Abstract
This thesis introduces a new platform for ordering food at catering outlets. It introduces an innovative new personalized recommendation system for easy ordering. State-of-the-art algorithms like ItemRank and Tangent along with an improved algorithm called Personal Popularity Tendency Matching will be introduced. By leveraging on the algorithms, an improved algorithm, SWIPE, is introduced for food ordering via a mobile device by using a Tinder-like swipe function. Explicit feedback from users using the swipe function is gathered and used to update their precomputed recommendation score for a more accurate recommendation in the future. The mobile food ordering platform along with the proposed algorithm has potential commercial value and is also explored in this thesis.