Towards a Context-Aware Mobile Recommendation Architecture
Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. On the one hand, it is necessary to identify which items are relevant for the user at a particular moment and place. On the other hand, some mechanism would be needed to rank the different alternatives. Recommendation systems, that offer relevant items to the users, have been proposed as a solution to these problems. However, they usually target very specific use cases (e.g., books, movies, music, etc.) and are not designed with mobile users in mind, where the context and the movements of the users may be important factors to consider when deciding which items should be recommended.
In this paper, we present a context-aware mobile recommendation architecture specifically designed to be used in mobile computing environments. The interest of context-aware recommendation systems has been already shown for certain application domains, indicating that they lead to a performance improvement over traditional recommenders. However, only very few studies have provided insights towards the development of a generic architecture that is able to exploit static and dynamic context information in mobile environments. We attempt to make a step in that direction and encourage further research in this area.
Keywordscontext-awareness recommendation systems mobile computing
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