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

Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data