Keywords
Recommender System, Matrix Factorization, Sparse matrix, Predicting Movie Rating, MovieLens Dataset
Abstract
Nowadays, a Recommender System is a technology
that aims to predict preferences based on the user’s selections.
These systems are applied in numerous fields, such as movies,
music, news, books, research articles, search queries, social tags,
and various products. In this study, we use this potential tool to
predict the ratings of users’ preferences in MovieLens datasets. To
do so, we applied the matrix factorization algorithm and calculate
the RMSE as our evaluation metric. The results represent that
RMSE estimated for the train and test set are 0.83 and 0.93 that
are very close one another. This results indicates that our model
performance is well
Course Name
STA 6704 Data Mining 2
Instructor Name
Rui Xie
College
College of Sciences
STARS Citation
Alipour Yengejeh, Amir, "A Recommender System for Movie Ratings with Matrix Factorization Algorithm" (2023). Data Science and Data Mining. 7.
https://stars.library.ucf.edu/data-science-mining/7
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