"Semantic Similarity & Taxonomic Distance: Using Structured Metadata in Data Science Models", Andrew Clegg, Data Scientist at Etsy
"Semantic Similarity & Taxonomic Distance: Using Structured Metadata in Data Science Models", Andrew Clegg, Data Scientist at Etsy Watch videos from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf https://www.youtube.com/c/DataNatives Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS About the Author: Andrew joined Etsy in 2014, and lives in London, making him their first data scientist outside the USA. Since then he has worked on a variety of challenges including localized recommendations, image similarity search, and anomaly detection. Prior to Etsy, he spent almost 15 years designing machine learning workflows, and building search and analytics services, in academia, startups and enterprises, and in an ever-growing list of research areas including biomedical informatics, computational linguistics, social media analytics, and educational gaming. These days he’s interested in probabilistic algorithms and data structures, online learning, deep learning, data visualization, and the convergence of search and recommender systems. He can count to over 1000 on his fingers but doesn’t know how to drive a car.
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"Semantic Similarity & Taxonomic Distance: Using Structured Metadata in Data Science Models", Andrew Clegg, Data Scientist at Etsy
- 1. Andrew Clegg Data Natives, Berlin, 2016
- 2. Semantic Similarity and Taxonomic Distance: Using Structured Metadata in Data Science Models
- 8. digital_prints music_and_movie_posters (0.84) digital_prints digital_prints (1.00) digital_prints lithographs (0.79) lens_cases lens_cases (1.00) lens_cases camera_cases (0.92) lens_cases laptop_bags (0.77) True Label Prediction / Score 8
- 9. Before After
- 11. All Items Shoes Boots Sneakers & Athletic Shoes Hi Tops Sandals SkatesTie Sneakers Path Length sim(node1, node2) = 1 len(node1, node2) 2 ⇥ max depth
- 12. home_and_living 1 kitchen_and_dining 2 cookware 3 pots_and_pans 4 pans 5 skillets 6 Node Depth 12
- 13. All Items Shoes Boots Sneakers & Athletic Shoes Hi Tops Sandals SkatesTie Sneakers Wu & Palmer 1994 sim(node1, node2) = 2 ⇥ depth(ancestor) len(node1, node2) + 2 ⇥ depth(ancestor)
- 14. All Items Shoes Boots Sneakers & Athletic Shoes Hi Tops Sandals SkatesTie Sneakers Sussna 1993 0.17 0.17 0.11 dist(parent, child) = 1 1 ÷ num children(parent) 2 ⇥ depth(child)
- 16. How frequent is this node or any of its descendants in your data? Information Content 16 I(node) = log P(node) | {z }
- 17. All Items Shoes Boots Sneakers & Athletic Shoes Hi Tops Sandals SkatesTie Sneakers Resnik 1995 sim(node1, node2) = log P(ancestor) P(shoes) = 0.14 -log P(shoes) = 2.83
- 18. All Items Shoes Boots Sneakers & Athletic Shoes Hi Tops Sandals SkatesTie Sneakers Lin 1998 sim(node1, node2) = 2 ⇥ log P(ancestor) log P(node1) + log P(node2) P(shoes) = 0.14 -log P(shoes) = 2.83 P(boots) = 0.04 -log P(boots) = 4.64 P(tie sneakers) = 0.03 -log P(tie sneakers) = 5.06
- 20. Thanks! See this paper for all the references: Budanitsky & Hearst, Computational Linguistics 32 (1), 2006. Find me on Twitter: @andrew_clegg PS We’re hiring! https://www.etsy.com/careers/