1. Thomas Sandholm, Hang Ung, Christina Aperjis, Bernardo Huberman Hewlett-Packard, HP Labs, Social Computing Lab RecSys, Barcelona September 27, 2010 Global budgets for local recommendations
2. Why Vote? How do we get more people to contribute their opinions?
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4. 500 30 6 Users EXPERIMENTS LOCATIONS Paris Chicago Athens Palo Alto Mumbai Bangalore
5. Many people consume content – FEW LEAVE OPINIONS May 2008 300M+ April 2010 8M April 2010 27M Sample taken Users
7. RATING REWARDS Alice $5 http://dominos.com Bob $4 http://dominos.com A $3 http://roundtable.com B $3 http://roundtable.com http://roundtable.com $3 John Top Rewards (reward factor 10)$40Bob Dominos @ A $30AliceRoundTable @ B
8. CLICK TO RATING Ratio July 2010 4K May 2008 300M+ April 2010 8M April 2010 27M Sample taken Users
13. Bonus effect on participation Probability of signals Number of surveys taken
14. Lessons LEARNED Amazon Mechanical Turk Workers not random geographic sample Sensitive to task complexity Respond well to small incentives Budget Mechanism Higher quality recommendations with incentives Social/Economic/Status value extract more opinions Tuned based on usage, e.g. reward factor
15. FUTURE WORK Projects AfricaMap: map annotation in remote parts for disaster relief UNOSAT/Uni. Geneva Mobile print provider recommendations via HP ePrint Research GSP Auction for commercial bidding LMSR Market for recommendation arbitrage Enhance reward mechanism to both encourage and identify high quality contributions
20. HP Gloe: STATUS ~4k* users on Android, iPhone, BlackBerry, WebOS, Web… ~7m* recommendations at http://hpgloe.com *Sept 2010
21. Lessons LEARNED CONTINUED Gloe system Geohash location partitioning simple and efficient HTTP(S) GET/JSON(P) has served us well on all platforms MySQL & Sharded architecture flexible and fast