A new preference based model for relevant dimension identification in contextual mobile search

Mobile search is a significant task in information retrieval and when coupled with context awareness technologies they can become key tools for mobile users for Web search applications. Context awareness techniques can increase the usability of mobile search providing personalized and more focussed content. However, Contextualized Mobile Information Retrieval still remains a challenging problem. This problem is to identify contextual dimensions that improve search effectiveness and should therefore be in the user's focus. We propose a context filtering process based on a new Preference Language Model, and a new relevance measurement. The experiments have been performed with over than 6000 contextual dimensions. The results show the potential of our Preference model in limiting the negative effects of contextual information overload by using the relevance measurement. © 2014 Springer International Publishing.