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Competition

Competition. Business Case. Nutrition based meal shopping No one doing this currently Huge market! Capturing user preferences for food items “Eat This” button Directed search advertising rates Google ad rates, not Facebook rates Targeted ads based on N utrition preferences Location.

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Competition

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Presentation Transcript

  1. Competition

  2. Business Case • Nutrition based meal shopping • No one doing this currently • Huge market! • Capturing user preferences for food items • “Eat This” button • Directed search advertising rates • Google ad rates, not Facebook rates • Targeted ads based on • Nutrition preferences • Location

  3. Novelty • Android Mobile App (iOS too!) • True SoLoMo (Web 3.0) • Created EatRightAPI • Twitter Sentiment • Recommendation Engine • PCI Compliant Credit Card Processing • Cloud Based

  4. Twitter Sentiment • API from TweetSentiments.com • Support Vector Machine from Taiwan National University > 60 Index > 40-60 Index < 40 Index

  5. Recommendation Engine Custom similarity function • Compares user history with food items • 2 criteria for comparison: • Category: Beef, Chicken, Other • Packaging: Sandwich, Salad, Other • Top 3 recommendations for each restaurant with a minimum similarity score

  6. System Architecture

  7. API’s Implemented Google Maps Google Places Google Directions Google Adsense YouTube • Facebook Connect • Facebook Like • Twitter Post • Tweet Sentiments • Stripe • Yelp

  8. Our Team Jim Marquardson Platform Design & Developer Mark Grimes Mobile Application Developer Dave Wilson User Interface Design & APIs Justin Williams Data Scraping & Sentiment

  9. Future Extensions • Food item search • More restaurant menu data • Estimated nutrition information for smaller restaurants • Additional mobile platforms

  10. Appendix - Screenshots

  11. Mobile Screenshots

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