An Efficient Fashion Recommendation System using a Deep CNN Model

The primary goal of the recommender system is to make suggestions for products that are comparable to the given query image. It can be difficult to separate related objects from a vast data set. Systems for online purchasing are looking into how to make product recommendations based on the user's interests. In the past, different statistical techniques and similarity measures were employed to gather comparable items, which resulted in less accurate and precise product recommendations. An efficient Deep CNN model is proposed for classifying the given product. The proposed model is evaluated using fashion products data set, and the results are pleasing. This makes it possible to reliably and precisely recommend the products with an accuracy percentage of 89.02%. The proposed model outperforms other existing models in terms of classification metrics