While the number of connected objects in world Internet Thing (IoT) is increasing, an efficient and intelligent solution to exploit huge amount generated data does not exist. Smart homes powered by IoT devises are able automate monitor every day activities home owners, improve life quality especially for elderly disabled people. In this paper, we propose a machine learning based model order ana...

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...

We present a mixture model based approach for learning individualized behavior models for the Web users. We investigate the use of maximum entropy and Markov mixture models for generating probabilistic behavior models. We first build a global behavior model for the entire population and then personalize this global model for the existing users by assigning each user individual component weights...

Understanding users is an essential task for providing personal Web experience and targeted advertisements. Current commercial or research systems try to understand users from their online behaviors, for example, how they search, read and write on the Web. However, this type of approaches missed a large part of people’s everyday life, or called ‘physical’ behaviors. The physical behaviors inclu...