Towards source-aligned variational models for cross-domain recommendation

• Main Authors: SALAH, Aghiles, TRAN, Thanh-Binh, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Collaborative Filtering; Cross-Domain Recommendation; Neural Networks; Variational Autoencoder; Databases and Information Systems; Data Science
• Online Access: https://ink.library.smu.edu.sg/sis_research/6430; https://ink.library.smu.edu.sg/context/sis_research/article/7433/viewcontent/recsys21.pdf
• Institution: Singapore Management University

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 thi...