  Recommender systems(RS), especially collaborative filtering(CF) based RS, has
been playing an important role in many e-commerce applications. As the
information being searched over the internet is rapidly increasing, users often
face the difficulty of finding items of his/her own interest and RS often
provides help in such tasks. Recent studies show that, as the item space
increases, and the number of items rated by the users become very less, issues
like sparsity arise. To mitigate the sparsity problem, transfer learning
techniques are being used wherein the data from dense domain(source) is
considered in order to predict the missing entries in the sparse
domain(target). In this paper, we propose a transfer learning approach for
cross-domain recommendation when both domains have no overlap of users and
items. In our approach the transferring of knowledge from source to target
domain is done in a novel way. We make use of co-clustering technique to obtain
the codebook (cluster-level rating pattern) of source domain. By making use of
hinge loss function we transfer the learnt codebook of the source domain to
target. The use of hinge loss as a loss function is novel and has not been
tried before in transfer learning. We demonstrate that our technique improves
the approximation of the target matrix on benchmark datasets.
