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  • Deep Autoencoder for Recommender Systems: Parameter Influence Analysis

    Dai Hoang Tran Zawar Hussain Wei Emma Zhang Nguyen Lu Dang Khoa Nguyen H. Tran Quan Z. Sheng

    Chapter from the book: Australasian Conference on Information Systems, . 2018. Australasian Conference on Information Systems 2018.

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    Recommender systems have recently attracted many researchers in the deep learning community. The state-of-the-art deep neural network models used in recommender systems are multilayer perceptron and deep autoencoder (DAE). In this work, we focus on DAE model due to its superior capability to reconstruct the inputs, which works well for recommender systems. Existing works have similar implementations of DAE but the parameter settings are vastly different for similar datasets. In this work, we have built a flexible DAE model, named FlexEncoder that uses configurable parameters and unique features to analyse the parameter influences on the prediction accuracy of recommender systems. Extensive evaluation on the MovieLens datasets are conducted, which drives our conclusions on the influences of DAE parameters. We find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect remains valid for similar datasets in a larger.

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    How to cite this chapter
    Tran, D et al. 2018. Deep Autoencoder for Recommender Systems: Parameter Influence Analysis. In: Australasian Conference on Information Systems, (ed.), Australasian Conference on Information Systems 2018. Sydney: University of Technology Sydney ePress. DOI: https://doi.org/10.5130/acis2018.aj
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    Additional Information

    Published on Jan. 1, 2018

    DOI
    https://doi.org/10.5130/acis2018.aj


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