Harnessing the Power of Graph Neural Networks for Personalized Rail Recommendations

In streaming services, recommendations are vital for guiding users to content that suits their preferences. The homepage plays a key role in helping users quickly find something they’ll enjoy, but the challenge lies in curating a vast catalog within limited screen space while catering to diverse individual interests. Typically, the homepage is organized into thematic rows that users can scroll through horizontally or vertically. The challenge of optimizing this layout to maximize user serendipity involves determining how to select the most relevant rows for each user, populate those rows with appropriate videos, and arrange them within the constrained page space to ensure intuitive video selection. To address this challenge, this paper introduces a scalable framework designed to generate personalized thematic rails and rank the generated rails vertically per users’ taste. Central to this framework is the utilization of a Graph Neural Network (GNN), which learns item representations from rich item graphs infused with metadata. By harnessing users’ historical interactions alongside these learned item representations, the framework constructs nuanced user profiles, capturing their evolving preferences and behaviors. The primary objective of this framework is to enhance the Normalized 2-Dimensional Discounted Cumulative Gain (N2DCG) metric, a key measure of user engagement with recommended content. This is achieved by iteratively refining the vertical ranking of the generated rails per user. Rigorous offline evaluations and consequent online experiments prove the effectiveness of our framework. Our findings not only affirm the potency of personalized thematic rails in driving user engagement but also reinforce the potential of leveraging advanced techniques such as Graph Neural Networks in enhancing recommendation systems within the streaming landscape.