Product recommendation using enhanced convolutional neural network for e-commerce platform.

In the present scenario, the product recommendation system is important to boost sales on many e-commerce websites. The majority of the existing product recommendation systems completely rely on the customer's website browsing history or historical transaction records to precisely predict the user's preferences for online product recommendations that are constrained by the limited information available on the e-commerce websites. Usually, processing the customer's feedback is a difficult procedure, where it is tough in interpreting and analyzing the text information. Therefore, an effective deep learning-based recommendation framework is implemented in this research manuscript. After acquiring text information from the amazon product reviews database, the data pre-processing is carried-out by using stemming, lemmatization, and stop word removal techniques. The undertaken pre-processing techniques remove both inconsistent and duplicate data that eases the process of data interpretation and use. Further, the computational time of the model is decreased by removing inconsistent and duplicate data from the original dataset. Then, the feature values are extracted from the pre-processed text data using the term frequency-inverse document frequency (TF-IDF) technique. The extracted feature values are fed to the enhanced convolutional-neural-networks (CNNs) for sentiment analysis. The enhanced CNN model uses Glove as a pre-trained model with a skip-gram in the word-embedding layer for identifying the relevant product ratings based on the user's experience to further enhance product sentiment analysis. The embedding layer in the enhanced CNN-model effectively converts every word into fixed feature vectors that helps in better word representation along with reduced dimensions. The enhanced CNN model obtained a mean recall of 94.80%, the precision of 93.64%, and accuracy of 96.92% on the amazon product reviews database and it is superior to the traditional CNN. [ABSTRACT FROM AUTHOR]