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Global budgets for local recommendations

Authors:

Thomas Sandholm,

Christina Aperjis,

Bernardo A. HubermanAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 13 - 20

https://doi.org/10.1145/1864708.1864715

Published: 26 September 2010 Publication History

Abstract

We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile setting. The service automates the content search process by taking advantage of geographic and social context, while using crowdsourced expertise to present a personalized feed of targeted information ranked by a novel geo-aware rating and incentive mechanism.

Users rate the relevance of recommendations for particular locations using a limited, global voting budget. This budget is, in turn, increased by accurately predicting local content popularity. One of the key goals of our mechanism is to encourage ratings, and in an evaluation of the live system we found that the rating to click ratio was 107 times higher than the ratio for videos on YouTube, 34 times higher than the ratio for applications on the Android Market, and 3 times higher than the ratio for Web pages on Digg.

To investigate whether our mechanism also had qualitative effects on the ratings we conducted a number of experiments on Amazon Mechanical Turk, with 500 users, comparing our mechanism to the de-facto 5-star ratings commonly in use on the Web. Our results show that budgets improved the ranking and incentives improved the aggregate rating of a series of location-dependent Web pages.

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References

[1]

}}G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst., 23(1):103--145, 2005.

Digital Library

[2]

}}E. Amitay, N.Har'El, R. Sivan, and A. Soffer. Web-a-where: geotagging web content. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 273--280, Sheffield, United Kingdom, 2004. ACM.

Digital Library

[3]

}}S. Asadi, X. Zhou, and G. Yang. Using local popularity of web resources for geo-ranking of search engine results. World Wide Web, 12(2):149--170, 2009.

Digital Library

[4]

}}R. Baraglia, F. Cacheda, V. Carneiro, D. Fernandez, V. Formoso, R. Perego, and F. Silvestri. Search shortcuts: a new approach to the recommendation of queries. In RecSys '09: Proceedings of the third ACM conference on Recommender systems, pages 77--84, New York, NY, USA, 2009. ACM.

Digital Library

[5]

}}R. Bhattacharjee and A. Goel. Algorithms and incentives for robust ranking. In SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 425--433, Philadelphia, PA, USA, 2007. Society for Industrial and Applied Mathematics.

Digital Library

[6]

}}S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Computer Networks and ISDN Systems, pages 107--117, 1998.

Digital Library

[7]

}}Y.-Y. Chen, T. Suel, and A. Markowetz. Efficient query processing in geographic web search engines. In SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pages 277--288, New York, NY, USA, 2006. ACM.

Digital Library

[8]

}}B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1):242--259, March 2007.

[9]

}}R. Forsythe, F. Nelson, G. Neumann, and J. Wright. The iowa presidential stock market: A field experiment. Research in experimental economics, 4:1--43, 1991.

[10]

}}J. Freyne, M. Jacovi, I. Guy, and W. Geyer. Increasing engagement through early recommender intervention. In Proceedings of the third ACM conference on Recommender systems, pages 85--92. ACM, 2009.

Digital Library

[11]

}}A. Goodman. Winning Results with Google AdWords, Second Edition. McGraw-Hill Osborne Media, 2008.

Digital Library

[12]

}}R. V. Guha. Programmable search engine, February 2007. US Patent Application 202423:11.

[13]

}}I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. Personalized recommendation of social software items based on social relations. In RecSys '09: Proceedings of the third ACM conference on Recommender systems, pages 53--60, New York, NY, USA, 2009. ACM.

Digital Library

[14]

}}R. Hanson. Combinatorial information market design. Information Systems Frontiers, 5(1):107--119, 2003.

Digital Library

[15]

}}B. A. Huberman. The social mind. In J.-P. Changeux and J. Chavaillon, editors, Origins of the Human Brain, pages 250--261. Oxford University Press, 2002.

[16]

}}B. A. Huberman, D. M. Romero, and F. Wu. Crowdsourcing, attention and productivity. Journal of Information Science, 35(6):758--765, 2009.

Digital Library

[17]

}}P. Ipeirotis. Demographics of Mechanical Turk. CeDER Working Papers, 2010.

[18]

}}A. Josang, R. Ismail, and C. Boyd. A survey of trust and reputation systems for online service provision. Decision Support Systems, 43(2):618--644, Mar. 2007.

Digital Library

[19]

}}M. Kamvar and S. Baluja. A large scale study of wireless search behavior: Google mobile search. In CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 701--709, New York, NY, USA, 2006. ACM.

