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?