  Recommender systems based on collaborative filtering play a vital role in
many E-commerce applications as they guide the user in finding their items of
interest based on the user's past transactions and feedback of other similar
customers. Data Sparsity is one of the major drawbacks with collaborative
filtering technique arising due to the less number of transactions and feedback
data. In order to reduce the sparsity problem, techniques called transfer
learning/cross-domain recommendation has emerged. In transfer learning methods,
the data from other dense domain(s) (source) is considered in order to predict
the missing ratings in the sparse domain (target). In this paper, we come up
with a novel transfer learning approach for cross-domain recommendation,
wherein the cluster-level rating pattern(codebook) of the source domain is
obtained via a co-clustering technique. Thereafter we apply the Maximum Margin
Matrix factorization (MMMF) technique on the codebook in order to learn the
user and item latent features of codebook. Prediction of the target rating
matrix is achieved by introducing these latent features in a novel way into the
optimisation function. In the experiments we demonstrate that our model
improves the prediction accuracy of the target matrix on benchmark datasets.
