| Author Name | Affiliation | Postcode | | Meesala Sravani* | Department of Computer Science and Engineering, GMR Institute of Technology, Rajam 532127, Andhra Pradesh, India | 532127 | | Ch.Vidyadhari | Department of Information Technology, Gokaraju Rangaraju Institute of Engineering,Kukatpally,Hydarabad 500090 ,Telangana, India | 500090 | | S Anjali Devi | Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,Vaddeswaram,Guntur 522302, Andhra Pradesh, India | 522302 |
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| Abstract: |
| In the realm of contemporary Artificial Intelligence, Machine Learning enables automation, allowing systems to naturally acquire and enhance their capabilities through learning. In 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 clients. The expectation depends on past evaluations, history, interest, IMDB rating, and so on. This 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 time. The required datasets for the video are taken from Grouplens. This recommender framework is executed by utilizing Python Programming Language. For 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 examples. For 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 focuses. The 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. |
| Key words: video recommendation system, KNN algorithms, collaborative filtering, and content-based filtering, classification algorithms, artificial intelligence |
| DOI：10.11916/j.issn.1005-9113.2023097 |
| Clc Number:TP181 |
| Fund: |
