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Research Paper | Computer Engineering | India | Volume 10 Issue 8, August 2021

Multi - Category & Multi - Criteria Recommendation System using Collaborative Based Filtering

Shubham Gaur | Rishabh Naulakha | Khushal Hanswal | Meenakshi Sharma ^[3] | Abhishek Mohanty

Abstract: Recommendations Systems have become one of the most popular application of data science today. It predicts or offers products to customers based on their past browsing history or purchases. Although a lot of effort, research and time has been spent on recommendation engines, we are yet to truly unlock their potential. At the core, a recommender system employs a machine learning algorithm whose job is to predict user's ratings for a particular entity. Through this project, we are employing a multi category recommendation system which will give the user recommendations across different categories based on the user data of multiple categories consisting of different attributes. The concept of K - Nearest Neighbor Algorithm is implemented to derive the similarity of unknown entities or users based on past ratings of a particular entity. The implementation is carried out using JavaScript in Node, thereby extending the capabilities of Collaborative based filtering Algorithm to multiple categories.

Keywords: Multi Criteria Recommender System (MCRS), K - Nearest Neighbor Algorithm (KNN), Similarity Score, Collaborative - based filtering algorithm

Edition: Volume 10 Issue 8, August 2021,

Pages: 1076 - 1080

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