Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization

Abstract

E-commerce is essential application in world wide. In everyday live, we cannot escape from e-commerce transaction. E-commerce requires intelligent machine to deliver product information to customer. Intelligent machine popular called recommender system developed by matrix factorization. Rating is representation of customer expression for satisfied product or service. Unfortunately, number of ratings is too sparse due to majority customer lazy to give rating for e-commerce product. Number of sparse rating matrix have impact in matrix factorization in rating prediction. Moreover, extreme sparse rating has impact degrade performance significantly. Many researchers consider to enhance matrix factorization using customer and product information such as customer demographics information, customer testimony, product review, product description, and etc. In this research, we consider to incorporating Stack Denoising Auto Encoder (SDAE), attention mechanism aims to enhance product review document understanding representation and matrix factorization based on Probabilistic Matric Factorization (PMF) to produce rating prediction. According to experiment report, our model superior over previous work based on hybrid model between PMF, Convolutional Neural Network (CNN) and SDAE that popular called PHD in 1%, and superior over hybrid model between PMF, Long Short Term Memory (LSTM) and SDAE that popular called DDL-PMF in 0.9%, and achieved significantly in 8% over traditional PMF based on RMSE evaluation metrics