What features could make Kuaishou's short video recommendations more personalized and engaging?

Introduction

To improve Kuaishou's short video recommendations and make them more personalized and engaging, we need to dive deep into user behavior, content preferences, and the underlying recommendation algorithms. I'll outline a strategic approach to enhance the user experience and increase engagement on the platform.

Step 1

Clarifying Questions (5 mins)

• Looking at Kuaishou's position in the short video market, I'm curious about our current user base and growth trajectory. Could you share some insights on our monthly active users (MAU) and how it compares to our main competitors?

Why it matters: This helps us understand the scale of our user base and potential for growth. Expected answer: Kuaishou has around 600 million MAU, second to TikTok in the global market. Impact on approach: A large user base would suggest focusing on personalization at scale.

• Considering the nature of short-form video content, I'm wondering about our current average user session duration and daily active user (DAU) to MAU ratio. Can you provide some context on these metrics?

Why it matters: These metrics indicate user engagement levels and help identify areas for improvement. Expected answer: Average session duration of 30 minutes, with a DAU/MAU ratio of 0.4. Impact on approach: Lower engagement metrics would prioritize features that increase session time and frequency.

• Given the importance of content in short video platforms, I'm interested in understanding our content ecosystem. What's the ratio of user-generated content to professional or sponsored content on our platform?

Why it matters: This balance affects our recommendation strategy and user engagement. Expected answer: 80% user-generated, 20% professional/sponsored content. Impact on approach: A high proportion of UGC would suggest focusing on improving discovery for diverse content creators.

• Considering the rapid evolution of AI and machine learning, I'm curious about our current recommendation system. What type of algorithm are we currently using, and what data points are we leveraging for personalization?

Why it matters: Understanding our technical capabilities helps in proposing feasible improvements. Expected answer: Using a collaborative filtering algorithm with user interaction data and content features. Impact on approach: Advanced AI capabilities would allow for more sophisticated personalization strategies.