International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064


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Survey Paper | Computer Science & Engineering | India | Volume 9 Issue 12, December 2020


Product Recommendation System Using Machine Learning Based Collaborative Filtering Technique

Mrunal Gandhi, Tushar Nikam, Akshay Shahane, Mayur Shete, Dr. Kamini C. Nalavade


Abstract: In today's modern epoch of information technology, the idea of efficiently finding one's favourite product in a large dataset of application database, becomes an essential issue to address for the online content providers in order to attract the masses as opposed to their competitors. Recommender systems or recommendation systems, as they are popularly known, are information filtering systems which are usually integrated with several consumer and commercial applications. Such systems act as a bridge between various content facilitators such as social media websites, e-commerce portals, streaming platforms, etc. and the users of these applications, by suggesting them items from the application database which conform to the user preferences and past activities. Such personalized systems play a vital role, especially when the user is unclear of the item to the searched for. These systems are infiltrating every aspect of our lives, in the form of ?Because you watched? header on Netflix, ?People you may know? section on Facebook, ?Customers who bought this also bought' partition on Amazon.


Keywords: Recommender/recommendation systems, Content based technique, Collaborative technique, Demographic technique, User’s preferences, Rating


Edition: Volume 9 Issue 12, December 2020,


Pages: 176 - 180


How to Cite this Article?

Mrunal Gandhi, Tushar Nikam, Akshay Shahane, Mayur Shete, Dr. Kamini C. Nalavade, "Product Recommendation System Using Machine Learning Based Collaborative Filtering Technique", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=SR201127214109, Volume 9 Issue 12, December 2020, 176 - 180

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