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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



E-Commerce Recommender System Using Product Data

[Full Text]

 

AUTHOR(S)

Rajesh Kumar E, Kakani Jyotsna, Keerthana Ganta, Ramya Sirisha Nori

 

KEYWORDS

Content-based filtering, Convolutional neural networks, Inverse Document Frequency, One hot encoding, Recommender systems, Term Frequency, Word2Vec

 

ABSTRACT

In previous days, before buying a product, people used to get suggestions from our close friends, family or people known. This is the basic idea of recommender systems. Recommender systems work with the same idea of predicting a product that a customer may like to buy. Recommender systems can be used in different areas to recommend products such as books, apparel, accessories, movies according to the items viewed. A recommender system is a system that helps to expect and recommend similar products to the given input of the product. In many online e-commerce websites like Amazon, Myntra and other sites, one can find sections like “recommended for you”, “products related to this item”, “customers also viewed” when a person views certain product. These are the recommended sections.

 

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