• S.Ananya
• Vanshika Sandeep
• Dr.G.Paavai Anand
• N.Mahesh

Developing a system that provides a complementary outfit based on user queries can be challenging due to its complexity and subjectivity. At present, the methodologies available are used to recommend outfits to the users according to meta-data and user’s location. So we present an empirical study on the application where we make use of Deep Convolutional Neural Networks (DCNN) to the task of classifying apparel and user’s input from their wardrobe along with Convolutional Auto-encoder which is capable of finding similar product images with the aim to remove the usage of product tags and improve the recommendations of e-commerce platforms. Choosing the right pair of clothes according to the customer’s choice and preference is of utmost importance. Sometimes people find it difficult to keep up with the trend so in such cases this project will be very helpful for them. Also, this helps the user to save their time and energy which implies that the user need not spend much time worrying about “what goes well with what”. Hence we have come up with a system that is capable of resolving such difficulties and whose primary purpose is to make the user’s work easier and beneficial.

outfit matching, fashion recommendation, fashion, convolution neural networks, autoencoder, deep learning.

"Complementary Outfit Recommendation Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1520-1525, April-2020, Available :http://www.jetir.org/papers/JETIR2004407.pdf

"Complementary Outfit Recommendation Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1520-1525, April-2020, Available at : http://www.jetir.org/papers/JETIR2004407.pdf