AB  - Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.
AU  - Liu, W
AU  - Li, L
AU  - Do, DMQ
CY  - Palo Alto, California USA
DA  - 2019
DO  - 10.1609/aaai.v33i01.33019965
EP  - 9966
JO  - 33rd AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
PB  - AAAI Press
PY  - 2019
SP  - 9965
TI  - Cross-Domain Recommendation via Coupled Factorization Machines
VL  - 33
Y1  - 2019
Y2  - 2026/05/08
ER  -