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FM-Intent: Predicting User Session Intent with Hierarchical Multi-Task Learning
- FM-Intent is a novel recommendation model introduced by Netflix to predict user intents and enhance next-item recommendations through hierarchical multi-task learning.
- The model aims to enrich the understanding of user sessions by incorporating the prediction of underlying user intents, offering a more nuanced recommendation experience.
- FM-Intent utilizes implicit signals from user interaction metadata to predict various user intents related to actions, genre preferences, movie/show types, and time-since-release.
- The model architecture of FM-Intent involves three main components: input feature sequence formation, user intent prediction using a Transformer encoder, and next-item prediction with hierarchical multi-task learning.
- Experimental validation shows that FM-Intent outperforms state-of-the-art models, including Netflix's foundation model, in next-item prediction accuracy.
- FM-Intent generates meaningful user intent embeddings for clustering users with similar intents, providing valuable insights into user viewing patterns and preferences.
- The model has been integrated into Netflix's recommendation ecosystem, allowing for personalized UI optimization, enhanced recommendation signals, and search optimization based on user intent predictions.
- By understanding user intents beyond next-item prediction, FM-Intent enhances Netflix's recommendation capabilities, delivering more personalized and relevant content recommendations.
- The model's hierarchical multi-task learning approach and comprehensive experimental results demonstrate its effectiveness in improving recommendation accuracy and user experience.
- FM-Intent signifies a significant advancement in Netflix's recommendation system, emphasizing the importance of user intent prediction for providing satisfying and tailored recommendations.
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