A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data

TL;DR

This paper introduces a novel transfer learning method for cross-domain recommendation that uses hinge loss to transfer codebooks between non-overlapping domains, improving recommendation accuracy in sparse data scenarios.

Contribution

It proposes a new hinge-loss based transfer learning approach utilizing co-clustering to transfer knowledge between non-overlapping domains in recommender systems.

Findings

Improves matrix approximation on benchmark datasets.

Utilizes hinge loss for effective transfer learning.

Addresses data sparsity in cross-domain recommendations.

Abstract

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.