Ads and the City

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

mobile social-networking sites

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

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, close) ∝ pgo · pclose · plike

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.

pgo #venues visited by user upgo = total #venues

pclose 1pclose = k1 α dui

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

p(go|like, close) ∝ pgo · pclose · plikeNaive BayesianBayesianLinear Regression

Results

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

Discussionscalabilitycold start situation

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 role.domain knowledge signiﬁcantly improves recommendationsresults.

“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

by Daniele Quercia on Sep 14, 2012

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