• Turangi Venkata Udayasri
• Dr Y V Ramkumar
• K ChandraSekhar

Nowadays, the internet has been widely recognized as a huge data repository consisting of a variety of data kinds as well as vast quantity of unknown valuable information which may be found by a broad range of data mining or machine learning methods. Although the rise of e-commerce marketplaces leads in the development of search engines, consumers are still confronting a difficulty with accurate results. Instead, to accomplish this issue recommendation engines are primarily beneficial. Most e-commerce sites are creating recommendation systems evaluate a significant quantity of transaction data without having any understanding of what the items in the transactions represent or what they say about the people who bought or browsed things. Apparel fashion recommendation engine that employs deep convolutional neural networks utilizing Amazon API to propose goods and offer clients with information to assist them discover the products. Utilizing deep neural networks enable us to interpret such photos into a high dimensional feature representation that enables us to propose a pair from user preference tensor.

Recommendation systems, e-commerce, CNN, feature extraction, machine learning

"ECOMMERCE WEBSITE PRODUCT RECOMMENDATIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.d368-d373, September-2022, Available :http://www.jetir.org/papers/JETIR2209342.pdf

"ECOMMERCE WEBSITE PRODUCT RECOMMENDATIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppd368-d373, September-2022, Available at : http://www.jetir.org/papers/JETIR2209342.pdf