oa 3bij3 – Developing a framework for researching recommender systems and their effects

References

1. Arceneaux, K., & Johnson, M.(2013). Changing minds or changing channels? Partisan news in an age of choice. Chicago, IL: University of Chicago Press. doi:10.7208/chicago/9780226047447.001.0001
   [Google Scholar]
2. Bawden, D., & Robinson, L.(2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies.Journal of Information Science, 35(2), 180–191. doi:10.1177/0165551508095781
   [Google Scholar]
3. Beam, M.A.(2014). Automating the news: How personalized news recommender system design choices impact news reception.Communication Research, 41(8), 1019–1041. doi:10.1177/0093650213497979
   [Google Scholar]
4. Bhaskar, M.(2016). In the age of the algorithm, the human gatekeeper is back.The Guardian. Retrieved from https://www.theguardian.com/technology/2016/sep/30/ age-of-algorithm-human-gatekeeper
   [Google Scholar]
5. Bodó, B., Helberger, N., Eskens, S., & Möller, J.(2019). Interested in diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization.Digital Journalism, 7(2), 206–229. https://doi.org/10.1080/21670811.2018.1521292
   [Google Scholar]
6. Bogers, T., & Van den Bosch, A.(2007). Comparing and evaluating information retrieval algorithms for news recommendation. In Proceedings of the 2007 ACM Conference on Recommender Systems (pp. 141–144). Minneapolis, MA: ACM Press. doi:10.1145/1297231.1297256
   [Google Scholar]
7. Bomhardt, C., & Gaul, W.(2005). NewsRec, a personal recommendation system for news websites. In C.Weihs & W.Gaul (Eds.), Classification—the ubiquitous challenge (pp. 394–401). Berlin: Springer. doi:10.1007/3‑540‑28084‑7_45
   [Google Scholar]
8. Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., & Hauff, C.(2019). SIREN: A simulation framework for understanding the effects of recommender systems in online news environments. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 150–159). Atlanta, GA: ACM Press. doi:10.1145/3287560.3287583
   [Google Scholar]
9. boyd, D., & Crawford, K.(2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon.Information Communication and Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
   [Google Scholar]
10. Bozdag, E.(2013). Bias in algorithmic filtering and personalization.Ethics and Information Technology, 15(3), 209–227. doi:10.1007/s10676‑013‑9321‑6
    [Google Scholar]
11. Bridge, D., & Kelly, J.P.(2006). Ways of computing diverse collaborative recommendations. In Lecture Notes in Computer Science (pp. 41–50). Berlin: Springer. doi:10.1007/11768012_6
    [Google Scholar]
12. Bruns, A.(2018). Gatewatching and news curation: Journalism, social media, and the public sphere. New York: Peter Lang. doi:10.3726/b13293
    [Google Scholar]
13. Burscher, B., Vliegenthart, R., & De Vreese, C.H.(2015). Using supervised machine learning to code policy issues.The ANNALS of the American Academy of Political and Social Science, 659(1), 122–131. doi:10.1177/0002716215569441
    [Google Scholar]
14. De Smedt, T., & Daelemans, W.(2012). Pattern for python.Journal of Machine Learning Research, 13(6), 2063–2067.
    [Google Scholar]
15. Donsbach, W.(2014). Journalism as the new knowledge profession and consequences for journalism education.Journalism, 15(6), 661–677. doi:10.1177/1464884913491347
    [Google Scholar]
16. Dylko, I., Dolgov, I., Hoffman, W., Eckhart, N., Molina, M., & Aaziz, O.(2017). The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure.Computers in Human Behavior, 73, 181–190. doi:10.1016/j.chb.2017.03.031
    [Google Scholar]
17. Ekstrand, M.D., & Willemsen, M.C.(2016). Behaviorism is not enough: Better recommendations through listening to users. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 221–224). Boston, MA: ACM Press. doi:10.1145/2959100.2959179
    [Google Scholar]
18. Gaines, B.J., & Kuklinski, J.H.(2011). Experimental estimation of heterogeneous treatment effects related to self-selection.American Journal of Political Science, 55(3), 724–736. doi:10.1111/j.1540‑5907.2011.00518.x
    [Google Scholar]
19. Garcin, F., & Faltings, B.(2013). PEN recsys. In Proceedings of the 2013 International News Recommender Systems Workshop and Challenge (NRS) (pp. 3–9). New York, NY: ACM Press. doi:10.1145/2516641.2516642
    [Google Scholar]
20. Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., & Huber, A.(2014). Offline and online evaluation of news recommender systems at swissinfo.ch. In Proceedings of the 8th ACM Conference on Recommender Systems (pp. 169–176). Foster City, CA: ACM Press. doi:10.1145/2645710.2645745
    [Google Scholar]
21. Geiß, S., Magin, M., Stark, B., & Jürgens, P.(2018). “Common Meeting Ground” in Gefahr? Selektionslogiken politischer Informationsquellen und ihr Einfluss auf die Fragmentierung individueller Themenhorizonte.Medien & Kommunikationswissenschaft, 66(4), 502–525. doi:10.5771/1615‑634X‑2018‑4‑502
    [Google Scholar]
