Exploring the filter bubble: The effect of using recommender systems on content diversity

WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web

Volumn , Issue , 2014, Pages 677-686

  Nguyen, Tien T ^a   Hui, Pik Mai ^a   Harper, F Maxwell ^a   Terveen, Loren ^a   Konstan, Joseph A ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Content diversity; Filter bubble; Longitudinal data; Recommender system; Tag genome; User experience

Indexed keywords

COLLABORATIVE FILTERING; ONLINE SYSTEMS; USER EXPERIENCE;

CONTENT DIVERSITY; FILTER BUBBLE; INDIVIDUAL LEVELS; LONGITUDINAL DATA; ONLINE RECOMMENDER SYSTEMS; PERSONALIZATIONS; TEMPORAL EFFECTS; USER-GENERATED;

RECOMMENDER SYSTEMS;

EID: 84908888053     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2566486.2568012     Document Type: Conference Paper

References (21)

1. X. Amatriain and J. Basilico. The netix tech blog: Netix recommendations: Beyond the 5 stars (part 1). http://techblog.netix.com/2012/04/netix-recommendations-beyond-5-stars.html, visited on 2013-09-06.
2. D. Fleder and K. Hosanagar. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55(5):697-712, May 2009.
3. K. Hosanagar, D. M. Fleder, D. Lee, and A. Buja. Will the global village fracture into tribes: Recommender systems and their effects on consumers. SSRN Scholarly Paper ID 1321962, Social Science Research Network, Rochester, NY, Oct. 2012.
4. T. Kamba, K. A. Bharat, and M. C. Albers. The krakatoa chronicle-an interactive, personalized newspaper on the web. 1995.
5. G. Linden. Eli pariser is wrong. http://glinden.blogspot.com/2011/05/eli-pariser-is- wrong.html, visited on 2013-09-13.
6. G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76-80, 2003.
7. M. Marshall. Aggregate knowledge raises $5m from kleiner, on a roll j VentureBeat. http://venturebeat.com/2006/12/10/aggregate-knowledge-raises-5m-from-kleiner-on-a-roll/, visited on 2013-09-06.
8. N. Negroponte. 000 000 111 - double agents. http://www.wired.com/wired/archive/3.03/negroponte-pr.html, visited on 2013-09-13.
9. N. Negroponte. Being Digital. Random House LLC, Jan. 1996.
10. T. T. Nguyen, D. Kluver, T.-Y. Wang, P.-M. Hui, M. D. Ekstrand, M. C. Willemsen, and J. Riedl. Rating support interfaces to improve user experience and recommender accuracy. To appear in the seventh ACM Recommender System Conference, RecSys 2013, Oct. 2013.
11. E. Pariser. The Filter Bubble: What the Internet is Hiding from You. Penguin, Mar. 2012.
12. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, CSCW '94, pages 175-186, New York, NY, USA, 1994. ACM.
13. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW '01, pages 285-295, New York, NY, USA, 2001. ACM.
14. S. Senecal and J. Nantel. The inuence of online product recommendations on consumers" online choices. Journal of Retailing, 80(2):159-169, 2004.
15. C. R. Sunstein. Republic.com: XA-GB. .. Princeton University Press, 2002.
16. P. E. Tetlock. Expert political judgment: How good is it? How can we know? Princeton University Press, 2005.
17. M. Van Alstyne and E. Brynjolfsson. Global village or cyber-balkans? modeling and measuring the integration of electronic communities. Management Science, 51(6):851-868, 2005.
18. J. Vig, S. Sen, and J. Riedl. Navigating the tag genome. In Proceedings of the 16th international conference on Intelligent user interfaces, pages 93-102. ACM, 2011.
19. J. Vig, S. Sen, and J. Riedl. The tag genome: Encoding community knowledge to support novel interaction. ACM Trans. Interact. Intell. Syst., 2(3):13:1-13:44, Sept. 2012.
20. B. Xiao and I. Benbasat. E-commerce product recommendation agents: use, characteristics, and impact. MIS Q., 31(1):137?209, Mar. 2007.
21. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, pages 22-32. ACM, 2005.