Please experiment with the ALS algorithm, you can use the following notebook as starting template: https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/42740061589303/2161112564136160/8105921225255291/latest.html 1. Try to train and run the model on the 20 Million movie ratings dataset instead of the 1 Million one. Files: /databricks-datasets/cs110x/ml-20m/data-001/movies.csv /databricks-datasets/cs110x/ml-20m/data-001/ratings.csv
PYTHON / DATABRICKS
Please experiment with the ALS
1. Try to train and run the model on the 20 Million movie ratings dataset instead of the 1 Million one. Files:
/databricks-datasets/cs110x/ml-20m/data-001/movies.csv
/databricks-datasets/cs110x/ml-20m/data-001/ratings.csv
2. Test with various values of: ranks, regularization parameter, number of iterations.
Compare the models and find the best model based on the error value (i.e RMSE).
The documenation of the algorithm can be found at: https://spark.apache.org/docs/3.3.1/ml-collaborative-filtering.html
https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.recommendation.ALS.html#pyspark.ml.recommendation.ALS
Prepare a table with at least 10 of your own ratings for the movies that you select and run the model with this data as input and show the top 20 movies that the model recommends for you to watch.
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