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Survey -- Social Systems: Can We Do More Than Just Poke Friends

Survey -- Social Systems: Can We Do More Than Just Poke Friends. This work is by Georgia Koutrika, published on CIDR'09 All the figures & tables in these slides are from that paper. Outline. Motivation CourseRank Unique features Lessons Learnt so Far Interaction with rich data Conclusion.

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Survey -- Social Systems: Can We Do More Than Just Poke Friends

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  1. Survey -- Social Systems: Can We Do More Than Just Poke Friends 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 • 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 • 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 • 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 • 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 • 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 Thank you!

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