A Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products

E-Commerce is the activity of electronically purchasing or selling products in an online platform. E-Commerce recommender systems provide suggestions of products based on the consumer sentiment and ratings. There is often a mismatch between consumer rating and their sentiment. Identifying the accuracy of the mismatch is a challenge in machine learning. This research proposes a Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products based on consumer sentiment and product descriptions. This proposed framework combines a text embeddings model, sentiment analysis model and a rating engine. The text embeddings model is implemented using gensim doc2vec for consumer reviews and product descriptions. Further it uses neural networks for capturing the consumer product interactions for collaborative filtering. The sentiment analysis model is implemented by inputting distributed text embeddings into neural networks that are trained to capture content feature of products and sentiment of consumer evaluations. The rating engine is implemented by aggregating several embeddings as attention weights for consumers and products, then outputting the prediction score for the consumer–product interaction. This research makes use of the real-world Amazon product category semi structured baby and digital music semi structured datasets, each of which contains information on consumer reviews and product metadata. Mean absolute error (MAE) and root mean-square error (RMSE) are considered to evaluate the recommendation performance, thereby measuring the accuracy of prediction ratings. Experimental results on Amazon distinct product dataset demonstrate an accuracy metric MAE value of 0.5909 and RMSE value of 0.8080. These results demonstrate that the proposed framework performs better on rating prediction in enhancing consumers experience in order to find their preferences for e-commerce products. Consequently, e-commerce platforms can enhance sales and consumer satisfaction by using machine learning frameworks to recommend which products a consumer will be interested in based on their past purchasing behavior.