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REMAKES.COM

REMAKES.COM. Final Presentation Jordan Marshall Nevil Pozholiparambil Jaebong Son Allen Wang. Agenda. Business Case Competitive Analysis Value Propositions System Architecture Data and Mining Similarity Function Prediction Formula Novelty Team Contribution Future Outlook. Business .

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REMAKES.COM

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  1. REMAKES.COM Final Presentation Jordan MarshallNevil Pozholiparambil Jaebong Son Allen Wang

  2. Agenda • Business Case • Competitive Analysis • Value Propositions • System Architecture • Data and Mining • Similarity Function • Prediction Formula • Novelty • Team Contribution • Future Outlook Business System Data Mining Novelty Team Outlook

  3. Business Case • U.S. film industry revenue reached a record high $9.62 billion in 2007 • Hit movies are often reproduced to appeal to the new generation • Over 60 remake films each year • Fans and critics love to scrutinize the result and quality of the remakes Business System Data Mining Novelty Team Outlook

  4. Competitive Analysis • Remakes.com is a one stop shop for remakes information and can be enjoyed by all • Casual moviegoers interested in basic information • Film buffs dedicated to defending their opinions Higher consumer value Larger movie database Business System Data Mining Novelty Team Outlook

  5. Value Propositions Business System Data Mining Novelty Team Outlook

  6. System Architecture Business Data Mining Novelty Team Outlook System

  7. Data and Mining • General movie information from IMDB dataset • User rating information form Netflix dataset • Use of similarity function and prediction formula to make movie recommendations Business System Data Mining Novelty Team Outlook

  8. Similarity Function • Pearson Correlation Coefficient where ra,i is the rating given to movie i by user a, is the mean rating given by user a. Business System Data Mining Novelty Team Outlook

  9. Prediction Formula where U is an active user, i is an unseen movie. J is a user who rated item i, rUJ is correlation coefficient between user U and J. Business System Data Mining Novelty Team Outlook

  10. Novelty • Dynamic movie comparison features • Head-to-head movie trailers and descriptions • Voting style viewer review system • Use of Google Charts to display customer reviews • Aggregated upcoming movie news • Use of RSS feeds for the newest and anticipated movie titles • New movie locator • Identifying the new film locations with Google Maps • Unique movie recommendation system • Questionnaire style movie type preference • Recommending movie remake pairs to capture Long Tail products Business System Data Mining Team Outlook Novelty

  11. Team Contribution Jordan • Website and APIs integration • Website development • Yahoo Images API Allen • Deliverable administrator • Amazon Associates API • Google Charts API • Explored with Cooliris and forums API Nevil • Database design • Youtube API • Google Maps API • RSS feeds • Ads API Jaebong • Database design • Data processing • Database queries • Data mining Business System Data Mining Novelty Outlook Team

  12. Future Outlook • More expansive database • More remakes pairs • Include international films • Additional rating and comparison functionalities for users • Sponsors and associate programs Business System Data Mining Novelty Team Outlook

  13. Thank You

  14. Basic Concept of CF System Business System Data Mining Novelty Team Outlook

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