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Recommender System for Movies using Collaborative Filtering

http://hdl.handle.net/20.500.12678/0000004116
5981ab74-a3cc-4a6b-86e9-f68dedcb4fca
db14160f-9dd7-4ecd-80f9-e54cea3bc50a
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55238.pdf 55238.pdf (396 Kb)
Publication type
Article
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Publication
Title
Title Recommender System for Movies using Collaborative Filtering
Language en
Publication date 2009-12-30
Authors
Hlaing, Nan Yu
Oo, May Phyo
Description
Recommender systems are programs which attempt to predict items that a user may be interest in. Recommender systems acts personalized decision guides, aiding users in decisions on matter related to personal taste. Recommender system depends on information provided by different users to gather its knowledge. Collaborative Filtering (CF) become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. One popular algorithm is the memory-based collaborative filtering, which predicts a user’s preference based on his or her similarity to other users (instances) in the database. Movie recommendation system demonstrates the advantages of multidimensional visualization of the recommender system’s results. In this paper, we implement a recommender system for movies using memory–based collaborative filtering method. Memory-based for collaborative filtering predicts average (weighted) rating between similar users or items.
Keywords
Recommender system, Collaborative filtering, Memory - Based Algorithms
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1818
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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