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Title: A Movie Rating Prediction Algorithm with Collaborative Filtering
Authors: Fikir, O. Bora
Yaz, İlker O.
Özyer, Tansel
Keywords: Collaborative filtering
QR factorization
k-nearest neighborhood
Issue Date: 2010
Publisher: IEEE Computer Soc
Source: International Conference on Advances in Social Network Analysis and Mining (ASONAM) -- AUG 09-11, 2010 -- Odense, DENMARK
Abstract: Recommendation systems are one of the research areas studied intensively in the last decades and several solutions have been elicited for problems in different domains for recommending. Recommendation may differ as content, collaborative filtering or both. Other than known challenges in collaborative filtering techniques, accuracy and computational cost at a large scale data are still at saliency. In this paper we proposed an approach by utilizing matrix value factorization for predicting rating i by user j with the sub matrix as k-most similar items specific to user i for all users who rated them all. In an attempt, previously predicted values are used for subsequent predictions. In order to investigate the accuracy of neighborhood methods we applied our method on Netflix Prize [1]. We have considered both items and users relationships on Netflix dataset for predicting movie ratings. We have conducted several experiments.
ISBN: 978-0-7695-4138-9
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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