Cross-Domain Recommendation via Coupled Factorization Machines

AAAI Press
Publication Type:
Conference Proceeding
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019, 33 (1), pp. 9965 - 9966
Issue Date:
Filename Description Size
Cross-Domain Recommendation via Coupled Factorization Machines.pdfPublished version1.08 MB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: