Building a Location Based Social Graph in Spark at InMobi-(Seinjuti Chatterjee and Ian Anderson, InMobi)
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This document discusses building location-based social groups using Spark at InMobi. It begins with an overview of InMobi and their data collection through mobile ads. It then discusses using location data, point of interest classification, and connected components analysis in Spark to identify frequent co-visitations between locations and group them into location-based social graphs. Examples of identified groups include university students, business travelers at airports, and people who frequently visit stores and coffee shops near their work. The document concludes by noting InMobi's migration to using Spark for more of their data processing needs.
Building a Location Based Social Graph in Spark at InMobi-(Seinjuti Chatterjee and Ian Anderson, InMobi)
- 1. Building a Location Based Social Graph in Spark at InMobi Seinjuti Chatterjee Ian Anderson
- 2. Talk Agenda • InMobi background • Privacy • Location Service • Data collection • Location based social groups • Static • Dynamic • Spark at InMobi
- 3. InMobi: Engaging 1bn users across the globe
- 5. Rich MediaBanner Video Interstitial Native Example Mobile Ads
- 8. Ad Request Data Enrichment / Processing Ad Auction Serve Ad InMobi SDK and Data Collection Target behaviours not users
- 9. Hadoop / Pig with Falcon / Oozie scheduling HDFS store Spark - Standalone Scheduler Daily snapshots Classified data per geo region Hadoop and Spark Setup Feature generation pipeline hRaven monitoring Decision tree generation Connected components generation Location based app similarity
- 10. Social Groups • “A social group within social sciences has been defined as two or more people who interact with one another, share similar characteristics, and collectively have a sense of unity.” - http://en.wikipedia.org/wiki/Social_group Uni. Friends 49ers fans Film club Work Friends Car club
- 11. Location-Based Social Groups • Focus on location as the group membership criteria • Shared behaviours • Location can indicate social connection e.g. 49ers fans at Levi’s Stadium • San Francisco example • We want to reach a group of people that travel into San Francisco for business purposes • Visual demonstration • Implementation walkthrough
- 12. Identifying POIs
- 13. Location Data
- 16. POI Classifier “Given a geographic location e.g the United States, United Kingdom, India we want to understand the underlying nature of the location, in order to enrich the context of the ad request at the time of origin” • Types of POI can be Public Terminal, Hotel or Inn or B&B, Eatery, Sports Center, Community Center, Academic Institution, Retail Store. • POI classification helps profile user as a commuter or a frequent flyer or a university student or frequent business traveller or avid retail therapist • We have trained a Decision Tree Model on labelled data of POI visitation frequency patterns which gives us upto 70% accuracy now for predicting a POI type for a location. • The final classifier was trained on 70K labelled locations and used to label 500K locations and public wifi’s
