Ads and the City

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Ads and the City: Considering Geographic Distance Goes a Long Way http://bit.ly/PbEzpD

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  • 1. Ads and the City:Considering Geographic Distance Goes a Long Way Diego Saez-Trumper1 Daniele Quercia 2 Jon Crowcroft 2 1 Universitat Pompeu Fabra, Barcelona 2 Computer Laboratory, University of Cambridge Dublin, September, 2012
  • 2. mobile social-networking sites
  • 3. Category #Venues #Users food 1,293 1,566 nightlife 1,075 1,207 travel 850 1,744 home/work/etc. 411 1,037 shops 362 878arts&entertainment 348 841 parks&outdoors 184 363 education 49 117 Total 4,572 3,110 Table: London Foursquare Data
  • 4. Given a venue, suggests guests
  • 5. Contextsimilar to target advertising (?)domain knowledge in people mobility
  • 6. On people mobility (from the literature)distance matterslikes might matter“power users” are special p(go|like, close) ∝ pgo · pclose · plike
  • 7. plike #venues visited by user u with rating lui p(like = lui |go) = total #venues visited by user ului is ranking obtained from item-based CF algorithm.
  • 8. pgo #venues visited by user upgo = total #venues
  • 9. pclose 1pclose = k1 α dui
  • 10. pclose 1pclose = k1 α dui
  • 11. pclose 1 pclose = k1 α dui Category α food 1.64 nightlife 1.61travel (airports/trainstations) 2.22 home/work/etc. 1.62 shops 1.64 arts&entertainment 1.64 parks&outdoors 1.68 education 1.93 High α → travel farther
  • 12. p(go|like, close) ∝ pgo · pclose · plikeNaive BayesianBayesianLinear Regression
  • 13. Results
  • 14. Results
  • 15. Results
  • 16. Results 1.0 p_go p_close p_like 0.8 Naive Bayesian Linear Reg. 0.6accuracy 0.4 0.2 0.0 Arts.and.Ent. Education Food HomeWork Nightlife Parks Shops Travel
  • 17. Discussionscalabilitycold start situation
  • 18. When it does not work
  • 19. When It Does not Work
  • 20. When It Does not Work
  • 21. Final Remarksresults depend on venue category (different α and predictability)geographic closeness plays a very important role.domain knowledge significantly improves recommendationsresults.
  • 22. “Understanding the specifics of your domain is critical to building a good recommender” Paul Lamere @ recsys’12
  • 23. Questions?