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

Introduction

Evaluating iQiyi's recommendation algorithm is crucial for optimizing user engagement and content discovery on the platform. 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

iQiyi is a leading Chinese video streaming platform, often referred to as the "Netflix of China." The recommendation algorithm is a core feature that suggests personalized content to users based on their viewing history, preferences, and behavior.

Key stakeholders include:

1. Users: Seeking engaging, relevant content
2. Content creators: Aiming for visibility and viewership
3. Advertisers: Targeting specific audience segments
4. iQiyi business team: Driving user growth and retention

User flow:

1. User logs in to iQiyi
2. Algorithm analyzes user data and current trends
3. Personalized recommendations appear on the home screen and throughout the app
4. User interacts with recommendations, providing further data for algorithm refinement

The recommendation algorithm is central to iQiyi's strategy of increasing user engagement and time spent on the platform. It directly impacts content discovery, user satisfaction, and ultimately, subscriber retention and growth.

Compared to competitors like Tencent Video and Youku, iQiyi has positioned itself as a technology-driven platform with a strong focus on AI and machine learning for content recommendation and creation.

Product Lifecycle Stage: Mature but continually evolving. The algorithm is a core feature that undergoes constant refinement and improvement to stay competitive in the rapidly changing streaming landscape.