- 17. Average merit Average rank Attribute 1.331 +/- 0.003 1.0 +/- 0.0 normalized ssid groupsize 0.251 +/- 0.006 2.1 +/- 0.3 avg hour spread 0.241 +/- 0.001 2.9 +/- 0.3 ratio of avg weekend daytime adreq to avg weekend nighttime adreq 0.227 +/- 0.001 4.0 +/- 0.0 ratio of weekend early hours to weekend adreq 0.224 +/- 0.001 5.2 +/- 0.4 ratio of weekend late hours to weekend adreq 0.222 +/- 0.002 5.8 +/- 0.4 ratio of weekend lunch hours to weekend adreq 0.208 +/- 0.002 7.0 +/- 0.0 percentage of devices who visited at least 1 uniq days 0.202 +/- 0.001 8.0 +/- 0.0 ratio of avg weekday daytime adreq to avg weekday nighttime adreq 0.194 +/- 0.001 9.0 +/- 0.0 ratio of avg weekday to weekend adreq 0.184 +/- 0.001 10.0 +/- 0.0 ratio of weekday lunch hours to weekday adreq 0.178 +/- 0.001 11.0 +/- 0.0 ratio_of_weekend_breakfast_hours_to_weekend_adreq 0.172 +/- 0.001 12.0 +/- 0.0 percentage_of_devices_who_visited_atleast_2_uniq_days Feature Ranking using Information Gain
- 18. Spark Implementation • Number of Data Points = 69,465 Num Classes = 7 • Test-Train Split = 30-70% • Impurity=”gini”, maxDepth=30, maxBins=32, numTrees=13, featureSubsetStrategy=”auto” • DecisionTree Classifier with holdout set • RandomForest Classifier with holdout set • More tuning and 10 fold cross validation is required
- 19. TP Rate FP Rate Precision Recall F-Measure Class 0.54% 0.05% 0.54% 0.54% 0.54% UNIVERSlTY_OR_COLLEGE 0.71% 0.11% 0.72% 0.72% 0.71% lNN_AND_HOTELS 0.70% 0.15% 0.71% 0.71% 0.70% EATERY 0.29% 0.08% 0.28% 0.28% 0.29% COMMUNlTY_CENTER 0.60% 0.06% 0.60% 0.60% 0.60% STORE 0.24% 0.05% 0.23% 0.24% 0.24% SPORTS_AND_FlTNESS 0.19% 0.00% 0.20% 0.19% 0.19% AlRPORT_BUS_RAlL_TERMlNAL Detailed Accuracy by Class DecisionTreeModel classifier of depth 30 with 22351 nodes Decision Tree Model Results: Spark
- 20. a b c d e f g <- classified as 1100 189 174 375 90 88 4 a = UNIVERSlTY_OR_COLLEGE 201 4154 648 304 269 217 22 b = lNN_AND_HOTELS 208 633 5047 463 451 364 23 c = EATERY 335 284 389 581 158 226 9 d = COMMUNlTY_CENTER 103 246 433 157 1561 94 17 e = STORE 95 228 342 213 74 301 6 f = SPORTS_AND_FlTNESS 6 23 27 17 8 4 20 g = AlRPORT_BUS_RAlL_TERMlNAL Decision Tree Model Results: Spark Confusion Matrix
- 21. TP Rate FP Rate Precision Recall F-Measure Class 0.64% 0.03% 0.73% 0.68% 0.64% UNIVERSITY_OR_COLLEGE 0.82% 0.09% 0.77% 0.80% 0.82% INN_AND_HOTELS 0.85% 0.21% 0.68% 0.75% 0.85% EATERY 0.26% 0.04% 0.42% 0.32% 0.26% COMMUNITY_CENTER 0.60% 0.03% 0.77% 0.68% 0.60% STORE 0.18% 0.03% 0.31% 0.23% 0.18% SPORTS_AND_FITNESS 0.30% 0.00% 0.83% 0.44% 0.30% AIRPORT_BUS_RAIL_TERMINA L Detailed Accuracy by Class TreeEnsembleModel classifier with 13 trees, Test Error = 0.308 Random Forest Results: Spark
- 22. a b c d e f g <- classified as 1303 167 251 202 53 52 0 a = UNIVERSlTY_OR_COLLEGE 47 4747 721 69 113 97 4 b = lNN_AND_HOTELS 51 537 6060 178 141 168 0 c = EATERY 327 259 632 506 109 130 2 d = COMMUNlTY_CENTER 37 170 695 94 1587 48 0 e = STORE 23 231 568 151 38 228 0 f = SPORTS_AND_FlTNESS 1 21 29 8 11 1 30 g = AlRPORT_BUS_RAlL_TERMlNAL Random Forest Results: Spark Confusion Matrix
- 23. Weka Implementation Trained a Decision Tree Model in WEKA • Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2 • Relation: us.publicPOI.classification • Filter: weka.filters.supervised.instance.SMOTE • Instances: 69465 • Attributes: 28 • Number of Leaves : 6377 • Size of the tree : 12753
- 24. TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.671 0.043 0.628 0.671 0.649 0.815 UNIVERSlTY_OR_COLLEGE 0.805 0.089 0.776 0.805 0.790 0.873 lNN_AND_HOTELS 0.802 0.113 0.788 0.802 0.795 0.859 EATERY 0.333 0.060 0.368 0.333 0.350 0.672 COMMUNlTY_CENTER 0.786 0.025 0.820 0.786 0.803 0.887 STORE 0.234 0.040 0.269 0.234 0.250 0.617 SPORTS_AND_FlTNESS 0.405 0.001 0.599 0.405 0.484 0.757 AlRPORT_BUS_RAlL_TERMlNAL Detailed Accuracy by Class Decision Tree Model Results: Weka
