A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data

Abstract

Recommender systems, especially collaborative filtering (CF) based recommender systems, 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 recommender systems 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 novel transfer learning approach called Transfer of Codebook via Hinge loss or TCH for cross-domain recommendation when both domains have no overlap of users and items. In our approach constructing the codebook and transferring the same knowledge from source to target domain is done in a novel way. We employ a similar formulation 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.