Abstract
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.
Highlights
Recommended systems deals with recommending of products or items to a user based on their interest
The main reason we need a recommendation system in the current generation is because humans have extremely many alternatives to utilize required information which is popular from the Internet
In this paper generally we used classification algorithms like Support-Vector Machine, K-Nearest Neighbors, Logistic Regression, Multinomial Naïve Bayes, Multilayer Perceptron to classify Data and we evaluated these algorithms performance metrics to compare which algorithm is given better results
Summary
Recommended systems deals with recommending of products or items to a user based on their interest. The Recommend system solves the information overloading problem [1] It is running based on three phase’s object-data collection, similarity decision and prediction computation on the report of Chen and Anne Yun-An [2]. Supervised learning: In supervised learning the machine is learned from the data which have labels and tag values.
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