- 25. a b c d e f g <- classified as 4500 562 478 768 158 226 10 a = UNIVERSlTY_OR_COLLEGE 517 15458 1569 765 292 580 31 b = lNN_AND_HOTELS 486 1673 19145 1144 497 902 11 c = EATERY 1229 957 1219 2194 315 643 26 d = COMMUNlTY_CENTER 169 443 653 363 6849 230 7 e = STORE 257 737 1195 703 219 953 4 f = SPORTS_AND_FlTNESS 12 78 47 28 18 12 133 g = AlRPORT_BUS_RAlL_TERMlNAL Confusion Matrix Decision Tree Model Results: Weka Model built with 10 fold cross validation
- 26. Model Results Comparison Decision Tree Spark Random Forest Spark Baseline Decision Tree Weka Weighted F-measure 0.618 0.676 0.705 Weighted Precision 0.620 0.676 0.702 Weighted Recall 0.616 0.693 0.709 Weighted TPR 0.616 0.693 0.709 Weighted FPR 0.098 0.109 0.079 Test Error Rate 0.384 0.307 0.290 Hotel and Inn F-measure 0.71% 0.79% 0.79% University/College F-measure 0.54% 0.68% 0.65% Eatery F-measure 0.70% 0.76% 0.79% Store F-measure 0.60% 0.76% 0.80% Decision Tree Spark Random Forest Spark Baseline Decision Tree Weka Input Training Sample SIze 70K 70K 70K Time taken to build model 2 mins 2 mins 35.69 seconds Resource Usage Single Node Cluster Single Node Cluster Single Node Cluster Scalability YES YES NO Parallelism YES YES NO Accuracy 60% 69% 70%
- 29. Visualization: University / College
- 32. • A connected component is a set of locations which have been frequently co-visited by users over a month. • Conceptually it is a subgraph of frequent visitation trends which transforms into a profile of the user. • 5265 connected components generated for 576093 locations in 2 hours where each location has seen on an average 4 devices. Examples: • University students who like eating @BuffaloWing • Frequent business travellers to SFO who stay at hotels and rent a car Connected Component
- 33. Spark Implementation L(1) L(2) L(3) SSID: shopwifi_67 GPS: 37.21, -122.43 Label: STORE L(1) L(2) L(3) Days revisited: 7 Avg. hour spread: 1 Req. frequency: 0.1 1. Create vertices 2. Create edges 3. Run connected component algorithm 4. Rank users 1 user covisited 2userscovisited ● Connection strength given by number of edges (ie. common users) between L(i) and L(j) ● Cluster locations sorted by component size ● Rank the users per connected component ● Profile the user with the profile of the connected component
- 34. University, Eateries, Target Store Connected Component
- 35. Students, Coffee Shops, Library Connected Component
- 36. Frequent Business Travellers to SFO Connected Component
- 37. Frequent Business Travellers to LAX Connected Component
- 38. University Students General Population Category 0.01% 6.65% Productivity 0.14% 5.77% Health & Fitness 0.01% 4.44% Lifestyle 3.14% 4.26% Entertainment 0.00% 3.33% News 0.03% 2.91% Reference 9.55% 2.42% Tools 4.39% 0.28% Social 5.26% 0.21% Media & Video Using classifications: App Usage
- 39. Using classifications: App Usage
- 40. Using classifications: App Usage
- 41. • Migration towards spark as the runtime of choice for data processing • Legacy Pig jobs being switched to the Spark backend using Pig on Spark • Pure MR applications being rewritten to use the Spark Java API Wider Spark Usage at InMobi
- 42. Special Thanks to Paul Duff, Senior Research Scientist Thank you & Questions