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Taste-Analyzing Foodie Apps

Clean the Sky - Positive Eco Trends & Breakthroughs

The Nara App Analyzes Your Tastes to Find Restaurants for You to Try

— July 16, 2013 — Tech
The Nara app is an amazing little piece of mobile software that acts like a personal concierge to help you find your new favorite restaurant. Using your taste history and a network of users, the app works to find restaurants that you may like. Nara analyzes what restaurants you've enjoyed and disliked and compares that with other users. After thoroughly profiling your tastes, the app is able to find restaurants that you are likely to love.

The app begins by asking you for personal details such as what type of food you enjoy and your demographics. After quizzing you, Nara tracks your restaurant history through Foursquare and asks which venues you enjoyed. With your data and the data of many others, Nara is able to accurately predict which restaurants you would like to try.
Trend Themes
1. Personalized Restaurant Recommendations - Opportunity for restaurants to partner with apps like Nara and utilize customers' taste data to make personalized recommendations.
2. Big Data Analysis for Taste Curation - Opportunity to develop more advanced algorithms that can analyze bigger data sets to provide more accurate and diverse restaurant recommendations.
3. Collaborative Filtering-based Apps - Opportunity for app developers to build collaborative filtering-based foodie apps, which recommend restaurants based on users' past dining experiences and taste preferences.
Industry Implications
1. Restaurant Industry - Restaurants can partner with taste-analyzing apps to attract more customers and provide a personalized dining experience.
2. Mobile App Industry - Opportunity for app developers to create and market taste-analyzing foodie apps that provide personalized restaurant recommendations to users.
3. Big Data Analytics Industry - Big data analytics companies can develop algorithms that can utilize vast data sets to curate and make personalized restaurant recommendations for users.
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