Foodle is an iPhone application that allows users to ask people in a community for restaurant recommendations. The application is based on asking questions: users enter a food-related question to ask the Foodle user community. Once the user enters the question, this question is sent to select users in the Foodle community. The recipients of the question are determined by a machine learning system, which adapts to the user's tastes and sends questions to people that should be best able to answer the question based on the user's food preferences. Recipients of the question receive a push notification on their iPhone.

Once the question is answered, the original user receives a push notification on their iPhone. They can browse through the answers that have been sent to them. If they like a particular restaurant recommendation, they can rate the user's response.

Key Features:

Personalized recommendations. Since the food questions are sent out to real users that specialize in a particular type of food, users receive relevant and personalized food recommendations. Users are asked to fill out basic food profiling information the first time they use Foodle, so that Foodle can learn about the users' food preferences. These questions include food preferences, favorite dishes, and where the user is from. Once a question is sent, the system will send the question to a selection of users who share similar food preferences or share a food preference specific to the type of food being asked.

Leaderboard reward system. Users will receive points if their restaurant recommendation is rated favorably by another user. The aggregate number of points a user has from all of his/her restaurant recommendations is used to determine their rank in the system.

Social interaction in the community. Foodle allows users to send messages to each other and find out about other people in the community through their Foodle and Facebook profiles. Users who ask questions and recipients of the questions can chat with each other and discuss about the restaurant recommendation.

 

Demo