Date post: | 21-Jun-2015 |
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Ads and the City:Considering Geographic Distance Goes a
Long Way
Diego Saez-Trumper1 Daniele Quercia 2 Jon Crowcroft 2
1Universitat Pompeu Fabra, Barcelona2Computer Laboratory, University of Cambridge
Dublin, September, 2012
mobile social-networking sites
Category #Venues #Usersfood 1,293 1,566
nightlife 1,075 1,207travel 850 1,744
home/work/etc. 411 1,037shops 362 878
arts&entertainment 348 841parks&outdoors 184 363
education 49 117Total 4,572 3,110
Table: London Foursquare Data
Given a venue, suggests guests
Context
I similar to target advertising (?)I domain knowledge in people mobility
On people mobility (from the literature)
I distance mattersI likes might matterI “power users” are special
p(go|like, close) ∝ pgo · pclose · plike
plike
p(like = lui |go) =#venues visited by user u with rating lui
total #venues visited by user u
I lui is ranking obtained from item-based CF algorithm.
pgo
pgo =#venues visited by user u
total #venues
pclose
pclose = k11
dαui
pclose
pclose = k11
dαui
pclose
pclose = k11
dαui
Category α
food 1.64nightlife 1.61
travel (airports/trainstations) 2.22home/work/etc. 1.62
shops 1.64arts&entertainment 1.64
parks&outdoors 1.68education 1.93
High α→ travel farther
p(go|like, close) ∝ pgo · pclose · plike
I Naive BayesianI BayesianI Linear Regression
Results
Results
Results
Results
Arts.and.Ent. Education Food HomeWork Nightlife Parks Shops Travel
accu
racy
0.0
0.2
0.4
0.6
0.8
1.0
p_gop_closep_likeNaiveBayesianLinear Reg.
Discussion
I scalabilityI cold start situation
When it does not work
When It Does not Work
When It Does not Work
Final Remarks
I results depend on venue category (different α and predictability)I geographic closeness plays a very important role.I domain knowledge significantly improves recommendations
results.
“Understanding the specifics of your domainis critical to building a good recommender”
Paul Lamere @ recsys’12
Questions?