Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.



Mal Recommender is a recommender system for predicting MyAnimeList scores using linear regression.

Under the Crawler package are some files for crawling data on top anime, and users. I specifically chose the top 1000 anime and users who have watched more than 100 of the top 1000. The MyAnimeList API did not have any tools for finding users, so I repeatedly crawled the recently active page and specific anime forums to get 10,000 users. In total, I had approximately 2 million data points and a 1000x10000 table describing the ratings which was 20% fill.

Under the Learner package are the programs to both plot, learn, and test the data. I created another cross validation data set of approximately 1000 users to determine the linear regularization parameter and the number of features to use. Finally, I used a testing data set of approximately 1000 users to determine the final accurancy of the predictor.


Top row is regularization parameter.

500 Features

3 6 10 15 20 30 50 100
Score Diff = 0 0.37936 0.28980 0.43582 0.44818 0.44945 0.44269 0.44063 0.42689
Score Diff <= 1 0.66282 0.52252 0.72763 0.73624 0.73773 0.72890 0.72396 0.70461
Score Diff <= 2 0.91816 0.75100 0.94533 0.94998 0.94871 0.94417 0.93973 0.92715

1000 Features

10 15 30 60
Score Diff = 0 0.44900 0.44996 0.43627 0.40715
Score Diff <= 1 0.73246 0.73893 0.73129 0.69986
Score Diff <= 2 0.94799 0.95033 0.94555 0.93502

Improvements and Some Notes

  • Increasing the features will marginally increase the accuracy
  • More data (Some of the anime only have a few hundred ratings)
  • Guessing the median score yields an accuracy of about 30% so a linear regression works substantially better.
  • The more anime the user has watched, the more accurate the predictor is
  • Fuzzy k-means and k-means can be used to cluster anime and find groups


A linear regression recommender system generally works pretty well. With a regularization parameter of 15 and 1000 features, it can get 45% of all scores correct, 75% of all scores correct within one point, and 95% of all scores correct within two points. However, it might not be the case that anime preferences can be linearly separable, so non-linear methods might be needed for further accuracy (maybe SVMs or self organizing maps?).

You can’t perform that action at this time.