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Title: Nonparametric bayesian multitask collaborative filtering
Authors: Chatzis, Sotirios P. 
Keywords: Collaborative filtering
Indian buffet process
Multitask learning
Issue Date: 2013
Publisher: Association for Computing Machinery
Source: CIKM'13 : proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 2013, Pages 2149-2158
Abstract: The dramatic rates new digital content becomes available has brought collaborative filtering systems to the epicenter of computer science research in the last decade. One of the greatest challenges collaborative filtering systems are confronted with is the data sparsity problem: users typically rate only very few items; thus, availability of historical data is not adequate to effectively perform prediction. To alleviate these issues, in this paper we propose a novel multitask collaborative filtering approach. Our approach is based on a coupled latent factor model of the users rating functions, which allows for coming up with an agile information sharing mechanism that extracts much richer task-correlation information compared to existing approaches. Formulation of our method is based on concepts from the field of Bayesian nonparametrics, specifically Indian Buffet Process priors, which allow for data-driven determination of the optimal number of underlying latent features (item characteristics and user traits) assumed in the context of the model. We experiment on several real-world datasets, demonstrating both the efficacy of our method, and its superiority over existing approaches.
Description: Paper presented at 22nd ACM International Conference on Information & Knowledge Management, 2013, San Francisco, CA, USA, 27 October - 1 November
ISSN: 978-1-4503-2263-8
DOI: 10.1145/2505515.2505517
Rights: © ACM
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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