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Video Recommendation System Using Machine-Learning Techniques
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作者 Meesala Sravani Ch Vidyadhari S Anjali Devi 《Journal of Harbin Institute of Technology(New Series)》 CAS 2024年第4期24-33,共10页
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 fini... 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. 展开更多
关键词 video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence
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Improving the precision of the keyword-matching pornographic text filtering method using a hybrid model 被引量:3
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作者 苏贵洋 李建华 +1 位作者 马颖华 李生红 《Journal of Zhejiang University Science》 EI CSCD 2004年第9期1106-1113,共8页
With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applica... With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision. 展开更多
关键词 Pornographic text filtering content based filtering Information filtering Network content security
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Internet Regulation, a New Approach: Outline of a System Formed to Be Controlled by the Internet Users
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作者 Nikolaos Koumartzis Andreas Veglis 《Computer Technology and Application》 2012年第1期16-23,共8页
This paper describes an outline for the proper design of a Fair Internet Regulation System (FIRS), i.e., a system that will be implemented in a national level and encourage the participation of Internet users in enr... This paper describes an outline for the proper design of a Fair Internet Regulation System (FIRS), i.e., a system that will be implemented in a national level and encourage the participation of Internet users in enriching and correcting its "behavior". Authors aim to design a system that will be operated in some extent by the Internet users, and so it will be easier to be accepted by Western democracies willing to implement a fair Internet regulation policy. Last, the authors state the importance of using well-designed surveys prior to the implementation of FIRS, announce the launch of an online tool (WebObserver.net) and invite researchers to be part of this international effort. 展开更多
关键词 content filtering content blocking INTERNET REGULATION blacklist.
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