TY  - JOUR
TI  - Movie Recommendation Systems Based on Collaborative Filtering: A Case Study onNetflix
AB  - User ratings on items like movies, songs, and shopping products are usedby Recommendation Systems (RS) to predict user preferences for items that havenot been rated. RS has been utilized to give suggestions to users in various domainsand one of the applications of RS is movie recommendation. In this domain, threegeneral algorithms are applied; Collaborative Filtering that provides predictionbased on similarities among users, Content-Based Filtering that is fed from therelation between item-user pairs and Hybrid Filtering one which combines thesetwo algorithms. In this paper, we discuss which methods are more efficient in movierecommendation in the framework of Collaborative Filtering. In our analysis, we useNetflix Prize dataset and compare well-known Collaborative Filtering methodswhich are Singular Value Decomposition, Singular Value Decomposition++, K Nearest Neighbour and Co-Clustering. The error of each method is calculated byusing Root Mean Square Error (RMSE). Finally, we conclude that K-NearestNeighbour method is more successful in our dataset.
AU  - SÜTÇÜ, MUHAMMED
AU  - ERDEM, OĞUZKAN
AU  - KAYA, ECEM
PY  - 2021
JO  - Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi
VL  - 37
IS  - 3
SN  - 1012-2354
SP  - 367
EP  - 376
DB  - TRDizin
UR  - http://search/yayin/detay/507439
ER  -