  Collaborative Filtering (CF) is a widely adopted technique in recommender
systems. Traditional CF models mainly focus on predicting a user's preference
to the items in a single domain such as the movie domain or the music domain. A
major challenge for such models is the data sparsity problem, and especially,
CF cannot make accurate predictions for the cold-start users who have no
ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is
proposed for effectively transferring users' rating preference across different
domains, it is still difficult for existing CDCF models to tackle the
cold-start users in the target domain due to the extreme data sparsity. In this
paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for
cold-start users in the target domain. Firstly, in order to better characterize
users in sparse domains, we take the users' similarity relationship on rating
behaviors into consideration and propose the Matrix Factorization by
incorporating User Similarities (MFUS) in which three similarity measures are
proposed. Next, to perform knowledge transfer across domains, we propose a
neighborhood based gradient boosting trees method to learn the cross-domain
user latent feature mapping function. For each cold-start user, we learn
his/her feature mapping function based on the latent feature pairs of those
linked users who have similar rating behaviors with the cold-start user in the
auxiliary domain. And the preference of the cold-start user in the target
domain can be predicted based on the mapping function and his/her latent
features in the auxiliary domain. Experimental results on two real data sets
extracted from Amazon transaction data demonstrate the superiority of our
proposed model against other state-of-the-art methods.