22. Grinberg, M.(2014). Flask web development. Sebastopol, CA: O’Reilly Media.
    [Google Scholar]
23. Guess, A., & Coppock, A.(2018). Does counter-attitudinal information cause backlash? Results from three large survey experiments.British Journal of Political Science, online first. doi:10.1017/S0007123418000327
    [Google Scholar]
24. Gupta, J., & Gadge, J. (2015, Jan). Performance analysis of recommendation system based on collaborative filtering and demographics. In International Conference on Communication, Information Computing Technology (pp. 1–6). doi:10.1109/ICCICT.2015.7045675
    [Google Scholar]
25. Haim, M., Graefe, A., & Brosius, H.B.(2018). Burst of the filter bubble? Effects of personalization on the diversity of Google News.Digital Journalism, 6(3), 330–343. doi:10.1080/21670811.2017.1338145
    [Google Scholar]
26. Helberger, N.(2019). On the democratic role of news recommenders.Digital Journalism, online first. doi:10.1080/21670811.2019.1623700
    [Google Scholar]
27. Helberger, N., Karppinen, K., & D’Acunto, L.(2018). Exposure diversity as a design principle for recommender systems.Information Communication and Society, 21(2), 191–207. doi:10.1080/1369118X.2016.1271900
    [Google Scholar]
28. Honnibal, M., & Montani, I.(2017). spaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing.To appear.
    [Google Scholar]
29. Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G.(2007). Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search.ACM Transactions on Information Systems (TOIS), 25(2), 1–27. doi:10.1145/1229179.1229181
    [Google Scholar]
30. Jonnalagedda, N., Gauch, S., Labille, K., & Alfarhood, S.(2016). Incorporating popularity in a personalized news recommender system.PeerJ Computer Science, 2. doi:10.7717/peerj‑cs.63
    [Google Scholar]
31. Karakaya, M.Ö., & Aytekin, T.(2017). Effective methods for increasing aggregate diversity in recommender systems.Knowledge and Information Systems, 56(2), 1–18. doi:10.1007/s10115‑017‑1135‑0
    [Google Scholar]
32. Karimi, M., Jannach, D., & Jugovac, M.(2018). News recommender systems–Survey and roads ahead.Information Processing & Management, 54(6), 1203–1227. doi:10.1016/j.ipm.2018.04.008
    [Google Scholar]
33. Kitchin, R.(2014). Big Data, new epistemologies and paradigm shifts.Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481
    [Google Scholar]
34. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., & Newell, C.(2012). Explaining the user experience of recommender systems.User Modeling and User-Adapted Interaction, 22(4-5), 441–504. doi:10.1007/s11257‑011‑9118‑4
    [Google Scholar]
35. Konstan, J.A., & Riedl, J.(2012). Recommender systems: from algorithms to user experience.User Modeling and User-Adapted Interaction, 22(1/2), 101–123. doi:10.1007/s11257‑011‑9112‑x
    [Google Scholar]
36. Kotkov, D., Wang, S., & Veijalainen, J.(2016). Knowledge-Based systems: A survey of serendipity in recommender systems.Knowledge-Based Systems, 111, 180–192. doi:10.1016/j.knosys.2016.08.014
    [Google Scholar]
37. Kroon, A., Trilling, D., Fokkens, A., Loecherbach, F., Moeller, J., van Atteveldt, W., & van der Velden, M.(2019). Deriving semantics from dutch media corpora: The Amsterdam word embedding model. Paper presented at Etmaal van de communicatiewetenschap. Nijmegen, Netherlands.
