Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16172
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dc.contributor.authorVamsi Krishna, Neelapala Jola-
dc.contributor.authorMaitra, Sarit-
dc.date.accessioned2024-07-22T03:54:54Z-
dc.date.available2024-07-22T03:54:54Z-
dc.date.issued2024-
dc.identifier.citation27p.en_US
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16172-
dc.description.abstractMostly people are accessible to afew Recommendation systems that works with accuracy and delivering the good recommendations to the users in the way they wanted and also based on the ratings of the previous users . Mainly the Collaborative filtering has been ruling from the past few years and most of the algorithms are working on this Recommendation System approach and also when the users have been increasing continuously changing from offline to online Environment . Previously less than a decade back people used to buy the books from the book stores or read the books from the Libraries and they have suggested by some shopkeeper or Librarian . The Recommendation system approach is likely to be same but it takes the past data of the other users regarding the ratings then it go for the analysis and finally give the suggestions . Through online Platforms have many publications of the different authors but user need some Personalized book recommendations to look into further books which is similar to the user preferences of the book ratings , authors , Publications, Genres, etc..en_US
dc.language.isoenen_US
dc.publisherAlliance School of Business, Alliance Universityen_US
dc.relation.ispartofseries2022MMBA07ASB203-
dc.subjectMachine Learningen_US
dc.subjectAlgorithmsen_US
dc.subjectBooken_US
dc.subjectPublicationsen_US
dc.subjectLibrariesen_US
dc.titleBook Recommendation System Using Machine Learningen_US
dc.typeOtheren_US
Appears in Collections:Dissertations - Alliance School of Business

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