Recommender Systems with Surprise

Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.

Examples

1. RS with KNN
2. RS with SVD
3. Tune model (SVD)

Data

MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota.

This data set consists of:

Table format: u.data

user id item id rating timestamp
196 242 3 881250949
186 302 3 891717742
22 377 1 878887116
244 51 2 880606923
166 346 1 886397596

Table format: u.item

movie id movie title release date IMDb URL
1 Toy Story (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)
2 GoldenEye (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?GoldenEye%20(1995)
3 Four Rooms (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)
4 Get Shorty (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)
5 Copycat (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Copycat%20(1995)

Table format: u.user

user id age gender occupation zip code
1 24 M technician 85711
2 53 F other 94043
3 23 M writer 32067
4 24 M technician 43537
5 33 F other 15213

You can see the original dataset here

Python Dependencies

  conda install -c conda-forge scikit-surprise 

Contributing and Feedback

Any kind of feedback/criticism would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc…).

Authors

License

This project is licensed under the terms of the MIT license.

Acknowledgments

I would like to show my gratitude to:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI = http://dx.doi.org/10.1145/2827872