How would you design a recommendation system for Disney+ for new customers (with less data)?

Designing a Recommendation System for Disney+ New Customers: Balancing Limited Data with Personalization

To design a recommendation system for new Disney+ customers with limited data, we'll implement a hybrid approach combining content-based filtering, collaborative filtering, and contextual recommendations. This system will leverage Disney's vast content library metadata, initial user interactions, and demographic information to provide personalized suggestions while continuously improving as more user data is collected.

Introduction

The challenge at hand is to create an effective recommendation system for new Disney+ customers, where we have limited user data to work with. This is a critical technical problem as it directly impacts user engagement, retention, and ultimately, the success of the streaming platform. Our goal is to design a system that can provide relevant recommendations from the start, while also being able to quickly adapt and improve as we gather more data on user preferences.

To address this challenge, I'll outline a comprehensive approach that covers:

1. Clarifying technical requirements and constraints
2. Analyzing the current state and technical challenges
3. Proposing technical solutions
4. Developing an implementation roadmap
5. Establishing metrics and monitoring strategies
6. Managing potential risks
7. Outlining a long-term technical strategy

Let's begin by clarifying the technical requirements to ensure we're aligned on the problem space.

Step 1

Clarify the Technical Requirements (3-4 minutes)

1. "Considering the nature of a streaming platform, I'm assuming we're dealing with a microservices architecture. Can you confirm if this is the case, and if so, what are the key services we'll need to interact with for the recommendation system?

   Why it matters: This affects how we design and integrate the recommendation system. Expected answer: Microservices architecture with separate services for user management, content metadata, and streaming. Impact on approach: We'd need to design our recommendation system as a separate microservice that can efficiently communicate with these existing services."

2. "In terms of data availability, what specific user data do we have access to for new customers? I'm thinking along the lines of basic demographics, device information, and initial content selections.

   Why it matters: This determines our starting point for generating initial recommendations. Expected answer: Basic demographic data (age, gender, location) and initial content interactions. Impact on approach: We'd need to heavily leverage content-based filtering initially, gradually incorporating collaborative filtering as we gather more data."

3. "Regarding scalability, what's our expected user growth rate, and what's the current infrastructure's capacity to handle recommendation requests?

   Why it matters: This influences our choice of algorithms and infrastructure decisions. Expected answer: Expecting rapid growth, current infrastructure can handle X requests per second. Impact on approach: We might need to consider distributed computing solutions and caching strategies to ensure real-time recommendations at scale."

4. "From a compliance standpoint, are there any specific data protection regulations we need to adhere to, considering Disney+'s global presence?

   Why it matters: This affects how we store and process user data for recommendations. Expected answer: Need to comply with GDPR, CCPA, and other regional data protection laws. Impact on approach: We'd need to implement data anonymization, user consent management, and region-specific data handling in our recommendation system."

Assumptions:

• We have access to a comprehensive content metadata database for Disney's library.
• The platform supports real-time tracking of user interactions (views, likes, etc.).
• We can deploy machine learning models in production.