The aim of the thesis is the develop a framework for context-aware recipe recommendation systems. In order to address user-specific recommendations, the system is using three different information sources. First, it analyzes ratings from the community that is using the application (that is developed as a part of the thesis). Second, it extracts the current context of the user by using measurements of the user’s smartphone sensors. The context information includes location, season and activity. Finally, the system uses the data of an activity tracker information to draw inferences about the current mood of the user, which is also part of the current context.

 

In this thesis, a flexible and extensible framework for maintaining supermarket content and recommending products to supermarket customers, has been proposed. The system is composed of three distinct subsystems namely: Client Applications, Supermarket Content Management System (SCMS) and a Product Recommendation Engine (PRE).