Digital Library

[20]

}}M. Kamvar and S. Baluja. The role of context in query input: using contextual signals to complete queries on mobile devices. In MobileHCI '07: Proceedings of the 9th international conference on Human computer interaction with mobile devices and services, pages 405--412, New York, NY, USA, 2007. ACM.

Digital Library

[21]

}}M. Kendall. A new measure of rank correlation. Biometrika, 30(1--2):81--89, 1938.

[22]

}}A. Mas-Colell, M. D. Whinston, and J. R. Green. Microeconomic Theory. Oxford University Press, Oxford, United Kingdom, 1995.

[23]

}}D. M. Pennock, S. Lawrence, C. L. Giles, and F. A. Nielsen. The real power of artificial markets. Science, 291:987--988, February 2001.

[24]

}}H. Rheingold. Smart Mobs: The Next Social Revolution. Basic Books, 2002.

Digital Library

[25]

}}J. Schiller and A. Voisard. Location Based Services. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2004.

Digital Library

[26]

}}J. Surowiecki. The Wisdom of Crowds. Anchor, 2005.

Digital Library

[27]

}}G. Szabo and B. A. Huberman. Predicting the popularity of online content. Communications of the ACM, August 2010. to appear.

Digital Library

[28]

}}F. Wu and B. A. Huberman. Novelty and collective attention. Proc. Natl. Acad. Sci (USA)}, 104:17599--17601, 2007.

[29]

}}F. Wu, D. M. Wilkinson, and B. A. Huberman. Feedback loops of attention in peer production. In CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering, pages 409--415, Washington, DC, USA, 2009. IEEE Computer Society.

Digital Library

[30]

}}Y. Zhen, W. Li, and D. Yeung. TagiCoFi: tag informed collaborative filtering. In Proceedings of the third {ACM} conference on Recommender systems, pages 69--76, New York, New York, {USA}, 2009. ACM.

Digital Library

Cited By

Leano RWang ZSarma A(2016)Labeling relevant skills in tasks: Can the crowd help?2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VLHCC.2016.7739683(185-189)Online publication date: Sep-2016
https://doi.org/10.1109/VLHCC.2016.7739683
Kitayama DSumiya K(2012)Geographical information search using estimated search criteria based on user operationsThe 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems10.1109/SCIS-ISIS.2012.6505279(1713-1719)Online publication date: Nov-2012
https://doi.org/10.1109/SCIS-ISIS.2012.6505279
Kirshenbaum EForman GDugan M(2012)A Live Comparison of Methods for Personalized Article Recommendation at Forbes.comMachine Learning and Knowledge Discovery in Databases10.1007/978-3-642-33486-3_4(51-66)Online publication date: 2012
https://doi.org/10.1007/978-3-642-33486-3_4
Show More Cited By

Index Terms

Global budgets for local recommendations
1. Human-centered computing
  1. Collaborative and social computing

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Leano RWang ZSarma A(2016)Labeling relevant skills in tasks: Can the crowd help?2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VLHCC.2016.7739683(185-189)Online publication date: Sep-2016
https://doi.org/10.1109/VLHCC.2016.7739683
Kitayama DSumiya K(2012)Geographical information search using estimated search criteria based on user operationsThe 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems10.1109/SCIS-ISIS.2012.6505279(1713-1719)Online publication date: Nov-2012
https://doi.org/10.1109/SCIS-ISIS.2012.6505279
Kirshenbaum EForman GDugan M(2012)A Live Comparison of Methods for Personalized Article Recommendation at Forbes.comMachine Learning and Knowledge Discovery in Databases10.1007/978-3-642-33486-3_4(51-66)Online publication date: 2012
https://doi.org/10.1007/978-3-642-33486-3_4
Ankolekar ASandholm TSandholm TMitchell ASöderberg JVaara EVorbau A(2011)FoxtrotProceedings of Interacting with Sound Workshop: Exploring Context-Aware, Local and Social Audio Applications10.1145/2019335.2019341(26-31)Online publication date: 30-Aug-2011
https://dl.acm.org/doi/10.1145/2019335.2019341
Sandholm TUng HDe Luca ESaid ABöhmer MMichahelles F(2011)Real-time, location-aware collaborative filtering of web contentProceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation10.1145/1961634.1961638(14-18)Online publication date: 13-Feb-2011
https://dl.acm.org/doi/10.1145/1961634.1961638

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