is most effective way for users to interact? What can be shared among the users? What information can be trusted? How users to visualize and interact with information? How users interact with other users? How system evolve over time?

8. CourseRank An educational social site where Stanford students can explore course offerings and plan their academic program Describe the insight of CourseRank in this paper

9. CourseRank What CourseRank can do Search for courses Rank courses Requirement check Feedback to faculties etc.

10. CourseRank Unique features Hybrid system – database + social system Rich data New tools – plannar, requirement checker, CourseCloud, etc. Site Control Closed Community & Restricted Access Constituents

11. Lessons Learnt so Far Meaningful Incentives - Yahoo! Answers: Best answer – 10 points, vote for best answer – 1 point - CourseRank: Different tools: planner, Q&A forum seeds Interaction for Constituents - Department Requirement both useful for staff and students

12. Lessons Learnt so Far Meaningful Incentives - Yahoo! Answers: Best answer – 10 points, vote for best answer – 1 point - CourseRank: Different tools: planner, Q&A forum seeds Interaction for Constituents - Department Requirement both useful for staff and students

13. Lessons Learnt so Far The power of a closed community - Block spammers and malicious users - User are more willing to contribute - Example: group forum, department forum, school forum, public forum It’s the Data, Stupid - External data - Hard to be shared data

14. Lessons Learnt so Far Privacy can be “shared” - The course planned to be taken of a student -> closed community Closed Loop Feedback - Build by stanford students theirself, quickly get feedback Beyond CourseRank: The Corporate Social Site - Example: Inner forum of a company - Can corporate social site learn something from CourseRank?

15. Interaction with Rich Data Rich data A student want to take a course: Course name&description, user’s profile(major, class, grade), course interrelationships, user’s comments, etc. Problem of typical search engines a student want something related to Greece Search “Greece” -> no result Search “Greek, science” -> got the course “history of science” Search engine does not provide user specific result “Java” is a good course, but not fit for non-engineering students

16. Interaction with Rich Data Data Clouds A data cloud is a tag cloud, where the “tags” are the most representative or significant words found in the results of a keyword search over the database. Example: “American” -> “Latin American”, “Indians”, and “politics”. “ American ”: 1160 courses “ Latin American ”: 123 courses Challenge: Multiple relation: tags does not only appear in course name and description. For example, “java”. How to rank the result How to dynamically and efficiently update cloud

17. Interaction with Rich Data Data Clouds

18. Interaction with Rich Data Flexible Recommendation (FlexRecs) Why Provide recommendation is not easy considering multiple connections. It need to be manually adjusted. Previous recommendation algorithm is fixed

19. Interaction with Rich Data Flexible Recommendation Example Relations: Simple reconmmendation example

20. Interaction with Rich Data Flexible Recommendation Example Complicated reconmmendation example : recommend : Expand : Select : Connect

21. Conclusion Social sites: A closed, well defined community Provide rich data Not simply for sharing links and networkings Two mining tools Data clouds FlexRecs

22. Q&A