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International Conference on Conceptual Modeling

ER 2019: Advances in Conceptual Modeling pp 239–246Cite as

Representing the Filter Bubble: Towards a Model to Diversification in News

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11787))

Abstract

Filtering techniques like recommender systems are commonly employed to help people selecting items that best fit their conceptual needs. Although many benefits, recommender systems can put the user inside a filter-bubble given their high focus on similarity measures. This effect tends to limit user experiences, discovering new things, and so on. In the news domain, filter-bubbles are quite critical once they are means of changing people opinions. Therefore we propose a diversification approach to pop the bubble through a representation model based on points of view.

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Notes

  1. 1.

    Available at https://sisrec.inf.ufrgs.br/news-rec/.

  2. 2.

    https://g1.globo.com/.

  3. 3.

    https://www.r7.com/.

  4. 4.

    https://piaui.folha.uol.com.br/lupa/.

References

  1. Adomavicius, G., Kwon, Y.: Toward more diverse recommendations: item re-ranking methods for recommender systems. In: Proceedings of the 19th Workshop on Information Technology and Systems. Phoenix, Arizona (2009)

    Google Scholar 

  2. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining - WSDM 2009, p. 5. ACM Press, New York (2009). https://doi.org/10.1145/1498759.1498766

  3. Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., Bonneau, R.: Tweeting from left to right: is online political communication more than an echo chamber? Psychol. Sci. 26(10), 1531–1542 (2015). https://doi.org/10.1177/0956797615594620

    Article  Google Scholar 

  4. Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 335–336. ACM, New York (1998). https://doi.org/10.1145/290941.291025, http://doi.acm.org/10.1145/290941.291025

  5. Desarkar, M.S., Shinde, N.: Diversification in news recommendation for privacy concerned users. In: 2014 International Conference on Data Science and Advanced Analytics (DSAA), pp. 135–141 (2014). https://doi.org/10.1109/DSAA.2014.7058064

  6. Galway, N.U.I.: XploDiv: Diversification Approach For Recommender Systems. Technical report (2015). https://doi.org/10.13025/S8PC74

  7. Jannach, D., Adomavicius, G.: Recommendations with a purpose. In: Proceedings of the 10th ACM Conference on Recommender Systems - RecSys 2016, pp. 7–10. ACM Press, New York (2016). https://doi.org/10.1145/2959100.2959186

  8. Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.: A serendipity model for news recommendation. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 111–123. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24489-1_9

    Chapter  Google Scholar 

  9. Karimi, M., Jannach, D., Jugovac, M.: News recommender systems - survey and roads ahead. Inf. Process. Manag. 54(6), 1203–1227 (2018). https://doi.org/10.1016/j.ipm.2018.04.008

    Article  Google Scholar 

  10. Kunaver, M., Poržl, T.: Diversity in recommender systems - a survey. Knowl. Based Syst. 123, 154–162 (2017). https://doi.org/10.1016/j.knosys.2017.02.009

    Article  Google Scholar 

  11. Pariser, E.: The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin Publishing Group (2011)

    Google Scholar 

  12. Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, 2nd edn. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6

    Book  MATH  Google Scholar 

  13. Said, A., Kille, B., Jain, B., Albayrak, S.: Increasing diversity through furthest neighbor-based recommendation. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, pp. 1–4 (2012)

    Google Scholar 

  14. Tintarev, N., Sullivan, E., Guldin, D., Qiu, S., Odjik, D.: Same but different. Linguist. Philos. 38(4), 289–314 (2015). https://doi.org/10.1007/s10988-015-9176-x

    Article  Google Scholar 

  15. Vargas, S., Castells, P.: Exploiting the diversity of user preferences for recommendation. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 129–136 (2013)

    Google Scholar 

  16. Vargas, S.S.: Novelty and diversity evaluation and enhancement in recommender systems. Ph.D. thesis, Universidad Autónoma de Madrid (2012)

    Google Scholar 

  17. Zhang, F.: Improving recommendation lists through neighbor diversification. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 222–225. IEEE, New York (2009). https://doi.org/10.1109/ICICISYS.2009.5358201

  18. Zhang, M., Hurley, N.: Novel item recommendation by user profile partitioning. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 508–515. IEEE (2009). https://doi.org/10.1109/WI-IAT.2009.85

  19. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web - WWW 2005, p. 22. ACM Press, New York, January 2005. https://doi.org/10.1145/1060745.1060754

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Author information

Authors and Affiliations

  1. Institute of Informatics, UFRGS, Porto Alegre, Brazil

    Gabriel Machado Lunardi

Authors
  1. Gabriel Machado Lunardi
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Correspondence to Gabriel Machado Lunardi .

Editor information

Editors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Giancarlo Guizzardi

  2. Ghent University, Ghent, Belgium

    Frederik Gailly

  3. Federal University of Southern Bahia, Salvador, Brazil

    Rita Suzana Pitangueira Maciel

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Lunardi, G.M. (2019). Representing the Filter Bubble: Towards a Model to Diversification in News. In: Guizzardi, G., Gailly, F., Suzana Pitangueira Maciel, R. (eds) Advances in Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11787. Springer, Cham. https://doi.org/10.1007/978-3-030-34146-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-34146-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34145-9

  • Online ISBN: 978-3-030-34146-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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