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

• URL: http://arxiv.org/abs/2108.01473v1
• Date: Mon, 2 Aug 2021 17:17:36 GMT
• Title: A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
• Authors: Sowmini Devi Veeramachaneni, Arun K Pujari, Vineet Padmanabhan and Vikas Kumar
• Abstract summary: 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)
• Score: 4.117391333767584
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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