A statistical recommendation model of mobile services based on contextual evidences

Mobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user's actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies. In the last few years, mobile devices are undergoing great advances in terms of capacity, connectivity and adaptability, thereby allowing providers to supply numerous services or personalized applications to the final user and even making it possible for the user to be the provider and to foster these services (Ciudad, XXXX). This great quantity of available services creates problems for the user when trying to find and select those services needed in a specific situation. A partial solution to this problem is the recommendation of services based on previously established preferences in the user's system. Based on this approach, the catalogued services are linked to specific preferences. An evolution of these methods allows establishing preferential services that have been recommended by other users (Tveit, 2001) or providing services associated to a specific location However, these approaches do not take into account the diversity of user context variables, including climate, location, day of the week, user availability, current activity, etc. allows recommending services based on different context variables that could affect the user's decision making (for example, a bicycle rental service would not be offered when raining). In this way, we take into account the variations of the user context in order to …