Presentation Social Systems: Can we do more than just poke friends?

1. Seem 5010Advanced Database and Information System PresentationSocial Systems: Can we do more than just poke friends? Jack Cheng Ka Ho The Chinese University of Hong Kong

2. List of Content • Motivation • CourseRank • Unique Features • Lessons Learnt so Far • Interaction with Rich Data • Data Clouds • Flexible Recommendations • Conclusion

3. Motivation • Social web sites • FaceBook, MySpace, Y! Answers and Flickr • Shared resources • Photos, Personal Information, Evaluations, Answers to Questions and else • Thinking: • Have they attracted equal attention from the research community? • Are there any new or interesting challenges to researchers? Can we do more than just poke friends?

4. Motivation (Con’t)

5. Motivation (Con’t) • Some Important Questions for Social Systems • What are the most effective ways for user to interact? • What can be shared among the users in a community? Is it sensitive information? • What information can be trusted? How to build into or studied in a social site? • What are the best ways for users to visualize and interact with information? • How and what kind of resources can interact among users? • How do the systems grow over time? Will it affect the user experience? CourseRank

6. CourseRank • Educational Social Site • For Stanford students can explore course offerings and plan academic program • Provides an ideal platform for conducting hand-on research on social systems • Helps to experiment with different algorithms and interface and “out of the box” thinking easily • Live System without competition

7. CourseRank (Con’t) • What a Stanford social site can do … • For students: • Search for courses of interest • Rank the accuracy of others’ comments • Get personalized recommendations • Shop for classes • Organize classes into quarterly or 4-year schedule • Check fulfill the requirements • Feedback tool for faculty and administrators • For Faculties: • Modify/Add comments to courses • Check the class compare to others • Little over a year, 9000 out of about 14000 Stanford students are using it.

8. CourseRank – Unique Features • Hybrid System • Database application + Social System • Rich Data • New Tools • Planner, Requirement Tracker, CourseCloud and FlexRecs • Site Control • Centrally stored • Closed Community • Stanford Community • Constituents • Students, Faculty and Staff • Restricted Access • Stanford Network

9. CourseRank – Lessons Learnt so Far • Learnt from building and running CourseRank … • Meaningful Incentives – • Critical for Visit and Share Resources • Example-Yahoo! Answers (Scoring Scheme) • Best Answers (10 points), Login each day (1 point), Vote to become the Best Answer (1 point) … • Boosting Reputation • CourseRank • Different Tools – Course Planner, Requirement Tracker and else • Motivate to input Accurate data

10. CourseRank – Lessons Learnt so Far • Interaction for Constituents • Offer the specialized and customized features for each • Motivate each to use • The Power of a Close Community • Known Identities • Willing to Contribute • No Spammers and Malicious users • Trust CourseRank

11. CourseRank – Lessons Learnt so Far • It’s the Data, Stupid • Official & External Data + User Input Data • HARD => Getting Permissions and the Right • Economic and Privacy • Carefully Negotiated with Owners • Privacy can be “shared” • Unconcerned about Privacy • Closed Community • Like to Visit others’s Pages on Facebook or MySpace

12. CourseRank – Lessons Learnt so Far • Closed Loop Feedback • CourseRank is better than Others built by outside contractor • Reason: Developers is Stanford Students • Familiar with the application • Feedback loop with customers • Beyond CourseRank: The Corporate Social Site • Interact and Share Experiences and Resourses • Some companies are tracking the progress

13. Interaction with Rich Data • CourseRank is an excellent testbed with Rich Data • Study Social System & Identify the features • Challenges: • Search Engines • Important Keywords should be known • “Can we make unexpected connections?” • Recommendation Engines • Popular Items • “Can we take into account the student’s personal interests and grade history to recommend appropriate courses?” Data Clouds & Flexible Recommendations

14. Interaction with Rich Data - Data Clouds • Data Cloud = Tag Cloud (Hyperlink) • Tags are the most representative and significant words after keyword search over the database • Summarize Search Results • Help refine the Searches • Questions: • How do they effectively define and search over search entities that span multiple relations? • How do they rank search entities depending on the position of a query term? • How can they dynamically and efficiently compute the data cloud?

15. Interaction with Rich Data - Flexible Recommendations • Typical recommendation system • Limitations: • Hard to modify the algorithm • Hard to experiment • FlexRecs • Easily Defined, Customized and Processed • Special recommend operator • Input a set of tuples and rank them by comparing them to others

16. Interaction with Rich Data - Flexible Recommendations • The relations: • A related course workflow: • Challenges: • How can we optimize the execution of workflows? • What is an appropriate interface for allowing users to control recommendations?

17. Conclusion • Social Sites • Well-defined Community • User more willing to contribute => Rich Data • Rich Data => Social Interaction Tools • 2 tools • Data Clouds • FlexRecs Social site can provide valuable services based on user contributed information and present interesting information management and interaction challenges.