A Scalable Recipe Recommendation System for Mobile Application

Abstract

There are more and more users to use mobile application to get the cooking recipe information, but to recommend right recipe for each user is challenging for the complex food and diet information. In this paper, a hybrid recommendation system is proposed for personalized recipe mobile application. We describe a scalable recommendation system to process the massive data from mobile applications based on the spark clusters, and design a hybrid recommendation algorithm, which combines content-based filtering and collaborative filtering to improve the recipe recommended effectiveness. Our experiments reveal that the recommendation system has scalable computational capability.