Graph Walks & Vector Embeddings: Exploiting the head and exploring the tail
Pinterest has the world’s largest catalog of human curated ideas. We’re building a visual discovery engine with 100+ billion ideas, collected by 175+ million people worldwide. As we work to match the right Pin to the right person at the right time, personalization is crucial. Random graph walks with restart are an excellent way to surface popular, high quality, relevant content. But we can also show you great ideas you may not even have known you were looking for - and that’s where vector embedding comes in. We embed you and these billions of ideas in a 128 or 256 dimensional space. Then we project them down into 1000 bits, cut them up into 16 bit chunks, index these chunks, and then find these ideas for you really fast using core search technology. Bio Brian joined Pinterest in 2017 as the Head of Knowledge. He was previously at eBay, Handspring, Excite@Home, Synopsys, and AT&T Bell Labs. Brian received his Ph.D. in Computer Science from the University of Maryland. His original Treemap data visualization paper has been cited thousands of times.
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Graph Walks & Vector Embeddings: Exploiting the head and exploring the tail
- 1. Help people discover, save and do things they love Brian Johnson Machine Learning Innovation Summit San Francisco, June 5 2017
- 8. GraphWalk Boards Pins How to recommend for this pin? Random walks from a node = Personalized PageRank. More connections = higher score.
- 11. SeedPin
- 12. ContentDistribution Majority of impressions Impressions Majority of content. Pins
- 13. RichGetRicher
- 17. VectorEmbeddings Word2Vec Youshallknowawordbythecompanyitkeeps(Firth,J.R.1957) Word2Vec Word2Vec Container Sentence Pin Item Word Word (n-gram from pin description, url, source page) Context Surrounding words Bag of words
- 18. TextEmbedding
- 19. PinEmbedding
- 20. LocalitySensitiveHashing Cosine similarity: 0.05 Bits: 1 out of 3 bits matches 100 010 (-1.0, 1.2) (1.3, -0.7)
- 23. UserModeling Users closeups, clicks, clickthroughs, repins
- 24. CanWePredicttheFuture… Let’s walk through a single user’s boards…
- 33. Graph Walks & Vector Embeddings: Exploiting the head and exploring the tail Pinterest has the world’s largest catalog of human curated ideas. We’re building a visual discovery engine with 100+ billion ideas, collected by 175+ million people worldwide. As we work to match the right Pin to the right person at the right time, personalization is crucial. Random graph walks with restart are an excellent way to surface popular, high quality, relevant content. But we can also show you great ideas you may not even have known you were looking for - and that’s where vector embedding comes in. We embed you and these billions of ideas in a 128 or 256 dimensional space. Then we project them down into 1000 bits, cut them up into 16 bit chunks, index these chunks, and then find these ideas for you really fast using core search technology. Brian joined Pinterest in 2017 as the Head of Knowledge. He was previously at eBay, Handspring, Excite@Home, Synopsys, and AT&T Bell Labs. Brian received his Ph.D. in Computer Science from the University of Maryland. His original Treemap data visualization paper has been cited thousands of times.
- 35. Onlinegraphtraversal “Pixie”systemfrom AppliedScienceteam-to bepublishedsoon. Random walk, 100K+ steps <50ms 26 27 30 29 28 31 14 15 16 17 18 13 25 24 23 2221 20 19 1 2 3 4 5 6 7 8 9 10 11 12 Pin-board graph
- 36. WewantsimilarPins Query Pin Few Several Many Board Co-occurrences Low Medium High Similarity