More Related Content
What's hot
What's hot (20)
Similar to Fashiondatasc
Similar to Fashiondatasc (20)
Recently uploaded
Recently uploaded (20)
Fashiondatasc
- 1. Fashion and Data Suman Bhattacharya Senior Data Scientist @ Uber
- 2. Astronomy Health Care retail social media e commerce About me 2003-2014 2014-2017 2017— Ride sharing
- 3. Fashion Industry • Rose lo bertin (July 2, 1747 – September 22, 1813) was the dressmaker named bill to Marie Antoinette, Queen of France, and a high public profile. Sometimes called sarcastically the "Minister of Fashion" Ref: Wikipedia • 1900— Golden age of fashion • $1.2 Trillion annual revenue as of 2016 • 240 Billion USA only
- 4. A new breed of fashion companies • 2005— • online only • mostly do not own the brands themselves • leverages the idea of “shared economy” • uses data and technology as a leverage
- 5. When Data meet Fashion Customer uses the app Get personalized list of items to choose from Receives a box delivered send back after usage or hold back the bought item , send the rest do it over Customer uses the app app flow data gathered user interaction data Provides feedback provides feedback ratings / experience data warehouse warehouse operations/ quality control data
- 6. Data science project cycle Business problem Exploratory data analysis build some prediction system customer interact experiment
- 7. Prediction system • Recommender system: given past preference of the customer, recommend clothes that the customer want to buy or get in the next box • Fit algorithm: predict the right size for the clothes that the customer wants to receive • Experimental methodology to test algorithms
- 8. What Makes Me Likely to Buy? Provide me products that improve my life… …found in a place convenient to me …in a store where the products are easy to find …where they are provided by friendly people …who are able to anticipate my needs …at a time convenient for me …for a price I am willing to pay*. Given our product set, which products are customers demonstrating the most interest in? Which ones are they likely to be interested in next season?
- 9. recommendation system landscape explicit (e.g. rating) implicit (e.g. click/not) feedback data algorithms factorization based e.g. SVD, latent factors neighborhood based e.g. kNN optimization based e.g. gradient descent data profile data e.g. user/ item information Bayesian recommendation System top-N e.g., predict top 10 items predict ratings e.g. for a user-item pair accuracy measure AUC/ROC precision/recall MSE end result ordered e.g., 1st>2nd unordered e.g., anywhere in top 10 ratings approach frequentist No user data: cold start problem-> Content based
- 10. The Netflix Prize Problem: predict ratings that users give to movies optimize time-SVD++ Koren 2009
- 11. data: rotten or not ratings reviews or/and + may be meta data user info: age/sex/profession/friends with item info: genre, dir, actors more available implicit feedback less available explicit feedback becoming available available relatively less available
- 12. 12 • historically, focus has been on models that predict the ratings accurately • need explicit feedback, less available, less commercial use • lately, focus is more on top-N recommendation • implicit feedback ok, much more data, more commercial interests (top 5 books , top 10 movies) end goal of the recommendation system: predict stars predict top 5 itemsor
- 13. 13 accuracy metrics: AUC/ROC if positions of items matters Precision/Recall if only top-N matters mean square error if predicting ratings
- 14. 14 algorithms: neighborhood optimization factorization collaborative filtering find items that are similar to the items used or find users that use items similar to yours, then recommend the items they have used and you haven’t kNN very fast less accurate for predicting ratings reasonable accuracy for top-N SLIM fast less accurate for predicting ratings best accuracy for top-N matrix factorization to find latent factors relatively slow best accuracy for ratings predictions less accurate for top-N Can also be used for content based recommendation
- 15. Recommendations - Signals we use 🔑🔑🔑 • Past Ratings • Purchases • Browse history • Brand, category, color & style types usage in the past. • Contexts: occasion, temperature/ weather of your location next 7 days • Find similar customers -> find what they’ve liked in the past -> if she hasn’t received it yet, we recommend it. • Need future prediction in real world! Most open source solutions are not
- 16. 16 Matrix Factorization item user 1 0 0 1 . . 1 1 0 1 . . 0 0 0 1 . . ……. ……. …….. …….. •# factors typically << # items or # of users •User -factors: users taste given the past usage •Item- factors: item attributes •We use Factorization machine for our work E.g.: collaborative filtering using implicit feedback Factors user X = Factors Items
