Utilizing implicit feedback and context to recommend mobile applications from first use
Christoffer Davidsson, Simon Moritz · 2011
Most mobile platforms of today enable the users to install third-party applications through application portals or stores. As the number of applications available increases, the users of mobile devices find it challenging to find new and relevant applications. The fact that these applications usually are browsed and downloaded from a mobile device, which has a smaller screen compared to desktop computers, makes this information overload even more intense. Recommender systems aid users in finding relevant applications. A challenge with such systems is that they traditionally need a user profile in order to produce recommendations, known as the new user problem. In this paper we present a context-aware recommender system for mobile applications which produces recommendations from the first use. This paper introduces context-based recommender concepts and presents a prototype implementation of said concepts.