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 362 878
arts&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. Context
similar to target advertising (?)
domain knowledge in people mobility

6. On people mobility (from the literature)
distance matters
likes 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 u
lui is ranking obtained from item-based CF algorithm.

8. pgo
#venues visited by user u
pgo =
total #venues

9. pclose
1
pclose = k1 α
dui

10. pclose
1
pclose = k1 α
dui

11. pclose
1
pclose = k1 α
dui
Category α
food 1.64
nightlife 1.61
travel (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 · plike
Naive Bayesian
Bayesian
Linear Regression

13. Results

14. Results

15. Results

16. Results
1.0
p_go
p_close
p_like
0.8
Naive
Bayesian
Linear Reg.
0.6
accuracy
0.4
0.2
0.0
Arts.and.Ent. Education Food HomeWork Nightlife Parks Shops Travel

17. Discussion
scalability
cold start situation

18. When it does not work

19. When It Does not Work

20. When It Does not Work

21. Final Remarks
results depend on venue category (different α and predictability)
geographic closeness plays a very important role.
domain knowledge significantly improves recommendations
results.

22. “Understanding the specifics of your domain
is critical to building a good recommender”
Paul Lamere @ recsys’12

23. Questions?