    [Google Scholar]
38. Kunert, J., & Thurman, N.(2019). The form of content personalisation at mainstream, transatlantic news outlets: 2010–2016.Journalism Practice, 1–22. doi:10.1080/17512786.2019.1567271
    [Google Scholar]
39. Lee, H., Kwak, N., & Campbell, S.W.(2015). Hearing the other side revisited: The joint workings of cross-cutting discussion and strong tie homogeneity in facilitating deliberative and participatory democracy.Communication Research, 42(4), 569–596. doi:10.1177/0093650213483824
    [Google Scholar]
40. Leuener, R.(2017). NZZ Companion: How we successfully developed a personalised news application.Medium. Retrieved from https://medium.com/@rouven.leuener/nzz-companion-how-we-successfully-developed-a-personalised-news-app-d3c382767025
    [Google Scholar]
41. McQuail, D.(2007). Revisiting diversity as a media policy goal. In W. A.Meier & J.Trappel (Eds.), Power, performance and politics: Media policy in Europe (pp. 41–57). Baden-Baden: Nomos. doi:10.5771/9783845202938‑41
    [Google Scholar]
42. Messing, S., & Westwood, S.J.(2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online.Communication Research, 41(8), 1042–1063. doi:10.1177/0093650212466406
    [Google Scholar]
43. Mikolov, T., Corrado, G., Chen, K., & Dean, J.(2013). Efficient estimation of word representations in vector space.Proceedings of the International Conference on Learning Representations (ICLR), 1–12. doi:10.1162/153244303322533223
    [Google Scholar]
44. Moeller, J., Trilling, D., Helberger, N., Irion, K., & De Vreese, C.(2016). Shrinking core? Exploring the differential agenda setting power of traditional and personalized news media.Info, 18(6). doi:10.1108/info‑05‑2016‑0020
    [Google Scholar]
45. Möller, J., Trilling, D., Helberger, N., & van Es, B.(2018). Do not blame it on the algorithm: An empirical assessment of multiple recommender systems and their impact on content diversity.Information, Communication and Society, 1–19. doi:10.1080/1369118X.2018.1444076
    [Google Scholar]
46. Möller, J., van de Velde, R.N., Merten, L., & Puschmann, C.(2019). Explaining online news engagement based on browsing behaviour: Creatures of habit?Social Science Computer Review, 1–17.doi:10.1177/0894439319828012
    [Google Scholar]
47. Napoli, P.M.(2015). Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers.Telecommunications Policy, 39(9), 751–760. doi:10.1016/j.telpol.2014.12.003
    [Google Scholar]
48. Nechushtai, E., & Lewis, S.C.(2019). What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations.Computers in Human Behavior, 90, 298–307. doi:10.1016/j.chb.2018.07.043
    [Google Scholar]
49. Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L., & Konstan, J. A.(2014). Exploring the filter bubble. In Proceedings of the 23rd International Conference on World Wide Web (pp. 677–686). doi:10.1145/2566486.2568012
    [Google Scholar]
50. Nic, N., Fletcher, R., Kalogeropoulos, A., Levy, D.A., & Nielsen, R. K.(2018). Reuters institute digital news report 2018. Reuters Institute for the Study of Journalism. Retrieved from www.digitalnewsreport.org/survey/2018
    [Google Scholar]
51. Paliouras, G., Mouzakidis, A., Moustakas, V., & Skourlas, C.(2008). PNS: A personalized news aggregator on the web.Studies in Computational Intelligence, 104, 175–197. doi:10.1007/978‑3‑540‑77471‑6_10
    [Google Scholar]
52. Pariser, E.(2011). The filter bubble: What the Internet is hiding from you. London: Penguin. doi:10.3139/9783446431164
    [Google Scholar]
53. Pazzani, M.J., & Billsus, D.(2007). Content-based recommendation systems. In The adaptive web (pp. 325–341). Berlin: Springer. doi:10.1007/978‑3‑540‑72079‑9_10
    [Google Scholar]
54. Peng, H., Liu, J., & Lin, C.Y.(2016). News citation recommendation with implicit and explicit semantics. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 388–398). doi:10.18653/v1/P16‑1037
    [Google Scholar]
55. Peperkamp, J., & Berendt, B.(2018). Diversity checker. In Adjunct publication of the 26th Conference on User Modelling, Adaptation and Personalization (UMAP) (pp. 35–41). New York, NY: ACM Press. doi:10.1145/3213586.3226208
    [Google Scholar]
56. Ribeiro, M.T., Ziviani, N., Moura, E.S.D., Hata, I., Lacerda, A., & Veloso, A.(2014). Multiobjective pareto-efficient approaches for recommender systems.ACM Transactions on Intelligent Systems and Technology, 5(4), 1–20. doi:10.1145/2629350
    [Google Scholar]
57. Ricci, F., Rokach, L., & Shapira, B.(2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Boston, MA: Springer US. doi:10.1007/978‑0‑387‑85820‑3_1
    [Google Scholar]
58. Schlesinger, P., & Doyle, G.(2015). From organizational crisis to multi-platform salvation? Creative destruction and the recomposition of news media.Journalism: Theory, Practice & Criticism, 16(3), 305–323. doi:10.1177/1464884914530223
    [Google Scholar]
59. Shah, D.V., Cappella, J.N., & Neuman, W.R.(2015). Big Data, digital media, and Computational Social Science.The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. doi:10.1177/0002716215572084
    [Google Scholar]
60. Shani, G., & Gunawardana, A.(2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257–297). Boston, MA: Springer US. doi:10.1007/978‑0‑387‑85820‑3_8
    [Google Scholar]
61. Sidorov, G., Gelbukh, A., Gómez-Adorno, H., & Pinto, D.(2014). Soft similarity and soft cosine measure: Similarity of features in vector space model.Computacion y Sistemas, 18(3), 491–504. doi:10.13053/CyS‑18‑3‑2043
    [Google Scholar]
62. Song, H., Jung, J., & Kim, Y.(2017). Perceived news overload and its cognitive and attitudinal consequences for news usage in South Korea.Journalism & Mass Communication Quarterly, 94(4), 1172–1190. doi:10.1177/1077699016679975
    [Google Scholar]
