scenarios/media_and_entertainment/README.md
Recommendation systems are fundamental to modern media and entertainment platforms. For example, Netflix credits recommendation systems for 75% of the content viewed on the platform. It is the same with YouTube, with 60% of views coming from recommendation systems. In TikTok, 90% of views come from reco, and for Spotify, the personalization service is estimated to make 10% of their revenue. A modern platform without recommendation systems would likely struggle to engage users and drive revenue.
Here are the primary scenarios and considerations for media and entertainment recommendations.
The main task is helping users discover relevant content from vast libraries of movies, shows, music, or games. This is typically shown on home pages and personalized feeds. Models like ALS, NCF, and SAR are commonly used.
This scenario predicts what content a user might want to consume next, such as the next episode, similar songs, or related games. This is particularly important for maintaining user engagement and reducing churn.
For music and video platforms, this involves creating custom playlists based on user preferences and listening/viewing history. Both collaborative filtering and content-based approaches can be used here.
This scenario involves recommending live content (streams, events, broadcasts) based on user interests and current popularity. Real-time processing and trending detection are crucial here.
Key data includes user profiles, content metadata (genre, actors, duration), viewing/listening history, and engagement metrics (watch time, ratings, shares).
Common evaluation metrics include engagement time, retention rate, and user satisfaction. For music and video platforms, metrics like CTR and MAU are particularly important. A/B testing is standard practice for evaluating recommendations.
Media recommendations need to balance between promoting new content and leveraging known user preferences. Content licensing windows, regional availability, and device compatibility must also be considered. Additionally, recommendations should account for household sharing of accounts and varying preferences based on viewing context.