Movie Recommender System

Business Goals

• Improve user engagement.
• Personalize movie recommendations.
• Increase movie viewing rate, recommend “long tail” items.

Challenge

• Movie recommendations were made manually by the content managers department. Recommendation creation became a manual toil, regularly consuming significant human efforts.
• Some movies were not watched a single time.

Results

• Manual toil (content management by hand) was reduced by 60% and boiled down mainly to validate the automatically generated recommendations.
• Over one month of A/B testing of the integrated movie recommender system, the movie watch rate increased from 20% to 40%. Compared to the controlled deployment with manual recommendations, the average user screen time increased by 20%.
• The number of non-watched films decreased by 25%.

Implementation Details

• We have developed the “Trending now” recommender offers users the most currently popular movies by the weighted average number of likes and view percentage.
• The “Movie-to-movie” similarity recommender offers the most similar movies to the movie that just has been watched by a user.
• Hybrid collaborative filtering recommender combining movie features, user features, and interaction data of users’ likes/dislikes and users’ movie watch percentages.