12 June 2015, Free University of Bozen-Bolzano. Building Large Scale Recommender Systems. Omar Moling

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1 12 June 2015, Free University of Bozen-Bolzano Building Large Scale Recommender Systems Omar Moling

2 AGENDA Intro Data Algorithms Systems

3 42MATTERS FACTS Founded in 2011 in Zurich, Switzerland Very strong Data Science expertise Currently 11 employees Backed by international VCs Top-Tier Customers

4 RECOMMENDATION PROBLEM Millions of apps Different countries and languages Many categories Different users Different devices Long-Tail problem

5 AGENDA Intro Data Algorithms Systems

6 APPS Field title description - up to 35 languages Example Google+ Get into what you love.. average rating 4.17 number of ratings 1,787,182 download range 1-5 billion icon screenshots

7 DEVICES Field brand, model installed apps with timestamps screen locale language, country available storage geo Example Samsung, Galaxy S6 [list of apps] 5.1, 2560x1440 en, US 5.6GB country, region, city

8 USER FEEDBACK no per-user ratings no per-user download statistics implicit feedback: impressions, clicks, installations timespan of installation [app usage]

9 BIG DATA 150M current app installations + historic changes 500k impressions a day

10 AGENDA Intro Data Algorithms Systems

11 OVERVIEW We use a variety of algorithm techniques From CF and CB to top trending lists and more Last step: make sure the user can install the app (size, SDK, language, country etc.)

12 LOCATION SPECIFICITY Custom implementations of algorithms to understand which apps are relevant in a location In Bolzano, you might be more interested in South Tyrol fahrplan.bz South Tyrol Suggests while you might be less interested in ZVV Timetable SBB Mobile

13 CONTENT-BASED We analyze the description of apps, just like search engines analyze content on websites (NLP, LDA) Extracted ~500 detailed app categories 45 Google Play app categories were too broad

14 MOBILE USER PROFILE GENDER: Female Our precision for gender inference is above 95% for most users. AGE: Young Adult We infer among 5 different age groups: teenager, young adult, adult, middle age, senior. 30 INTERESTS: Fitness Enjoys doing fitness and workouts. Singles Interested in dating and meeting new people. Value Shopper Interested in deals, coupons and price comparison. And more We are able to accurately identify 29+ interests from your users. Full list:

15 AGENDA Intro Data Algorithms Systems

16 SCALING Personalized recommendations We need data from each user We compute recommendations for each user How to scale from 10 to 10,000,000 users?

17 TECHNOLOGIES

18 ONLINE VS. OFFLINE Online can use recent data needs to be fast, real-time = no complex computations Offline can run complex algorithms loading tons of data cannot react to quick changes in the data

19 NEARLINE ONLINE A layer between Online and Offline which allows a trade-off between complex computations and data freshness OFFLINE

20 RECOMMENDATION PIPELINE ONLINE NEARLINE OFFLINE apps impressions installs clicks Event distribution ETL Offline algorithms SDK / S2S user events, caching SQS apps, devices, offline results S3 Recommendation Engine Model Building recommendations

21 ONLINE / NEARLINE SERVING ONLINE NEARLINE SDK / S2S autoscaling NEARLINE / OFFLINE autoscaling

22 SYSTEM FALLBACK What if all DBs fail but serving layer is still up? Serve previously computed lists from S3 user events, caching SQS Recommendation Engine

23 HOW TO DEPLOY TO MANY SERVERS? AWS provides simple rolling updates Set how many instances you update at once

24 TESTING Test your code Still, data is difficult to test Monitor its quality on production

25 MONITORING Consumption Quality

26 OPTIMIZATIONS 1/4 Load only the data you need On avg. recommended apps have 10 translations Description up to 4000 characters If you need only 100 characters, load only 100

27 EXAMPLE Facebook, for a high-traffic customer: < instead of loading this we load this > Savings: -98% data to load on avg. -90%

28 OPTIMIZATIONS 2/4 pre-aggregate data that belongs together regarding a user, we get data about device, impressions, clicks aggregate them and load at once, saving DB time

29 OPTIMIZATIONS 3/4 Embrace redundancy Storage is relatively cheap Optimize loading times for each use case Copy the data in the optimal format for each

30 OPTIMIZATIONS 4/4 Key-value: when serving, point queries, you know what you want to get No schema: flexibility, just append data to objects Offline: need all the data, using Redshift and PostgreSQL

31 APP RECOMMENDATIONS Recommended app. Hint about the next recommendation. Scroll down to see the details. Explanation on why we recommend the app.

32 BUSINESS MODEL? Usually per volume Number of requests per user per month Gets cheaper the more users/requests :)

33 THANKS!