Every e-commerce website works with a recommendation system to provide the costumers with best recommendations on what they might be willing to buy. This article covers how Natural Language Processing can be used to recommend products for the costumers. The models are tested for Fashion products. The project is also presented as to complete the course of CO102 at Delhi Technological University. Text Based product similarity First, a few functions are defined for displaying the recommended products in the form of heat maps and plots. three different models are defined for the text based recommendation system. Bag of Words. Term Frequency — Inverse Document Frequency Inverse Document Frequency Text Semantics Based product similarity Two models are applied in this section —Average Word2Vec and Word2Vec based on brand and color.
Conclusion and Future work It is thus observed that for a business perspective, Word2vec model based on brand and colors is more useful to give recommendations on a E-Commerce website. For future, more data can be used with mixed type of products, along with that the images can be used and neural networks can be used to give recommendations based on the images.
Data — https://drive.google.com/drive/folders/1K_BSjfQjZdHhy9qtQdGi032egY8sPJD5?usp=sharing Colab Notebook — https://colab.research.google.com/drive/15fGpnn-Ke2Ip5udQBjSQvlbCxWe7fh9l?usp=sharing