The YouTube Video Recommendation System

James Davidson Benjamin Liebald Junning LiuPalash NandyTaylor Van Vleet(Google inc)

Presented by Thuat Nguyen

Introduction

• YouTube – the most popular video community

• 1 billion users watch each month

• 24 hours of video uploaded every minute (2010)

• It’s a very information-rich environment

Goals

• The recommendation system • Find videos related to users’ interests• Helps users discover• Keep users engaged: not just to watch or find

Challenges

• Videos have no or poor metadata

• User interactions are relatively short and noisy

(compared to Netflix or Amazon)

• Videos usually have short life cycle

System Design

1. Input data

2. Related videos

3. Generating recommendation candidates

4. Ranking

5. System implementation

-> recent, fresh, diverse, relevant

Input Data

• Two main classes of data:

1. Content data

• Title, description…

2. User activity data

• Rating, liking, subscribing, etc. (explicit)

• Start to watch, close before finish (implicit)

Related Videos

• Relatedness score

• Normalization function

• vi -> Ri of top N candidates (impose min score)

Generating Recommendation Candidates

• Seed set S• C1 is narrow

• Broad the diversity of candidate set

Generating Recommendation Candidates (cont.)

Ranking

• Candidates ranked by using categorized signals:

• Video quality (view count, ratings…)

• User specificity (user’s taste and preferences)

• Diversification

• Impose constraints for each seed

System Implementation

• Three main steps:

• Data collection (log files)

• Recommendation generation (MapReduce)

• Recommendation serving

• Batch-oriented pre-computation approach

• Take advantages of CPU resources

• Cause delay between generating and serving

Evaluation and Results

Questions?