Comparing contextual and non-contextual features in ANNs for movie rating prediction

Contextual recommendation goes beyond traditional models by incorporating additional information. Context aware recommender systems (CARs) correspond to not only the user’s preference profile but also consider the given situation and context. However, the selection and incorporation of optimal contextual features in context aware recommender systems is always challenging. In this paper we evaluate different representations (feature sets) from the given dataset (LDOS-CoMoDa) for contextual recommendations, in particular looking into movie rating prediction as a subproblem of recommendation. We further crosscompare these representations to select useful and relevant features and their combination. We also compare the performance of standard matrix factorization to Artificial Neural Networks (ANNs) in CARs. Our evaluation shows that dynamic, contextual features are dominant compared to non-contextual ones for the given task in the given data set. We also show that ANNs slightly outperform matrix factorization approaches typically used in CARs.