Deep Autoencoder for Recommender Systems: Parameter Influence Analysis

TL;DR

This paper introduces FlexEncoder, a flexible deep autoencoder model for recommender systems, analyzing how parameter choices influence prediction accuracy through extensive experiments on MovieLens datasets.

Contribution

The work presents a configurable DAE model and systematic analysis of parameter effects, aiding optimal tuning for recommendation accuracy.

Findings

DAE parameters significantly impact prediction accuracy.

Parameter influence is transferable across similar datasets.

Open-source code facilitates understanding and tuning of DAE models.

Abstract

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 typically multilayer perceptron and deep Autoencoder (DAE), among which DAE usually shows better performance due to its superior capability to reconstruct the inputs. However, we found existing DAE recommendation systems that have similar implementations on similar datasets result in vastly different parameter settings. 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. This will help us identify the best-performance parameters given a dataset. Extensive evaluation on the MovieLens datasets are conducted, which drives our conclusions on the influences of DAE parameters. Specifically, we find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect is transferable to similar datasets in a larger size. We open our code to public which could benefit both new users for DAE -- they can quickly understand how DAE works for recommendation systems, and experienced DAE users -- it easier for them to tune the parameters on different datasets.