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Improving recommendation quality through outlier removal

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Abstract

Rating data collected by recommendation systems contain noise caused by human uncertainty and malicious attacks. Existing outlier removal approaches usually aim at detecting noise inserted into ground-truth ratings. However, in real applications, the ground-truth of the training data are unavailable, or even unimportant for the prediction task. In this paper, we propose an efficient and effective outlier removal algorithm to improve the quality of the training data. The noise is modeled by the mixture of Gaussian distribution, which can approximate any continuous distribution. First, we employ the expectation-maximization algorithm to calculate the low-rank matrices, whose product forms the recovered ratings. Second, we compare the original and recovered ratings to solicit suspected outliers. This process is repeated a number of times, and ratings that are suspected enough times will be treated as outliers. To validate the effectiveness of our algorithm, we compared the prediction quality of four popular recommendation algorithms. Results showed that several measures on the algorithms were improved with the new training data.

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Notes

  1. http://grouplens.org/datasets/movielens/

  2. http://snap.stanford.edu/data/web-Amazon-links.html

  3. https://www.yelp.com/dataset

  4. https://www.librec.net/datasets.html

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Acknowledgements

This work is supported in part by the National Natural Scientific Foundation of China (61976194, 41631179), the Open project of Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province (OBMA202005), the Zhejiang Provincial Natural Science Foundation of China (LY18F030017), the Natural Science Foundation of Sichuan Province (2019YJ0314).

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Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, People’s Republic of China

    Yuan-Yuan Xu & Fan Min

  2. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, People’s Republic of China

    Shen-Ming Gu

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  1. Yuan-Yuan Xu
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  2. Shen-Ming Gu
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  3. Fan Min
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Correspondence to Fan Min.

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Xu, YY., Gu, SM. & Min, F. Improving recommendation quality through outlier removal. Int. J. Mach. Learn. & Cyber. 13, 1819–1832 (2022). https://doi.org/10.1007/s13042-021-01490-7

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