Paper Info
Title
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.
Year
DOI
Venue
2022
10.1016/j.is.2022.102002
Information Systems
Keywords
DocType
Volume
Collaborative filtering,Matrix factorisation,Codebook,Transfer learning,Cross-domain recommender systems
Journal
107
ISSN
Citations 
PageRank 
0306-4379
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Sowmini Devi Veeramachaneni100.34
Arun K. Pujari242048.20
Vineet Padmanabhan321625.90
Vikas Kumar400.34