The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy...
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![Page 1: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/1.jpg)
The YouTube Video Recommendation System
James Davidson Benjamin Liebald Junning LiuPalash NandyTaylor Van Vleet(Google inc)
Presented by Thuat Nguyen
![Page 2: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/2.jpg)
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
![Page 3: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/3.jpg)
Goals
• The recommendation system • Find videos related to users’ interests• Helps users discover• Keep users engaged: not just to watch or find
![Page 4: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/4.jpg)
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
![Page 5: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/5.jpg)
System Design
1. Input data
2. Related videos
3. Generating recommendation candidates
4. Ranking
5. System implementation
-> recent, fresh, diverse, relevant
![Page 6: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/6.jpg)
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)
![Page 7: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/7.jpg)
Related Videos
• Relatedness score
• Normalization function
• vi -> Ri of top N candidates (impose min score)
![Page 8: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/8.jpg)
Generating Recommendation Candidates
• Seed set S• C1 is narrow
• Broad the diversity of candidate set
![Page 9: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/9.jpg)
Generating Recommendation Candidates (cont.)
![Page 10: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/10.jpg)
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
![Page 11: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/11.jpg)
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
![Page 12: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/12.jpg)
Evaluation and Results
![Page 13: The YouTube Video Recommendation System James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet (Google inc) Presented by Thuat Nguyen.](https://reader035.fdocuments.us/reader035/viewer/2022081520/5697bff71a28abf838cbe660/html5/thumbnails/13.jpg)
Questions?