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Video Recommendation System Using Machine-Learning Techniques

Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 24-33页

作者： Meesala Sravani Ch Vidyadhari S Anjali Devi Department of Computer Science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information Technology Gokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia

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“rating”or“inclination”given 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.

关键词： video recommendation system knn algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence

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