Uploaded byShannon Chan
Machine Learning @ Carousell
Machine learning is being used at Carousell in several ways to improve the user experience. Category and title suggestions using convolutional neural networks help users list items faster and improve listing quality. Chat reply suggestions apply dot product ranking and recurrent neural networks to previous messages to suggest relevant responses. These models are trained on Google Cloud and deployed using TensorFlow and Kubernetes to provide fast, efficient predictions at scale.
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Machine Learning @ Carousell
- 1. Machine Learning at Carousell ShannonChan Data Scientist
- 2. THE CAROUSELL APP •SNAP, LIST, SELL List your item for sale in 30 seconds • IN-APP CHAT Chat directly with sellers without revealing personal information • IMAGE-CENTRIC Main focus of the app on images • SOCIAL FEATURES Share listings on social media channels & join groups of people with similar interests
- 3. 110 million listings 15+ Avg Mins onthe app for active users 43 million Sold 19 Cities 7 Countries Largest and fastest growing mobile classifieds in the region
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- 6. CLASSIFIEDS 3.0: MOBILE-FIRST WHATCAROUSELL PIONEERED
- 7. CLASSIFIEDS 4.0: AI-FIRST WHATCAROUSELL IS BUILDING AND PIONEERING
- 8. Machine Learning forUser Experience Buying Experience Chat reply, Recommender system, Search Selling Experience Category, Title suggestions Trust & Safety Bad actor, Spam detections
- 9. Category & TitleSuggestions Buying Experience Chat reply, Recommender system, Search Selling Experience Category, Title suggestions Trust & Safety Bad actor, Spam detections
- 10. Category & TitleSuggestions
- 11. Category & TitleSuggestions
- 12. Title Suggestion: Rankingof whitelisted titles Samsung galaxy edge Red dress Forever 21 skirt PS3 Slim 500GB iPhone X Wine cooler iPhone case Toyota Wish Samsung galaxy edge Red dress Forever 21 skirt PS3 Slim 500GB iPhone X Wine cooler iPhone case Toyota Wish
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- 14. Dot Product Ranking ImageTitle xy score = x . y N-gram embeddings CNN Image Title
- 15. Dot Product Ranking 0.1 0.9 0.2 ... 0.5 Imagevector “Forever 21 skirt” vector “iPhone X” vector 0.1 0.9 0.2 ... 0.5 0.1 0.9 0.2 ... 0.5 Image . “Forever 21 skirt” = -0.7 Image . “iPhone X” = 0.9
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- 17. Dot Product Ranking Whitelistof titles x scores = x . Y precom puted vectors Y Image CNN
- 18. Category Suggestion ImageTitle x y score= x . y N-gram embeddings CNN category prediction softmax loss
- 19. Visualizing the sharedimage / title space
- 20. Visualizing the sharedimage / title space
- 21. Vector Direction red direction France+ (Berlin - Germany) = Paris capital direction
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- 23. Training and Deployment WriteML models using TensorFlow Train models using Google Cloud ML Engine Deploy models on Google Kubernetes Engine
- 24. Category & TitleSuggestions Fast training iteration Trained with Google Cloud ML for 2 days No manual labelling User-written titles as labels Performance Train on tens of millions of images Efficient inference Single pass with one matrix operation
- 25. Category & TitleSuggestions - Results ● ~10s faster in time to list on average ● Improved listing completion rate ● Less wrongly categorized listings
- 26. Chat Reply Suggestions Buying Experience Chatreply, Recommender system, Search Selling Experience Category, Title suggestions Trust & Safety Bad actor, Spam detections
- 27. General Chat ReplyModel
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- 29. Chat Reply Suggestions Whitelistof messages x scores = x . Y precom puted vectors Y Previous Messages N-gram embeddings Extension of Dot-Product model with Recurrent Neural Network RNN
- 30. Question Answer Model Whitelistof messages x scores = x . Y precom puted vectors Y Previous Messages - Sequence Examples - Bi-directional RNNs - Hash Embeddings - Attention layers N-gram embeddings RNN
- 31. References (1) Efficient NaturalLanguage Response Suggestion for Smart Reply https://arxiv.org/abs/1705.00652 Authors: Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil (2) Question-Answer Selection in User to User Marketplace Conversations https://arxiv.org/abs/1802.01766 Authors: Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, Lucas Ngoo
- 32. Future Directions Personalized Feed; Pricing Suggestion ; Caroulens ; Carousell Assistant ;
- 33. We’re hiring! (1) DataScientist , SG (Ads Ranking) (2) Business Intelligence Intern , SG Check out careers.carousell.com
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