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Ads and the City
 

Ads and the City

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Ads and the City: Considering Geographic Distance Goes a Long Way http://bit.ly/PbEzpD

Ads and the City: Considering Geographic Distance Goes a Long Way http://bit.ly/PbEzpD

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    Ads and the City Ads and the City Presentation Transcript

    • 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 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
    • 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
    • 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
    • Questions?