Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2017

Abstract

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.

Keywords

topic model, autoencoder, cold-start recommendation, social collaborative filtering, collaborative deep learning

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

CIKM '17: Proceedings of the ACM Conference on Information and Knowledge Management: Singapore, November 6-10

First Page

2231

Last Page

2234

ISBN

9781450349185

Identifier

10.1145/3132847.3133128

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3132847.3133128

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