A Neural Autoregressive Approach to Collaborative Filtering

Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:764-773, 2016.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-zheng16, title = {A Neural Autoregressive Approach to Collaborative Filtering}, author = {Zheng, Yin and Tang, Bangsheng and Ding, Wenkui and Zhou, Hanning}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {764--773}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/zheng16.pdf}, url = {https://proceedings.mlr.press/v48/zheng16.html}, 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.} }
Endnote
%0 Conference Paper %T A Neural Autoregressive Approach to Collaborative Filtering %A Yin Zheng %A Bangsheng Tang %A Wenkui Ding %A Hanning Zhou %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-zheng16 %I PMLR %P 764--773 %U https://proceedings.mlr.press/v48/zheng16.html %V 48 %X 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.
RIS
TY - CPAPER TI - A Neural Autoregressive Approach to Collaborative Filtering AU - Yin Zheng AU - Bangsheng Tang AU - Wenkui Ding AU - Hanning Zhou BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-zheng16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 764 EP - 773 L1 - http://proceedings.mlr.press/v48/zheng16.pdf UR - https://proceedings.mlr.press/v48/zheng16.html AB - 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. ER -
APA
Zheng, Y., Tang, B., Ding, W. & Zhou, H.. (2016). A Neural Autoregressive Approach to Collaborative Filtering. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:764-773 Available from https://proceedings.mlr.press/v48/zheng16.html.

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