2016 CollaborativeDenoisingAutoEncod

• (Wu et al., 2016) ⇒ Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. (2016). “Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.” In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-3716-8 doi:10.1145/2835776.2835837

Subject Headings: Deep Learning-based Recommendation Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Collaborative+Denoising+Auto-Encoders+for+Top-N+Recommender+Systems
• http://dl.acm.org/citation.cfm?id=2835776.2835837&preflayout=flat#citedby

Quotes

Abstract

Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. We demonstrate that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components. Thorough experiments are conducted to understand the performance of CDAE under various component settings. Furthermore, experimental results on several public datasets demonstrate that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 CollaborativeDenoisingAutoEncod	Martin Ester Alice X. Zheng Christopher DuBois Yao Wu			Collaborative Denoising Auto-Encoders for Top-N Recommender Systems				10.1145/2835776.2835837		2016