1. Create local Spark Context; 2. Read ratings.csv and movies.csv from movie-lens dataset into Spark (https://grouplens.org/datasets/movielens/); 3. Ask user for rating on 20 random movies to build user profile and include in training set; 4.Train Spark Collaborative Filtering Learner (Alternating Least Squares) algorithm https://www.infofarm.be/articles/alternating-least-squares-algorithm-re…; 5. Apply model to all other movies unrated by user; 6. Display recommendation results for user
BLOG: Movie Recommendations with Spark Collaborative Filtering https://www.knime.org/blog/MovieRecommendationswithSparkCollaborativeFi…
EXAMPLES Server: 10_Big_Data/02_Spark_Executor/10_Recommendation_Engine_w_Spark_Collaborative_Filtering10_Big_Data/02_Spark_Executor/10_Recommendation_Engine_w_Spark_Collaborative_Filtering*
Download a zip-archive
* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually