2016 ConvolutionalMatrixFactorizatio

• (Kim et al., 2016) ⇒ Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. (2016). “Convolutional Matrix Factorization for Document Context-Aware Recommendation.” In: Proceedings of the 10th ACM Conference on Recommender Systems. ISBN:978-1-4503-4035-9 doi:10.1145/2959100.2959165

Subject Headings: Convolutional Matrix Factorization (ConvMF), Text Content Support Recommendation Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Convolutional+Matrix+Factorization+for+Document+Context-Aware+Recommendation
• http://dl.acm.org/citation.cfm?id=2959100.2959165&preflayout=flat#citedby

Quotes

Abstract

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly outperforms the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 ConvolutionalMatrixFactorizatio	Jinoh Oh Hwanjo Yu Donghyun Kim Chanyoung Park Sungyoung Lee			Convolutional Matrix Factorization for Document Context-Aware Recommendation				10.1145/2959100.2959165		2016