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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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Presentation Transcript
Remakes com l.jpg

REMAKES.COM

Final Presentation

Jordan MarshallNevil Pozholiparambil

Jaebong Son

Allen Wang


Agenda l.jpg
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


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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


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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


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Value Propositions

Business

System

Data Mining

Novelty

Team

Outlook


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System Architecture

Business

Data Mining

Novelty

Team

Outlook

System


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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


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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


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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


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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


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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


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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



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Basic Concept of CF System

Business

System

Data Mining

Novelty

Team

Outlook


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