Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix
Özet
User ratings on items like movies, songs, and shopping products are used
by Recommendation Systems (RS) to predict user preferences for items that have
not been rated. RS has been utilized to give suggestions to users in various domains
and one of the applications of RS is movie recommendation. In this domain, three
general algorithms are applied; Collaborative Filtering that provides prediction
based on similarities among users, Content-Based Filtering that is fed from the
relation between item-user pairs and Hybrid Filtering one which combines these
two algorithms. In this paper, we discuss which methods are more efficient in movie
recommendation in the framework of Collaborative Filtering. In our analysis, we use
Netflix Prize dataset and compare well-known Collaborative Filtering methods
which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by
using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest
Neighbour method is more successful in our dataset.
