Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Entire DC Network
Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw
Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …