Data Science in E-commerce
- 1. Data Science in E-commerce industry DSSP 2016/05/20 Vincent Michel Big Data Europe, BDD, Rakuten Inc. / PriceMinister vincent.michel@rakuten.com @HowIMetYourData
- 2. 2 Short Bio ESPCI: engineer in Physics / Biology ENS Cachan: MVA Master Mathematics Vision and Learning INRIA Parietal team: PhD in Computer Science Understanding the visual cortex by using classification techniques Logilab – Development and data science consulting Data.bnf.fr (French National Library open-data platform) Brainomics (platform for heterogeneous medical data) Education Experience Rakuten PriceMinister– Senior Developer and data scientist Data engineer and data science consulting
- 3. Software engineering Lessons learned from (painful) experiences
- 4. 4 Do not redo it yourself ! Lots of really interesting open-source libraries for all your needs: Test first on a small POC, then contribute/develop Scikit-learn, pandas, Caffe, Scikit-image, opencv, …. Be careful: it is really easy to do something wrong ! Open-data: More and more open-data for catalogs, … E.g. data.bnf.fr ~ 2.000.000 authors ~ 200.000 works ~ 200.000 topics Contribute to open-source: Is there a need / pool of potential developers ? Do it well (documentation / test) Unless you are doing some kind of super magical algorithm May bring you help, bug fixes, and engineers ! But it takes time and energy
- 5. 5 Quality in data science software engineering Never underestimates integration cost Really easy to write a 20 lines Python code doing some fancy Random Forests… …that could be really hard to deploy (data pipeline, packaging, monitoring) Developer != DevOps != Sys admin Make it clean from the start (> 2 days of dev or > 100 lines of code): Tests, tests, tests, tests, tests, tests, tests, … Documentation Packaging / supervision / monitoring Release often release earlier Agile development, Pull request, code versioning Choose the right tool: Do you really need this super fancy NoSQL database to store your transactions?
- 6. 6 Monitoring and metrics Always monitor: Your development: continuous integration (Jenkins) Your service: nagios/shinken Your business data (BI): Kibana Your user: tracker Your data science process : e.g. A/B test Evaluation: Choose the right metric Prediction accuracy / Precision-recall … Always A/B test rather than relying on personal thoughts Good question leads to good answer: Define your problem
- 7. Hiring remarks Finding the good data scientist
- 8. 8 Finding your data scientist Do not try to find a unicorn! Define your needs (and unicorns no longer exist…)
- 9. 9 Few remarks on hiring – my personal opinion Be careful of CVs with buzzwords! E.g. “IT skills: SVM (linear, non-linear), Clustering (K-means, Hierarchical), Random Forests, Regularization (L1, L2, Elastic net…) …” It is like as someone saying “ IT skills: Python (for loop, if/else pattern, …) Often found in Junior CVs (ok), but huge warning in Senior CVs Hungry for data? Loving data is the most important thing to check Opendata? Personal project? Curious about data? (Hackaton?) Pluridisciplinary == knowing how to handle various datasets Check for IT skills: Should be able to install/develop new libraries/algorithms A huge part of the job could be to format / cleanup the data Experience VS education -> Autonomy
- 10. Recommendations @Rakuten Data science use-case
- 11. 11 Rakuten Group Worldwide Recommendation challenges Different languages Users behavior Business areas
- 12. 12 Rakuten Group in Numbers Rakuten in Japan > 12.000 employees > 48 billions euros of GMS > 100.000.000 users > 250.000.000 items > 40.000 merchants Rakuten Group Kobo 18.000.000 users Viki 28.000.000 users Viber 345.000.000 users
- 13. 13 Rakuten Ecosystem Rakuten global ecosystem : Member-based business model that connects Rakuten services Rakuten ID common to various Rakuten services Online shopping and services; Main business areas E-commerce Internet finance Digital content Recommendation challenges Cross-services Aggregated data Complex users features
- 14. 14 Rakuten’s e-commerce: B2B2C Business Model Business to Business to Consumer: Merchants located in different regions / online virtual shopping mall Main profit sources • Fixed fees from merchants • Fees based on each transaction and other service Recommendation challenges Many shops Items references Global catalog
- 15. 15 Big Data Department @ Rakuten Big Data Department 150+ engineers – Japan / Europe / US Missions Development and operations of internal systems for: Recommendations Search Targeting User behavior tracking Average traffic > 100.000.000 events / day > 40.000.000 items view / day > 50.000.000 search / day > 750.000 purchases / day Technology stack Java / Python / Ruby Solr / Lucene Cassandra / Couchbase Hadoop / Hive / Pig Redis / Kafka
- 16. 16 Recommendations on Rakuten Marketplaces Non-personalized recommendations All-shop recommendations: Item to item User to item In-shop recommendations Review-based recommendations Personalized recommendations Purchase history recommendations Cart add recommendations Order confirmation recommendations System status and scale In production in over 35 services of Rakuten Group worldwide Several hundreds of servers running: Hadoop Cassandra APIS
- 17. 17 Challenges in Recommendations Items Catalogue Items Similarity Recommendations engine Evaluation Process Items catalogues Catalogue for multiple shops with different items references ? Items similarity / distances Cross services aggregation ? Lots of parameters ? Recommendations engine Best / optimal recommendations logic ? Evaluation process Offline / online evaluation ? Long-tail ? KPI ?
- 18. 18 Recommendations Architecture: Constantly Evolving Browsing Events Cocounts Storage Purchase Events Catalogue(s) Distributionlayer Recommendations Offline / materialized Recommendations Online algebra / multi-arm
- 19. 19 Items Catalogues Use different levels of aggregation to improve recommendations Category-level (e.g. food, soda, clothes, …) Product-level (manufactured items) Item in shop-level (specific product sell by a specific shop) Increased statistical power in co-events computation Easier business handling (picking the good item)
- 20. 20 Enriching Catalogues using Record Linkage Marketplace 2Marketplace 1 Reference database Record linkage Use external sources (e.g., Wikidata) to align markets' products Fuzzy matching of 600K vs 350K items for movies alignments usecase. Blocking algorithm Cross recommendation Global catalog Items aggregation Helps with cold start issues Improved navigation
- 21. 21 Co-occurrences and Similarities Computation Only access to unitary data (purchase / browsing) Use co-occurrences for computing items similarity Multiple possible parameters: Size of time window to be considered: Does browsing and purchase data reflect similar behavior ? Threshold on co-occurrences Is one co-occurrence significant enough to be used ? Two ? Three ? Symmetric or asymmetric Is the order important in the co-occurrence ? A then B == B then A ? Similarity metrics Which similarity metrics to be used based on the co-occurrences ?
- 22. 22 Co-occurrences Example Browsing Purchase Session ? Session ?Time window 1 Session ?Time window 2 07/11/2015 08/11/2015 08/11/2015 24/11/2015 08/11/2015 08/11/2015 10/09/201 5 08/09/201 5 10/09/201 5
- 23. 23 Co-occurrences Computation Co-purchases Co-browsing Classical co-occurrences Complementary items Substitute items Other possible co-occurrences Items browsed and bought together Items browsed and not bought together “You may also want…” “Similar items…” 08/11/2015 08/11/2015 08/11/2015 07/11/2015 08/11/201510/09/201 5 08/09/201 5 07/11/2015
- 24. 24 Recommendation Quality Challenges Recommendations categories Cold start issue • External data ? • Cross-services ? Hot products (A) • Top-N items ? Short tail (B) Long tail (C + D) Minor Product Major Product (Popular) New Product Old Product (A) (B) (D) (C)
- 25. 25 Long Tail is Fat Long tail numbers • Most of the items are long tail • They still represent a large portion of the traffic Long tail approaches • Content-based • Aggregation / clustering • Personalization Popula r Short tail Long tail Browsing share Number of items Long tail Short tail Popular
- 26. 26 Recommendations Offline Evaluation Pros/Cons • Convenient way to try new ideas • Fast and cheap • But hard to align with online KPI Approaches • Rescoring • Prediction game • Business simulator
- 27. 27 Public Initiative – Viki Recommendation Challenge 567 submissions from 132 participants http://www.dextra.sg/challenges/rakuten-viki-video-challenge
- 28. 28 Datascience everywhere ! Rakuten provides marketplaces worldwide Specific challenges for recommendations Items catalogue: reinforce statistical power of co-occurrences across shops and services; Items similarities: find the good parameters for the different use- cases; Recommendations models: what is the best models for in-shop, all-shops, personalization? Evaluation: handling long-tail? Comparing different models?
- 29. 29 THANKS ! Questions ? More on Rakuten tech initiatives http://www.slideshare.net/rakutentech http://rit.rakuten.co.jp/oss.html http://rit.rakuten.co.jp/opendata.html Positions • http://global.rakuten.com/corp/careers/bigdata/ • http://www.priceminister.com/recrutement/?p=197
- 30. 30 We are Hiring! Big Data Department – team in Paris http://global.rakuten.com/corp/careers/bigdata/ http://www.priceminister.com/recrutement/?p=197 Data Scientist / Software Developer Build algorithms for recommendations, search, targeting Predictive modeling, machine learning, natural language processing Working close to business Python, Java, Hadoop, Couchbase, Cassandra… Also hiring: search engine developers, big data system administrators, etc.