PRESENTATION

SOCIAL SYSTEMS: CAN WE DO MORE THAN JUST POKE FRIENDS?

Jack Cheng Ka HoThe Chinese University of Hong

Kong

SEEM 5010ADVANCED DATABASE AND INFORMATION SYSTEM

List of Content Motivation CourseRank

Unique Features Lessons Learnt so Far

Interaction with Rich Data Data Clouds Flexible Recommendations

Conclusion

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?

Motivation (Con’t)

Social Site vs “Traditional” open Web

vs

“Traditional” Database

applicationsDate

Centrally Stored;User Contributed;

Mostly Unstructured;Extra Large

Uncontrolled;Many Providers;Unstructured;Humongous;

Centrally Controlled;“Official”;

Structured;Very Large;

Access

Users to Users Many Providers to Mass Consumers

1 Provider to Many Consumers

Users

Authorized;Fake and Multiple ids;

Shared but diverse interest;

Anyone;Anonymous;

Diverse Interests;

Authorized;Real ids;

Very focused interests;

Research

Little Research;Home-made Solution;

Index and Search;Little db technology;

Long-time established;

ACID database;

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?CourseRa

nk

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

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.

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

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

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

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

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

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

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?

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

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?

Conclusion Social Sites

Well-defined Community User more willing to contribute => Rich

Data Rich Data => Social Interaction Tools

2 tools Data Clouds FlexRecsSocial site can provide valuable services based on user contributed information and present interesting

information management and interaction challenges.