Open AccessA Recommendation System & Their Performance Metrics using several ML Algorithms
Author(s) -
Vineel Kumar Gattu,
Prasanta Sahoo,
K. Eswaran
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5791.029320
Subject(s) - computer science , recommender system , ranking (information retrieval) , support vector machine , set (abstract data type) , service (business) , product (mathematics) , data mining , information retrieval , sentence , data set , machine learning , artificial intelligence , algorithm , mathematics , geometry , economy , economics , programming language
Recommendation systems are subdivision of Refine Data that request to anticipate ranking or liking a user would give to an item. Recommended systems produce user customized exhortations for product or service. Recommended systems are used in different services like Google Search Engine, YouTube, Gmail and also Product recommendation service on any E-Commerce website. These systems usually depends on content based approach. in this paper, we develop these type recommended systems by using several algorithms like K-Nearest neighbors(KNN), Support-Vector Machine(SVM), Logistic Regression(LR), MultinomialNB(MNB),and Multi-layer Perception(MLP). These will predict nearest categories from the News Category Data, among these categories we will recommend the most common sentence to a user and we analyze the performance metrics. This approach is tested on News Category Data set. This data set having more or less 200k Headlines of News and 41 classes, collected from the Huff post from the year of 2012-2018.