ICACT–2021

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/24483

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    Hybrid Movie Recommendation System
    (Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Punitharasa, Sinthujan; Selvanajagam, Kirisanthi; Ramakrishnan, Thamilini; Chelvarajah, Amalraj; Weerasinghe, W.M.R.M.
    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.