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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAustralasian Conference on Information Systems, ACIS 2018
Subtitle of host publicationConference Proceedings
PublisherACIS Australasian Conference on Information Systems
Number of pages12
Publication statusPublished - 1 Jan 2018
Event29th Australasian Conference on Information Systems, ACIS 2018 - Sydney, Australia
Duration: 3 Dec 20185 Dec 2018


Conference29th Australasian Conference on Information Systems, ACIS 2018

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Autoencoder
  • Neural network
  • Recommender systems

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