what metrics would you use to evaluate bilibili's video recommendation algorithm?

Introduction

Evaluating Bilibili's video recommendation algorithm is crucial for optimizing user engagement and platform growth. To approach this product success metrics problem effectively, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Bilibili's video recommendation algorithm is a core feature of their content discovery system, aimed at personalizing user experiences and maximizing engagement on the platform. Key stakeholders include:

1. Users: Seeking entertaining and relevant content
2. Content creators: Wanting exposure for their videos
3. Advertisers: Targeting specific audiences
4. Bilibili: Aiming to increase user engagement and retention

The user flow typically involves:

1. User logs in and views the homepage
2. Algorithm presents personalized video recommendations
3. User interacts with recommended content (views, likes, comments)
4. Algorithm refines recommendations based on user behavior

This feature is critical to Bilibili's broader strategy of becoming the go-to platform for Gen Z users in China, differentiating itself from competitors like iQiyi and Youku through its focus on ACG (Anime, Comics, and Games) content and user-generated videos.

Compared to competitors, Bilibili's algorithm places a stronger emphasis on niche interests and community engagement, reflecting its unique user base and content ecosystem.

In terms of product lifecycle, the recommendation algorithm is in the growth/maturity stage, continuously evolving to improve accuracy and user satisfaction.