Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System

Hanafi, Abdul Samad Hasan Basari

2019

Abstract

E-commerce service have become popular way to shopping in recent decade. E-commerce machine requires a method to provide fit product information to customer called recommender system. The most of popular recommender system adopted for many large e-commerce companies named Collaborative filtering (CF) due to obtain relevant, fit and essential product information. Even though CF owned some benefit, it has shortcoming inaccurate recommendation when face minimum rating also popular named sparse data problem. Many researches have been conducted to proposed model how to generate rating prediction aim to handle sparse rating effectively. Most of them exploit latent factor model or matrix factorization (MF) to handle this problem, unfortunately, this problem fails to handle the problem when faced serious sparse data. Aims to improve the serious problem on above, several researchers involve auxiliary information in the form of product document or user demographic information respectively. Several researchers implemented Convolutional Neural Network (CNN) to extract product document review incorporate MF that responsible to produce rating prediction, another model exploited Stack Denoising Auto Encoder (SDAE) model as user demographic information extraction incorporate with MF. In this research, considered implementing dual information representation using deep learning model based on SDAE and Long Shorts Term Memory (LSTM) as product review document representation combined into PMF to generate rating prediction. According to experiment report, the proposed model called SLP (SDAE+LSTM+PMF) successful to obtained effectiveness rating prediction based on RMSE evaluation metrices over some current model based on traditional PMF more than 15% in average and superior over CNN more than 0.9% in average.

Paper Citation

in Harvard Style

Hanafi. and Basari A. (2019). Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System.In Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST, ISBN 978-989-758-453-4, pages 343-351. DOI: 10.5220/0009910603430351

in Bibtex Style

@conference{conrist19,
author={Hanafi and Abdul Samad Hasan Basari},
title={Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System},
booktitle={Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST,},
year={2019},
pages={343-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009910603430351},
isbn={978-989-758-453-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST,
TI - Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System
SN - 978-989-758-453-4
AU - Hanafi.
AU - Basari A.
PY - 2019
SP - 343
EP - 351
DO - 10.5220/0009910603430351