Conference Proceeding Article
Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through similarity in social relations. We seek to integrate both types of information, in addition to the adoption information, within a single integrated model. Our proposed method models item content via a topic model, and user communities via an autoencoder model, while bridging a user's community-based preference to her topic-based preference. Experiments on public real-life data showcase the utility of the model, particularly when there is significant compatibility between communities and topics.
topic model, autoencoder, cold-start recommendation, social collaborative filtering, collaborative deep learning
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
CIKM '17: Proceedings of the ACM Conference on Information and Knowledge Management: Singapore, November 6-10
City or Country
NGUYEN, Tiep Trong and LAUW, Hady Wirawan.
Collaborative topic regression with denoising AutoEncoder for content and community co-representation. (2017). CIKM '17: Proceedings of the ACM Conference on Information and Knowledge Management: Singapore, November 6-10. 2231-2234. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3883
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