What features could be added to Bilibili's video recommendation system to increase user engagement?

Introduction

To increase user engagement on Bilibili's video recommendation system, we need to explore innovative features that enhance personalization, discovery, and social interaction. I'll outline a strategic approach to identify key user segments, analyze pain points, and propose targeted solutions that align with Bilibili's unique position in the video-sharing market.

Step 1

Clarifying Questions (5 mins)

• Looking at Bilibili's unique position in the Chinese video-sharing market, I'm curious about the platform's primary user base. Could you provide more information on the core demographics and content preferences of Bilibili users?

Why it matters: Understanding the user base will help tailor recommendations to specific interests and subcultures. Expected answer: Primarily Gen Z and young millennials, with a focus on anime, gaming, and user-generated content. Impact on approach: Would focus on features that cater to niche interests and foster community engagement.

• Considering Bilibili's recent growth, I'm interested in understanding the current state of the recommendation system. What are the key metrics you're currently using to measure its performance, and how have they been trending?

Why it matters: Identifies areas of strength and weakness in the current system to guide improvement efforts. Expected answer: Key metrics include watch time, click-through rate, and user retention, with recent plateaus in engagement. Impact on approach: Would prioritize features that address specific areas of underperformance.

• Given the competitive landscape of video platforms in China, I'm wondering about Bilibili's unique value proposition. What sets Bilibili apart from other platforms like Douyin or Kuaishou in terms of content and user experience?

Why it matters: Helps align new features with Bilibili's core strengths and differentiation strategy. Expected answer: Bilibili's strength lies in its community-driven content and in-depth, longer-form videos. Impact on approach: Would focus on features that enhance community interaction and support content creators.

• Considering the importance of AI in recommendation systems, I'm curious about Bilibili's current AI capabilities. What machine learning models or algorithms are currently being used, and are there any limitations we should be aware of?

Why it matters: Determines the technical feasibility of potential improvements and identifies areas for AI enhancement. Expected answer: Currently using collaborative filtering and content-based models, with plans to explore more advanced deep learning techniques. Impact on approach: Would consider features that leverage existing AI capabilities while proposing enhancements that push the boundaries of the current system.