Video Recommendation System Using Machine-Learning Techniques

作     者：Meesala Sravani Ch Vidyadhari S Anjali Devi Meesala Sravani;Ch Vidyadhari;S Anjali Devi

作者机构：Department of Computer Science and EngineeringGMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information TechnologyGokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2024年第31卷第4期

页      面：24-33页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence 

摘      要：In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through *** this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“ratingor“inclinationgiven by the different *** expectation depends on past evaluations,history,interest,IMDB rating,and so *** can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific *** required datasets for the video are taken from *** recommender framework is executed by utilizing Python Programming *** building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the *** that K-implies searches for a steady‘k of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k number of the closest information *** last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.