A hybrid model for detecting e-commerce product returns using CNN-LSTM

This paper aims to detect the return of products using deep learning techniques. We work on generating a score for every product sold through the e-commerce firm using the consumer’s feedback and their ratings. An automated system for identifying the worth of every product will help the e-commerce firm to detect the products with negative reviews and low ratings which will drop down the fame of the e-commerce and in turn may increase the number of product returns to the origin. This defames and product returns may reflect in reducing the revenue of the firm. In this work, we developed a novel hybrid classification model combining Recurrent Neural Network (RNN) along with Long Short-Term Memory (LSTM). Hybrid deep learning model that combines LSTM networks and RRN is built and tested on the dataset. The results of the hybrid model are compared against four individual models Naive Bayes, SVM, LSTM and RNN. The model is developed and evaluated in python using keras and tensor flow as backend. The developed hybrid model outperforms with an increased accuracy of 7% to 8% compared to the individual LSTM and CNN models.