Authors - Raghav Modi, Ronakkumar Patel Abstract - People have gained greater shopping interest as their living standards have risen, owing to an increase in demand for fashion products. Fashion e-commerce has risen at a rate of 12.2 percent in the last year as a result of this demand, users often have trouble identifying desired items. The fashion product depends not only rating but also on other features. The season and style are two important features while recommending the fashion products. This research aims to provide recommendations to a wide range of users through collaborative filtering based system by incorporating season and style features of fashion products. The Deep learning based technique, convolutional neural network, is used to extract season and style features of fashion product. The dataset used to validate the proposed approach is the amazon product dataset for clothing, shoes, and jewellery. The results are compared using standard evaluation measures like MAE, MSE, RMSE.
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