Comparison of Bow and TF – IDF Algorithms i...

Comparison of Bow and TF – IDF Algorithms in an Apparel Recommendation System

Author Name : Abhay Chopde, Arya Joshi

ABSTRACT

The e-commerce business for clothing is expanding quickly because of people's ongoing preference for varied fashion products. According to customer needs and preferences, online retailers must improve their features. Effective recommendation systems are becoming a must for online shops because consumers have too many options to choose from in these online stores, which may make it difficult for them to choose the right outfit, save the user time, and boost sales. In the proposed work, we suggested a system for recommending clothing to consumers depending on their feedback. We made use of a real – world data collection that was obtained from the dominant online retailer Amazon utilising the Product Advertising API. We want to propose products using terms like brand, colour, and size. data exploration to learn more about our dataset in-depth, cleaning up (pre-processing) data to eliminate invalid portions, The task of an apparel recommendation system can be made simpler by model deployment (we have compared various feature extraction approaches, such as bag of words, TF-IDF, and IDF model) and model selection (we have compared various feature extraction techniques, such as bag of words, TF-IDF, and IDF). Response time and content matching are used to determine the model's accuracy.