TY  - JOUR
T1  - Effect of Denoising Autoencoder in the view of Item Popularity Bias
AU  - Kim, Jinhong 
AU  - Lee, Jae-woong 
AU  - Lee, Jongwuk 
JO  - Journal of KIISE, JOK
PY  - 2021
DA  - 2021/1/14
DO  - 10.5626/JOK.2021.48.5.575
KW  - denoising autoencoder
KW  - collaborative filtering
KW  - top-N recommendation
KW  - item popularity
AB  - Denoising autoencoder (DAE) is commonly used in recent recommendation systems. It is a type of Autoencoder that trains by giving noise to the input and has shown improved performance compared to autoencoder. In this paper, we analyze the effect of noise in terms of item popularity to interpret the training of DAE. For analysis, we design the experiment in the following two ways. First, we observe the changes of the learned item vector’s L2-norm by giving noise to the autoencoder. Second, by giving noise only to presampled items by popularity, we anlayze whether the improved performance of the DAE is related to item popularity. Results of the experiment showed that the variance of the item vector norm caused by popularity was reduced by noise, and that the accuracy increased when noise was given to the popular items.