Cross device algorithm
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The document discusses cross-device tracking techniques used to cluster cookies and user profiles into graphs to identify when different devices belong to the same user. It describes using features like location, time, IP addresses, and device types with machine learning classifiers to determine cookie associations across devices with high precision and recall. Challenges include limited data, anti-tracking mechanisms, and obscurity of identifying information.
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Cross device algorithm
- 1. Cross-Device Tracking June 2019 Morten Arngren Lead Data Scientist Vlad Sandulescu Senior Data Scientist Jan Kremer Data Scientist Damian Pawlowski Senior Software Engineer Sergey Gluschenko Senior Software Engineer Tomasz Perek Senior Software Engineer
- 3. 👻 time 📱 📞💻 Cookie Churn Cross-Device
- 8. Limited data set Anti-tracking mechanisms GDPR The cat and mouse game Safari Fake user agent 176 . 22 . 252 . ??? IP Obscurity
- 9. 📈data
- 11. ❓Ground Truth? External Deterministic Providers 👻 👽 Location ⏲ TimeCookie Device 📞 OS 📈data
- 12. engine room
- 13. Cross-Device Graph Cookie User-AgentIP Device Cluster cookies into user profiles. ⌖ 👽 👻 Data ⏲ 📞 Location Time Cookie Device OS
- 14. Cross-Device Graph Cookie User-AgentIP Device B nodes B edges Graph cutting algorithms • Min-Cut • Markov Random Fields • Community Detection • Graph Clustering… Production Scale B events
- 15. Cookie IP (Pre-Clustering) Divide Graph Ignored 1 2 3 n-1 n (Connected Components Algorithm) …by location 4 Scale….what to do??
- 16. by Pair-wise classification 👽👻 A B C D …with ground truth A B C D Positive Negative A C A D B C B D Create cookie pairs as classification data set E A E B E C E D E Cookie association
- 17. cookie_id Location Device device_type_id os_id browser_id browser_language_codes ip_v4 country_id region_id city_id zip_code_id log_time ID Time Crafting Features Observations for each cookie pair… A BA B Aggregation Cosine Similarity Correlation Similarity Hellinger Distance Max Overlap Similarity Pageviews 0 31 days 0 12 24 hours 0.78 0.68 0.62 0.75 0.23 0.34 0.30 0.27 24 hours 31 days
- 18. cookie_id Location device_type_id os_id browser_id browser_language_codes ip_v4 country_id region_id city_id zip_code_id log_time ID Time Crafting Features Observations for each cookie pair… A BA B set a set b Jaccard Index Similarity 0.49 1.00 0.67 0.26 0.18 ip_v4 country_id region_id city_id zip_code_id Aggregation Device
- 19. cookie_id Location device_type_id os_id browser_id browser_language_codes ip_v4 country_id region_id city_id zip_code_id log_time ID Time Crafting Features Observations for each cookie pair… A BA B 📱 ☼ Aggregation set a set b Jaccard Index Similarity 1.00 0.67 0.33 0.75 device_type_id os_id browser_id browser_language_codes ☼ ☼ 📱 Device
- 20. Crafting Features for each cookie pair… Features (http://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html) 🌴 0.78 A B Classifier Gradient Boosted Decision Trees 🌴 🌴 Principal Component 1+2 Vectorial Sets Numerical 26 dim. feature vector 1 2 3 4 5 6 7 8 26 cookie_id Location device_type_id os_id browser_id browser_language_codes ip_v4 country_id region_id city_id zip_code_id log_time ID Time Observations Device A B
- 21. (http://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html) >1 <=1 <0.5 >=0.5 <=0 >0 <=6<0.8 >=0.8 >6 >1 <=1 + + Boosted Decision Trees 🌴 🌲 12172 12410 3 F1 F4 F3 F8 F5 F2 1 + 0.78 A B
- 22. A C B D E F G A B C D E F G Classification A B C DE F G Clustering (Connected Components Algorithm) Pruning Degrees of edges 1 3 2222 2
- 23. Classification Clustering (Connected Components Algorithm) Pruning A C B D E F G A B C D E F G A B C DE F G Pre-Clustering (Connected Components Algorithm) Full Graph Components Sub-Graph A C B D E F G Clusters Users (XGBoost)
- 24. ☢challenges
- 25. One IP to rule them all…. Large pre-clusters 176 . 22 . 252 . ??? IP Obscurity Cookie IP Noisy pre-clusters Removed / Split Faced head-on…
- 26. 📈performance
- 27. time 21 days ⏲ 3M events 7 days ⏲ 1M events Present Testing -28 days 1 week 3 weeks Training
- 29. PerformanceGraphs (Preliminary YouGov data set…) (PreliminaryYouGovdataset…) 91% 32% Precision Recall F1-score 47%
- 30. PerformanceGraphs Cookie-Matchesdata (Cookie-Matches data) 94% 79% Precision Recall F1-score 86%
- 32. Scheduled tasks Training Train Classifier Model parameters Pre- Processing Pre- Clustering Predict Cross Device ID’s Cross-Device Cookies Ready XD Deterministic Data Pre- Processing Pre- Clustering XD Data Model ready S3 Kafka Predicting Serving Serve… S3 Kafka( ( ))
- 33. time 1B cookies Present Production 1 week 1 day 👤 149M 8-12 hours ⏲ 400GB
- 34. >96% >69% Precision Recall Reach <70% superior to time 8-12h
- 35. ☢challenges
- 36. Limited data set Anti-tracking mechanisms GDPR The cat and mouse game Safari Fake user agent 176 . 22 . 252 . ??? IP Obscurity
- 38. Streaming Model parameters Pre- Processing Pre- Clustering Predict Cross Device ID’s Cross-Device Cookies Ready XD Data Website categories Features Contextual Information ✈ 📹 Kafka ( ) 🚀 🎱🍔 🍸 🐦 🍂 🎧 👶 🎬 💻 🏈
- 39. thank you
- 40. Profile-cookie space Precision / Recall Estimated False Negatives (FN) Class A Class B True Negatives (TN) True Positives (TP) False Positives (FP) Recall = TP TP + FP Precision = = TP TP + FN = F1-Score = Precision • Recall 2 • Precision + Recall(Harmonic Mean)