Skewness Ranking Optimization for Personalized Recommendation

Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen, Ming-Feng Tsai
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:400-409, 2020.

Abstract

In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility of the skew normal distribution, based on which three hyperparameters are attached to the optimization criterion. Furthermore, from a theoretical point of view, we not only establish the relation between the maximization of the proposed criterion and the shape parameter in the skew normal distribution, but also provide the analogies and asymptotic analysis of the proposed criterion to maximization of the area under the ROC curve. Experimental results conducted on a range of large-scale real-world datasets show that our model significantly outperforms the state of the art and yields consistently best performance on all tested datasets.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-wang20c, title = {Skewness Ranking Optimization for Personalized Recommendation}, author = {Wang, Chuan-Ju and Chuang, Yu-Neng and Chen, Chih-Ming and Tsai, Ming-Feng}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {400--409}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/wang20c/wang20c.pdf}, url = { http://proceedings.mlr.press/v124/wang20c.html }, abstract = {In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility of the skew normal distribution, based on which three hyperparameters are attached to the optimization criterion. Furthermore, from a theoretical point of view, we not only establish the relation between the maximization of the proposed criterion and the shape parameter in the skew normal distribution, but also provide the analogies and asymptotic analysis of the proposed criterion to maximization of the area under the ROC curve. Experimental results conducted on a range of large-scale real-world datasets show that our model significantly outperforms the state of the art and yields consistently best performance on all tested datasets.} }
Endnote
%0 Conference Paper %T Skewness Ranking Optimization for Personalized Recommendation %A Chuan-Ju Wang %A Yu-Neng Chuang %A Chih-Ming Chen %A Ming-Feng Tsai %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-wang20c %I PMLR %P 400--409 %U http://proceedings.mlr.press/v124/wang20c.html %V 124 %X In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility of the skew normal distribution, based on which three hyperparameters are attached to the optimization criterion. Furthermore, from a theoretical point of view, we not only establish the relation between the maximization of the proposed criterion and the shape parameter in the skew normal distribution, but also provide the analogies and asymptotic analysis of the proposed criterion to maximization of the area under the ROC curve. Experimental results conducted on a range of large-scale real-world datasets show that our model significantly outperforms the state of the art and yields consistently best performance on all tested datasets.
APA
Wang, C., Chuang, Y., Chen, C. & Tsai, M.. (2020). Skewness Ranking Optimization for Personalized Recommendation. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:400-409 Available from http://proceedings.mlr.press/v124/wang20c.html .

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