Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)
Source of Data : https://www.kaggle.com/netflix-inc/netflix-prize-data Data files : combined_data_1.txt combined_data_2.txt combined_data_3.txt combined_data_4.txt movie_titles.csv The first line of each file [combined_data_1.txt, combined_data_2.txt, combined_data_3.txt, combined_data_4.txt] contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format: CustomerID,Rating,Date MovieIDs range from 1 to 17770 sequentially. CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users. Ratings are on a five star (integral) scale from 1 to 5. Dates have the format YYYY-MM-DD.
1.Predict the rating that a user would give to a movie that he has not yet rated. 2.Minimize the difference between predicted and actual rating (RMSE and MAPE)
1.Some form of interpretability. 2.There is no low latency requirement as the recommended movies can be precomputed earlier.