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CollegeAdvisor

CollegeAdvisor.com. Li Fan (Brian) Shuo Zeng (Sean) Xinlei Zhao (Ray). May 04, 2009 . Introduction Objective Competitors Design architecture Data mining Team participation Conclusion. Agenda.

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CollegeAdvisor

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  1. CollegeAdvisor.com Li Fan (Brian) Shuo Zeng (Sean) Xinlei Zhao (Ray) May 04, 2009

  2. Introduction Objective Competitors Design architecture Data mining Team participation Conclusion Agenda

  3. 565,039 international students study at graduate schools in the United States in 2006 (Open Doors 2006) No one-stop, content-rich, information sources can help prospective students to make decisions Introduction

  4. Rich information and data sources about diversified characteristics for universities Ranking data for academic majors covering different domains Recommendation and tailored services to international students Objectives

  5. Competitors “Ranking Sites”: provide the ranking and university profiles http://www.usnews.com http://www.petersons.com http: //www.gradschools.com “Recommendation Sites”: provide basic recommendation based on academic attributes http://www.anycollege.com/ http://www.phds.org Competitors

  6. Comparison

  7. Ranking Data 2009 U.S. News & World Report Ranking data from 11 domains (e.g. business, engineering) including 61 majors (e.g. MIS, CS) College Profile Data Admission requirement data (GPA, TOEFL, GRE/GMAT) Profile data (e.g. tuition, number of student, city scale, temperature, transportation) for nearly 200 universities Data Collection

  8. Architecture

  9. Create a vector space of university attributes Ranking Number of graduate students Number of international graduate students International tuition Scale of city/town Living cost Local average income Public transport convenience Average temperature Provide a questionnaire to collect users’ preferences for each of the attributes Recommend the proper universities whose attributes are closed to the users’ preferences Data Mining

  10. Use the Jaccard function to calculate the similarity between universities and users’ preferences where Xiis the vector of the ith university Y is the vector of user preferences Data Mining

  11. No other site provides rich information about the university profile (videos, pictures, news, location) Fill this gap with six APIs Multi-media content No other site offers a robust recommendation systems to students Diversified attributes for selection for users Customized services for users Calendar Novelty

  12. Li Fan Data parsing Data spidering Database design, implementation and administration Xinlei Zhao Business model Web & functions design Data collection & data mining Shuo Zeng Web implementation API implementation (google, Youtube, Flikr) Data mining component implementation Team

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