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Social Systems: Can We Do More Than Just Poke Friends?

Social Systems: Can We Do More Than Just Poke Friends?. Georgia Koutrika , Benjamin Bercovitz , Robert Ikeda, Filip Kaliszan , Henry Liou , Zahra Mohammadi Zadeh and Hector GarciaMolina . CIDR 2009. Presented by Rupa Tiwari CSci8735, Spring 2011. Outline. Introduction Paper Merits

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Social Systems: Can We Do More Than Just Poke Friends?

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  1. Social Systems: Can We Do More Than Just Poke Friends? Georgia Koutrika, Benjamin Bercovitz, Robert Ikeda, FilipKaliszan, Henry Liou, Zahra MohammadiZadeh and Hector GarciaMolina. CIDR 2009 Presented by RupaTiwari CSci8735, Spring 2011

  2. Outline • Introduction • Paper Merits • Problem Importance • Paper Drawbacks • Possible Research Directions • Other Relevant Papers • Conclusion • References

  3. introduction • Vision paper on research challenges of social sites. • Based on “CourseRank” CourseRank Screen Shots: course description (left), course planner (right)

  4. Paper Merits • Social systems vs Classical systems • Point of concerns in social systems • 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? • CourseRank system description & and lessons learnt

  5. CourseRank • An educational social site where Stanford students can explore course offerings and plan their academic program • It can • Search for courses • Rank courses • Requirement check • Feedback to faculties, etc • Unique Features • Hybrid system • Rich data • New tools • Site Control • Closed Community • Restricted Access • Constituents Components of CourseRank system

  6. Courserank - Lessons Learnt • 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 • Useful for staff and students • 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 • Privacy can be “shared” • The course planned to be taken of a student -> closed community • Closed Loop Feedback • Build by Stanford students themselves, quickly get feedback

  7. Problem importance • Beyond CourseRank: The Corporate Social Site • Example: Inner forum of a company • Can corporate social site learn something from CourseRank? • Rich Data Interaction: Proposed Mining Tools • Data Cloud • Flex Recs • Applicable to Various Social Sites

  8. Rich Data Interaction • 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

  9. Rich Data Interaction … contd • Data Clouds A tag cloud, where the “tags” are the most representative or significant words found in the results of a keyword search over the database.

  10. Rich Data Interaction … contd • Flexible Recommendation (FlexRecs) • Example • Relations: • Simple recommendation example

  11. Rich Data Interaction … contd • Allows flexible recommendations to be easily defined, customized, and processed. • Recommend operator, takes a set of tuples as inputs and ranks them by comparing them to another set of tuples. • Challenges: • Handling & implementing full suite of FlexRecs operators • Appropriate interface for allowing users to control recommendations?

  12. Paper drawback Too much focus on CourseRank resulted in loss of generality

  13. research directions • Decentralized Social Networking Services • Content Indexing • Novel Algorithms Ranked Retrieval • Novel algorithms for efficient distributed content retrieval • Personal Networks Creation [“eXO: Decentralized Autonomous Scalable Social Networking”] • Declarative Data-Driven Coordination (D3C) • Devising Coordination Abstraction • Achieving Efficient Coordination • Integrating Coordination into Transactions • Balancing Coordination and Privacy [“Beyond Isolation: Research Opportunities in Declarative Data-Driven Coordination”]

  14. Conclusion • Social Sites: • Not simply for resource sharing and networking • Of great help in day-to-day activities of any community like an university or a corporation • Research on Social Sites: • Focused on usage understanding & system evolution so far • More effort on information management & interaction aspects should be made.

  15. References • “CourseRank: A Social System for Course Planning” by Benjamin Bercovitz, FilipKaliszan, Georgia Koutrika, Henry Liou, Zahra MohammadiZadeh, and Hector Garcia-Molina • “eXO: Decentralized Autonomous Scalable Social Networking” by Andreas Loupasakis, Nikos Ntarmos, and Peter Triantafillou • “Beyond Isolation: Research Opportunities in Declarative Data-Driven Coordination” by LucjaKot, Nitin Gupta, Sudip Roy, Johannes Gehrke, and Christoph Koch

  16. Q&A Thank You!

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