An Efficient Approach of Product Recommendation System using NLP Technique

As we are moving toward an age of digital globalization and online shopping, there is an increasing need for an efficient and reliable system that can help the consumers and the visitors to find their suitable products. Currently, various websites display the searched product when a visitor comes to their website. What we need is a system, which can recommend the products which are like the searched products. This will help the consumer to find out another product in case the item is unavailable, or the searched product is not good enough, or when they would like to look through different similar products. A good recommendation system has been found out to be financially beneficial for the companies also. It is found out that consumer is 35% more likely to buy a product if the recommendation is good enough for consumers. This proposed approach to the problem of the product recommendation system is to make use of the Amazon Apparel database, which contains data of 180,000 apparels. We are going to use NLP technologies and CNN to help in predicting similar products. Title of the product is used as a major attribute during NLP analysis and recommendation. CNN used at last to create a feature vector from product images, and use this vector combined with all the other vectors, for prediction. We compare the distance between vectors of all products and recommend the products with least distance. VGG-16 architecture used to extract the features from the images. © 2021