“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

The increasing availability of large-scale location traces creates unprecedent opportunities
to change the paradigm for knowledge discovery in transportation systems. A particularly …

Achieving fairness over different user groups in recommender systems is an important
problem. The majority of existing works achieve fairness through constrained optimization …

The GPS technology and new forms of urban geography have changed the paradigm for
mobile services. As such, the abundant availability of GPS traces has enabled new ways of …

Provision of personalized recommendations to users requires accurate modeling of their
interests and needs. This work proposes a general framework and specific methodologies …

Collaborative filtering is among the most preferred techniques when implementing
recommender systems. Recently, great interest has turned toward parallel and distributed …

Machine learning over fully distributed data poses an important problem in peer‐to‐peer
applications. In this model, we have one data record at each network node but without the …

Collaborative filtering algorithm is widely used in the recommendation system of e-
commerce website, which is based on the analysis of a large number of users' historical …

Collaborative Filtering (CF) is a powerful technique for generating personalized predictions.
CF systems are typically based on a central storage of user profiles used for generating the …

Collaborative Filtering (CF) technique has been proved to be one of the most successful
techniques in recommender systems in recent years. However, most existing CF based …