M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation

Abstract

Matching preferred shows to the subscribers is extremely important in the Over-the-Top (OTT) platforms. The existing methods did not adequately consider the characteristics of the OTT services, i.e., rich meta information, diverse user interests, and mixed recommendation scenarios, leading to sub-optimal performance. This paper introduces the Multi-Modal Multi-Interest Multi-Scenario Matching (M5) for the OTT recommendation to fully exploit these attributes. A multi-modal embedding layer is first introduced to transform the show IDs into both ID embeddings initialized randomly and content graph (CG) embeddings derived from the node representations pre-trained on a metagraph. To segregate the semantics between ID and CG embeddings, M5 exploits the mirrored two-tower modeling in the subsequent layers for efficiency and effectiveness. Specifically, a multi-interest extraction layer is proposed separately on ID and CG behaviors to model users' coarse-grained and fine-grained interests through behavioral categorization, subsidiary decoration, masked-language-modeling augmented self-attention modeling and subsidiary-intensity interest calibration. Facing the inherent diverse scenarios, M5 distinguishes the scenario differences at both feature and model levels, which crosses features with the scenario indicators and employs Split Mixture-of-Experts to generate the ID, and CG user embeddings. Finally, a weighted candidate matching layer is established to calculate the ID- and CG-oriented user-item preferences and then merge into a hybrid score with dynamic weighting. The extensive online and offline experiments over two real-world OTT platforms Hulu and Disney+ reveal that M5 significantly outperforms the previous state-of-the-art and online matching algorithms over various scenarios, indicating the effectiveness and robustness of the proposed method. M5 has been fully deployed on the main traffic of the most popular "For You'' sets of both platforms, continuously enhancing the user experience for hundreds of millions of subscribers every day and steadily increasing business revenue.