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

OutlineMotivationCourseRankUnique featuresLessons Learnt so FarInteraction with rich dataConclusion

Motivation – CourseRank

MotivationSocial Web Site

FaceBook, del.icio.us, Y! Answer, Flickr, MySpaceGreat successIs it interesting for research community?Are there any interesting challenges to researchers?Can we do more than just poke friends?

MotivationSocial 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

MotivationSocial Web Site V.S. Traditional Open Web V.S. Database

MotivationResearch topics in databaseResearch topics in Web searchWhat 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?

CourseRankCourseRank

An educational social site where Stanford students can explore course offerings and plan their academic program

Describe the insight of CourseRank in this paper

CourseRankWhat CourseRank can do

Search for coursesRank coursesRequirement checkFeedback to facultiesetc.

CourseRankUnique features

Hybrid system – database + social systemRich dataNew tools – plannar, requirement checker, CourseCloud,

etc.Site ControlClosed Community & Restricted AccessConstituents

Lessons Learnt so FarLessons Learnt so Far

Meaningful Incentives- Yahoo! Answers:

Best answer – 10 points, vote for best answer – 1 point- CourseRank:

Different tools: planner, Q&A forum seedsInteraction for Constituents

- Department Requirementboth useful for staff and students

Lessons Learnt so FarLessons Learnt so Far

Meaningful Incentives- Yahoo! Answers:

Best answer – 10 points, vote for best answer – 1 point- CourseRank:

Different tools: planner, Q&A forum seedsInteraction for Constituents

- Department Requirementboth useful for staff and students

Lessons Learnt so FarLessons 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

forumIt’s the Data, Stupid

- External data- Hard to be shared data

Lessons Learnt so FarLessons Learnt so Far

Privacy can be “shared”- The course planned to be taken of a student -> closed

communityClosed Loop Feedback

- Build by stanford students theirself, quickly get feedbackBeyond CourseRank: The Corporate Social Site

- Example: Inner forum of a company- Can corporate social site learn something from CourseRank?

Interaction with Rich DataRich 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 enginesa 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

Interaction with Rich DataData 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

Interaction with Rich DataData Clouds

Interaction with Rich DataFlexible Recommendation (FlexRecs)Why

Provide recommendation is not easy considering multiple connections. It need to be manually adjusted.

Previous recommendation algorithm is fixed

Interaction with Rich DataFlexible Recommendation Example

Relations:

Simple reconmmendation example

Interaction with Rich DataFlexible Recommendation Example

Complicated reconmmendation example : recommend : Expand : Select : Connect

ConclusionSocial sites:

A closed, well defined communityProvide rich dataNot simply for sharing links and networkings

Two mining toolsData cloudsFlexRecs

Q&A