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.
|Title of host publication||Australasian Conference on Information Systems, ACIS 2018|
|Subtitle of host publication||Conference Proceedings|
|Publisher||ACIS Australasian Conference on Information Systems|
|Number of pages||12|
|Publication status||Published - 1 Jan 2018|
|Event||29th Australasian Conference on Information Systems, ACIS 2018 - Sydney, Australia|
Duration: 3 Dec 2018 → 5 Dec 2018
|Conference||29th Australasian Conference on Information Systems, ACIS 2018|
|Period||3/12/18 → 5/12/18|
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- Neural network
- Recommender systems