Context-aware recommendation using GPU based parallel tensor decomposition

Recommender system plays an important role in many practical applications that help users to deal with information overload and provide personalized recommendations to them. The context in which a choice is made has been recognized as an important factor for recommendation systems. Recently, researchers extend the classical matrix factorization and allows for a generic integration of contextual information by modeling the data as a tensor. However, current tensor factorization methods suffer from the limitation that the computing cost can be very high in practice. In this paper, we propose GALS, a GPU based parallel tensor factorization algorithm, to accelerate the tensor factorization on large data sets to support the efficient context-aware recommendation. Experiments show that the proposed method can achieve 10 times faster than the current tensor factorization methods. © Springer International Publishing Switzerland 2014.