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Hybrid Movie Recommendation System

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dc.contributor.author Punitharasa, Sinthujan
dc.contributor.author Selvanajagam, Kirisanthi
dc.contributor.author Ramakrishnan, Thamilini
dc.contributor.author Chelvarajah, Amalraj
dc.contributor.author Weerasinghe, W.M.R.M.
dc.date.accessioned 2022-02-25T03:44:12Z
dc.date.available 2022-02-25T03:44:12Z
dc.date.issued 2021
dc.identifier.citation Punitharasa Sinthujan, Selvanajagam Kirisanthi, Ramakrishnan Thamilini, Chelvarajah Amalraj, Chelvarajah Amalraj, Weerasinghe, W.M.R.M (2021), Hybrid Movie Recommendation System, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 13-19 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/24488
dc.description.abstract Movie recommendations play a great part in the aspects of our social life. Such a system allows users to recommend a group of films based on their interests or the popularity of the movies. This research was conducted to study different approaches to movie recommendation and discusses a hybrid approach that combines a content-based filter, a collaborative-memory-based filter, and a collaborative-model-based filter. The proposed system aims to reduce the issues with existing movie recommendation systems by enhancing performance. The content-based filter is based on a TF-IDF classifier with cosine similarity. The collaborative-memory-based filter is based on truncated SVD with Pearson correlation. A collaborative model-based filter is based on improved SVD matrix factorization. en_US
dc.publisher Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka en_US
dc.subject Movie Recommendation System; Content-based filtering; Collaborative- memory-based filtering; Collaborative- model-based filtering; Hybrid approach en_US
dc.title Hybrid Movie Recommendation System en_US


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