63. Stroud, N.J.(2011). Niche news: The politics of news choice. New York, NY: Oxford University Press.
    [Google Scholar]
64. Sunstein, C.R.(2009). Republic. com 2.0. Princeton, NJ: Princeton University Press.
    [Google Scholar]
65. Teppan, E.C., & Zanker, M.(2015). Decision biases in recommender systems.Journal of Internet Commerce, 14(2), 255–275. doi:10.1080/15332861.2015.1018703
    [Google Scholar]
66. Thorson, K., & Wells, C.(2016). Curated flows: A framework for mapping media exposure in the digital age.Communication Theory, 26(3), 309–328. doi:10.1111/comt.12087
    [Google Scholar]
67. Thurman, N., & Schifferes, S.(2012). The future of personalization at news websites: Lessons from a longitudinal study.Journalism Studies, 13(5-6), 775–790. doi:10.1080/1461670X.2012.664341
    [Google Scholar]
68. Trilling, D.(2014). Weg vom manuellen Speichern: RSS-Feeds in der automatisierten Datenerhebung bei Onlinemedien. In K.Sommer, M.Wettstein, W.Wirth, & J.Matthes (Eds.), Automatisierung in der Inhaltsanalyse (pp. 73–89). Köln: Herbert von Halem.
    [Google Scholar]
69. Trilling, D., Van Klingeren, M., & Tsfati, Y.(2017). Selective exposure, political polarization, and possible mediators: Evidence from the Netherlands.International Journal of Public Opinion Research, 29(2), 189–213. doi:10.1093/ijpor/edw003
    [Google Scholar]
70. Trilling, D., van de Velde, B., Kroon, A.C., Loecherbach, F., Araujo, T., Strycharz, J., Jonkman, J.(2018). INCA: Infrastructure for content analysis. In 14th International Conference on e-Science (IEEE) (pp. 329–330). Amsterdam. doi:10.1109/eScience.2018.00078
    [Google Scholar]
71. Van Atteveldt, W., Strycharz, J., Trilling, D., & Welbers, K.(2019). Toward open computational communication science: A practical roadmap for reusable data and code.International Journal of Communication, 13(1), 3935–3954.
    [Google Scholar]
72. Van Atteveldt, W., & Peng, T.Q.(2018). When communication meets computation: Opportunities, challenges, and pitfalls in computational communication science.Communication Methods and Measures, 12(2-3), 1–12. doi:10.1080/19312458.2018.1458084
    [Google Scholar]
73. Victor, P., De Cock, M., & Cornelis, C.(2011). Trust and recommendations. In Recommender systems handbook (pp. 645–675). Boston, MA: Springer. doi:10.1007/978‑0‑387‑85820‑3_20
    [Google Scholar]
74. Yang, J. A.(2016). Effects of popularity-based news recommendations (most-viewed) on users exposure to online news.Media Psychology, 19(2), 243–271. doi:10.1080/15213269.2015.1006333
    [Google Scholar]
75. Zhang, S., Yao, L., Sun, A., & Tay, Y.(2019). Deep learning based recommender system: A survey and new perspectives.ACM Computing Surveys (CSUR), 52(1), 5. doi:10.1145/3285029
    [Google Scholar]
76. Zuiderveen Borgesius, F.J., Trilling, D., Möller, J., Bodó, B., de Vreese, C.H., & Helberger, N.(2016). Should we worry about filter bubbles?Internet Policy Review, 5(1), 1–16. doi:10.14763/2016.1.401
    [Google Scholar]
77. Řehůřek, R., & Sojka, P.(2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (pp. 45–50). Valletta, Malta: ELRA.
    [Google Scholar]