ML System Design - Video Recommendation

Web Search Results

After conducting a thorough web search, I found the following relevant information:

1. Reddit Post (r/cscareerquestions): A user shared their experience with a video recommendation system interview question at Reddit. The post provides a brief overview of the problem statement and some key points to consider.

2. 1point3acres Thread: A discussion thread on 1point3acres mentions the same interview question. Users share their thoughts on the problem and possible approaches to solve it.

3. Glassdoor Interview Review: An interview review on Glassdoor mentions the video recommendation system question as one of the technical questions asked at Reddit.

Full Problem Statement

Video Recommendation System for Reddit

Design a video recommendation system for Reddit. Focus on how candidate generation, ranking, serving, and feedback loops work end to end, and how user interactions are logged so the system can learn and improve over time.

Key Points to Consider

1. Candidate Generation:

   • How do you identify relevant videos to recommend to a user?
   • What factors or signals are used to generate candidate videos?

2. Ranking:

   • How do you rank the candidate videos to determine the most relevant ones for a user?
   • What algorithms or models can be used for ranking?

3. Serving:

   • How are the top-ranked videos served to users?
   • How do you ensure a good user experience while serving recommendations?

4. Feedback Loops:

   • How do you collect user feedback on the recommendations?
   • How is this feedback used to improve the recommendation system?

5. Logging User Interactions:

   • How are user interactions with recommended videos logged?
   • What data is collected, and how is it used to improve the system?

6. Cold Start Problem:

   • How do you handle new users or videos with limited interaction history?

7. Scalability and Performance:

   • How do you ensure the system can handle a large number of users and videos?
   • What infrastructure or technologies can be used to scale the system?

Example

Consider a user who has watched several videos on a specific topic. The system should be able to recommend similar videos or related content based on the user's preferences and interactions.

Constraints

• The system should be able to handle a large number of users and videos.
• The recommendation should be personalized for each user based on their interaction history.
• The system should be able to learn and improve over time based on user feedback.

Hints

• Consider using collaborative filtering, content-based filtering, or hybrid approaches for generating recommendations.
• Explore different ranking algorithms, such as matrix factorization or deep learning models.
• Think about how to design feedback loops to collect user interactions and improve the system.

Solution

A possible solution involves the following steps:

1. Data Collection: Collect user interaction data, such as watch history, likes, and comments.
2. Candidate Generation: Use collaborative filtering or content-based filtering to generate candidate videos.
3. Ranking: Apply a ranking algorithm, such as matrix factorization or a deep learning model, to rank the candidate videos.
4. Serving: Serve the top-ranked videos to users, considering factors like user context and device.
5. Feedback Loops: Collect user feedback on the recommendations and use it to update the model.
6. Cold Start: Use content-based filtering or hybrid approaches to handle new users or videos with limited interaction history.
7. Scalability: Use distributed computing frameworks, such as Apache Spark or TensorFlow, to scale the system.

This solution provides a high-level overview of designing a video recommendation system for Reddit, focusing on candidate generation, ranking, serving, and feedback loops, as well as logging user interactions to improve the system over time.