movielens

Evaluating k-nearest neighbors and singular value decomposition techniques for collaborative filtering recommender systems

Variables

int userId: the integer ID of the anonymized user
int movieId: the integer ID of the movie
int rating: integer rating ranging from 1 to 5 given by the user to the movie
int timestamp: the number of seconds had elapsed since the Unix epoch until the user rated the movie

Files

Aside from README.md, the repository contains 1 subdirectory and 3 other files:

1. ml-100k, the folder which contains the MovieLens 100K dataset
2. preprocessing.R, the R script for validating the data
3. movielens.ipynb, the notebook in which the k-NN and SVD++ algorithms are implemented and compared
4. movielens.html, the exported HTML version of movielens.ipynb for browser-viewing

Generating recommendations

To generate movie recommendations for a specific user:

1. Run recommend.py.
2. Enter the ID of the user for which you want to produce recommendations.
3. Enter the number of recommendations.

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Code authorship

2022 © Jessan Rendell G. Belenzo

Terms of use

Licensed under the GNU General Public License v3.0. See LICENSE.

Acknowledgments

Hug, Nicolas. "Surprise: A Python library for recommender systems." Journal of Open Source Software 5.52 (2020): 2174.

Harper, F. Maxwell, and Joseph A. Konstan. "The movielens datasets: History and context." Acm transactions on interactive intelligent systems (tiis) 5.4 (2015): 1-19.

Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Recommender systems handbook." Springer, Boston, MA, 2011. 1-35.

Koren, Yehuda. "Factorization meets the neighborhood: a multifaceted collaborative filtering model." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008.