2016 ANeuralAutoregressiveApproachto

• (Zheng et al., 2016) ⇒ Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. (2016). “A Neural Autoregressive Approach to Collaborative Filtering.” In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48.

Subject Headings: CF-NADE, Deep Neural Network-based Collaborative Filtering Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+A+Neural+Autoregressive+Approach+to+Collaborative+Filtering
• http://dl.acm.org/citation.cfm?id=3045390.3045472&preflayout=flat#citedby

Quotes

Abstract

This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored version of CF-NADE is also proposed for better scalability. Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance. Finally, CF-NADE can be extended to a deep model, with only moderately increased computational complexity. Experimental results show that CF-NADE with a single hidden layer beats all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more hidden layers can further improve the performance.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 ANeuralAutoregressiveApproachto	Yin Zheng Bangsheng Tang Wenkui Ding Hanning Zhou			A Neural Autoregressive Approach to Collaborative Filtering