- 17. Factorization machine Rendle 2012 temporal information: Past ratings works better in sparse data
- 18. Visual recommendation • Convolution neural net visual features Visual Features K nearest neighbor find similar items to each item
- 19. Visual recommendations • Extract features from an image to find similar products that are visually similar ~80% similar ~40% similar
- 21. Visual Recommendations • Visual recommendations based on images in her closet Her closet
- 22. Visual recommendations - extract hidden visual attributes • Similar items in other categories: Because you chose this ———————————————————————
- 23. Predicting Fit Customer style Customer Fit • for each customer find the style, and the one that fit • Past ratings- what fit/ didn’t fit, customer body measurements, garment measurements, body-to-mass index • predict for each customer, top styles that will fit Customer see this
- 24. How it works in production Data in HDFS FM training on Spark using ratings train image data using CNN in GPU k nearest neighbor additional Features combine fit and like score of each product for each user list of products users like to app Shuffle the list in real time as users interacts using Multi-Arm-Bandit train to predict fit
- 25. Data science project cycle Business problem Exploratory data analysis build some prediction system customer interact experiment
- 26. Treat data science as… science! The big question – especially with complex predictive analytics, is did it work?
- 28. Define Questio n Gather Informati on Form Hypothe sis Test Hypothe sis Analyze Results Draw Conclusi ons Publish Results Retest The Scientific Method: 5/8ths of the steps in the scientific method are about testing our hypothesis and doing something with it.
- 29. A/ B testing • simplest form of testing the causality • can have multiple treatment groups, so A/B is really A/B1/B2…/BN • randomly split traffic into two groups- A (control) and B(treatment) => e.g., random split: flip a coin to decide which one of your party guests goes to control or treatment => e.g. non-random: family members in treatment , else in control • Goal is to draw causal inference from the treatment group Feature: changed tab color
- 30. first A/ B test in recorded history • Scurvy was an epidemic in 18th century • In 1740, Anson’s circumnavigation voyage- ->1900 sailors joined -> 1400 died most likely from Scurvy • Lind’s hypothesis: Scurvy could be cured by acids, include dietary supplements of acids • Lind’s approach: divide 12 sailors into six groups of two, give everyone same diet, but supplement that group 1: cider, 2: vitriol 3: vinegar , 4: seawater, and 5: oranges and lemon, 6: barley water • Result: group 5 recovered within a week James Lind Circa 1716- 94
- 31. How to experiment? Steps Hypothesis how much desired lift with what type-I and II error split data into two groups expose treatment to one of the groups until you reach the required sample size Analyze results calculate required sample size
- 32. How to experiment? Example Hypothesis how much desired lift with what type-I and II error split data into two groups expose treatment to one of the groups until you reach the required sample size Analyze results Calculate sample size required a new feature will Increase CTR Increase CTR by 10% with 5/100 false positive 20% type 2 error need pre experiment data to calculate sample size
- 33. Limitations of A/ B testing-> MAB • cannot adaptively allocate user to treatment groups • cannot analyze results mid way through the experiment • if the lift seen is less than the desired lift , cannot confirm its significance • A/ B - explore first, then exploit • Multi arm Bandit- explore- exploit simultaneously, adaptively drive traffic towards winning variations without waiting for final results
- 34. Recommendations and MAB • machine learning models have parameters to tune, search space is big • showing similar items sorted by score often looses the discovery aspect of the recommender system • split users into 10 groups- group 1: sees 1,2,3,4,5; group 2: 1,3,5,..; group3: 1, 5, 9,… • Explore - which combinations gives the best ratings- Exploit: choose that combinations for that user • We will know for which combinations works for each users, after the explore - exploit sorted by score: 1 2 3 4 5
- 35. Takeaways • A new breed of fashion companies are changing the industry leveraging data and personalizing shoppers’ experience • Deep learning especially computer vision allow use to photos , this was previously untapped data. • Combining standard signals (ratings, clicks ) with signals from photos, improve the recommendation accuracy significantly • Experimentation is just as important as the prediction of the recommender system • Combining experimentation and recommender prediction further optimize the personalized experience of shoppers