Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/10174
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dc.contributor.authorSingh, Gautmi-
dc.contributor.authorVerma, Shivam-
dc.contributor.authorSidhu, Jagpreet [Guided by]-
dc.date.accessioned2023-09-30T08:29:26Z-
dc.date.available2023-09-30T08:29:26Z-
dc.date.issued2023-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/10174-
dc.descriptionEnrollment No. 191354, 191455en_US
dc.description.abstractNowadays, a recommendation system plays a very important role in various fields including e-commerce and OTT-platforms and has made things easier to find. A recommendation engine recommends the most relevant data to the user by using different algorithms. For watching favourable movies online we can utilise movie recommendation systems, which are more reliable, since searching for preferred movies will require more and more time which one cannot afford to waste. In this paper, to improve the quality of a movie recommendation system, a Hybrid approach by combining content based filtering and collaborative filtering, using SVM as a classifier and genetic algorithm is presented in the proposed methodology and comparative results have been shown which depicts that the proposed approach shows an improvement in the accuracy, quality and scalability of the movie recommendation system than the pure approaches in three different datasets. Hybrid approach helps to get the advantages from both the approaches as well as tries to eliminate the drawbacks of both methods.en_US
dc.language.isoen_USen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectSupport vector machineen_US
dc.subjectK nearest neighbouren_US
dc.subjectMovie metadataen_US
dc.subjecte-commerceen_US
dc.titleRecommender System Project with Heroku Deploymenten_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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