At Etsy, our data science team is working on delivering cutting-edge scientific solutions to for:
- Search and Discovery
- Query Understanding
- Learning to Rank
- Image Understanding
- Text Mining
- Natural Language Processing
- Personalization and Recommendation
- User Profiling
- Collaborative Filtering
- Latent Factor Models
- Ensemble Models
- Exploitation and Exploration
- Computational Advertising
- Click-Through-Rate Prediction and Conversion Rate Prediction
- Real-time Bidding
- Dynamic Price
We work on innovations to power products and develop state-of-the-art algorithms and models. We get involved in research communities by giving talks and publish high quality papers in top computer science venues. Check out our blog posts about how data science can directly impact our products.
- L. Hong. “AI in E-Commerce at Etsy” at Insights Data Science, New York City, NY, August 2017. [Slides]
- L. Hong. “AI in E-Commerce at Etsy” at Machine Learning Summit, Beijing, China, June 2017. [Slides]
- L. Hong. “Data Science at Etsy” at Department of Statistics at Columbia University, New York, NY, Dec. 2016.
- K. Aryafar. “Machine Learning as the Key to Personalized Curation” at AirBnB’s OpenAir Tech Talks, August, 2015.
- M. Nayyar. Modeling Spelling Correction for Search at Etsy. May, 2017.
- G. Fernandez-Kincade. Targeting Broad Queries in Search. July, 2015.
- F. Condon. How Etsy Uses Thermodynamics to Help You Search for “Geeky”. August, 2015.
- R. Hall. Personalized Recommendations at Etsy. November, 2014.
- J. Attenberg. Conjecture: Scalable Machine Learning in Hadoop with Scalding. June, 2014.
- K. Aryafar, D. Guillory and L. Hong. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. In AdKDD & TargetAd 2017 workshop, held in conjunction KDD 2017.
- C. Lynch, K. Aryafar, and J. Attenberg. Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank. In KDD 2016. 541-548.
- S. Zakrewsky, K. Aryafar and A. Shokoufandeh. Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors. CoRRabs/1605.03663 (2016).
- R. Hall and J. Attenberg. Fast and Accurate Maximum Inner Product Recommendations on Map-Reduce. In WWW (Companion Volume) 2015. 1263-1268.
- D. Hu and T. Schneiter. Targeted Content for a Real-Time Activity Feed: For First Time Visitors to Power Users. In WWW (Companion Volume) 2015. 1269-1274.
- D. Hu, R. Hall and J. Attenberg. Style in the long tail: discovering unique interests with latent variable models in large scale social E-commerce. In KDD 2014. 1640-1649. (Best Industrial Paper Award)
K. Aryafar, C. Lynch and J. Attenberg. Exploring User Behaviour on Etsy through Dominant Colors. In ICPR 2014. 1437-1442.