BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis

Recently, reputation and trust analysis, especially using e-commerce product reviews which could be accumulated as large scale of text data, has contributed increasing greatly to marketing economics. However, it is not easy to find out the useful comments timely, or make the appropriate evaluation automatically, due to their sheer volume and time-varying updates. Existing studies usually treat them based on usefulness or sentiment analysis as a single task, but it is easy to ignore the latent features among different data domain and the potential causal relationships of various tasks. In this study, we focus on the multi-task deep learning, to cope with the reputation and trust analysis in e-commerce systems using product reviews. A so-called BiLSTM (Bidirectional LSTM) and VAE (Variational Autoencoder) Enhanced Multi-Task Neural Network (BiLV-MTNN) model is constructed to facilitate the evaluation, usefulness, and objectivity analysis simultaneously in a semi-supervised way. Specifically, A VAE based latent feature extraction mechanism and a BiLSTM based hidden vector generation mechanism are developed, which can contribute to the extraction of latent features and discovery of causal relationships among different tasks respectively. A labeling method is also proposed to quantify the objectivity of text composition based on the sentiment analysis, so as to improve the feature representation and further benefit the classification accuracy for multi-task learning. Compared with three baseline methods, experiments and evaluations using the Amazon data demonstrate the effectiveness and applicability of the proposed model in dealing with multi-task learning with alleviated negative transfer for trust-aware recommendation applications in e-commerce systems.