Ads and the City:Considering Geographic Distance Goes a              Long Way Diego Saez-Trumper1         Daniele Quercia ...
mobile social-networking sites
Category        #Venues    #Users        food          1,293      1,566      nightlife       1,075      1,207       travel...
Given a venue, suggests guests
Contextsimilar to target advertising (?)domain knowledge in people mobility
On people mobility (from the literature)distance matterslikes might matter“power users” are special             p(go|like,...
plike                         #venues visited by user u with rating lui   p(like = lui |go) =                             ...
pgo        #venues visited by user upgo =             total #venues
pclose               1pclose = k1     α              dui
pclose               1pclose = k1     α              dui
pclose                           1            pclose = k1     α                          dui           Category           ...
p(go|like, close) ∝ pgo · pclose · plikeNaive BayesianBayesianLinear Regression
Results
Results
Results
Results           1.0                                                                                           p_go      ...
Discussionscalabilitycold start situation
When it does not work
When It Does not Work
When It Does not Work
Final Remarksresults depend on venue category (different α and predictability)geographic closeness plays a very important ...
“Understanding the specifics of your domain is critical to building a good recommender”         Paul Lamere @ recsys’12
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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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Ads and the City

  1. 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. 2. mobile social-networking sites
  3. 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. 4. Given a venue, suggests guests
  5. 5. Contextsimilar to target advertising (?)domain knowledge in people mobility
  6. 6. On people mobility (from the literature)distance matterslikes might matter“power users” are special p(go|like, close) ∝ pgo · pclose · plike
  7. 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. 8. pgo #venues visited by user upgo = total #venues
  9. 9. pclose 1pclose = k1 α dui
  10. 10. pclose 1pclose = k1 α dui
  11. 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. 12. p(go|like, close) ∝ pgo · pclose · plikeNaive BayesianBayesianLinear Regression
  13. 13. Results
  14. 14. Results
  15. 15. Results
  16. 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. 17. Discussionscalabilitycold start situation
  18. 18. When it does not work
  19. 19. When It Does not Work
  20. 20. When It Does not Work
  21. 21. Final Remarksresults depend on venue category (different α and predictability)geographic closeness plays a very important role.domain knowledge significantly improves recommendationsresults.
  22. 22. “Understanding the specifics of your domain is critical to building a good recommender” Paul Lamere @ recsys’12
  23. 23. Questions?
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