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IMDB Improved (Random name that just stuck)

IMDB Improved (Random name that just stuck). Joshua Boren, Douglas Hitchcock, Calvin Kern, Ryan Norton. Recommenders. What is a recommender? How does a recommender learn? What applications are there for recommenders?. Steer Clear!.

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IMDB Improved (Random name that just stuck)

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Presentation Transcript

  1. IMDB Improved(Random name that just stuck) Joshua Boren, Douglas Hitchcock, Calvin Kern, Ryan Norton

  2. Recommenders • What is a recommender? • How does a recommender learn? • What applications are there for recommenders?

  3. Steer Clear! • Our recommender suggests movie titles that a user should not bother watching. • We have developed the world’s first unrecommender.

  4. Learning from Data • How did our recommender learn from the dataset? • What did our dataset consist of? • What features did we have to use? • Where did we get our data?

  5. Netflix Prize Data Set • Movies • Movie Ids • Year Released • Movie Title • Training Set • One for each movie that contained: • User ID • Rating • Date of rating

  6. Organizing the data • To apply the recommender, the data was reorganized • Converted from movies with users, to users with movies

  7. Learning from Data • Learning for one user and then growing on that • Re-creating the model when a user is updated • Methods used

  8. Collaborative Filtering • Collaborating • Collecting preferences or taste information from many users • Filtering • Cold Start • There were no early assumptions, conclusions were formed upon reviewing a large data set. • Content-Based-Filtering • Not all categories are treated equally.

  9. Results • What have our efforts gotten us? • Precision: 90% • Recall (True Positive Rate): 1

  10. Results • How do we define a successful recommendation? • How did our results stack up against the teams competing in the Netflix competition? • Trivial Algorithm: 1.0540 • Netflix Winner RMSE: 0.8554 • Our RMSE: 0.9820

  11. Future Plans • Given more time, how would we improve our recommender system to be more accurate? • What other changes would/could we make?

  12. Conclusion

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