Conceptual model for pervasive advertising supported on a Smart TV- SmartPhone cooperation framework.

Modelo conceptual para el despliegue de publicidad ubicua soportado en un esquema de cooperación Smart TV- SmartPhone.

dc.creatorMartinez-Pavon, Francisco
dc.creatorRamirez Gonzalez, Gustavo
dc.creatorChantre-Astaiza, Ángela
dc.date2014-06-01
dc.date.accessioned2022-12-15T15:54:57Z
dc.date.available2022-12-15T15:54:57Z
dc.identifierhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/11
dc.identifier10.18359/rcin.11
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5355322
dc.descriptionAdvertising has been one of the most valuable marketing tools for years by means of a massive, wide-ranging and vertical approach between customers and advertisers. However, a new tendency known as pervasive advertising suggests an evolution of the classical concept towards a more interactive, customized, and horizontal environment that seeks to improve the impact and efficiency of conventional advertising. As a result of the support of emerging technologies related to the development of smartphones and smart TVs, there are no doubts about pervasive advertising potential and its value as a rich research field. This article introduces a conceptual model, which compiles the most relevant research areas related to pervasive computing applied to advertising supported on a smart TV – smartphone cooperation framework.en-US
dc.descriptionLa publicidad ha sido durante años una de las herramientas más valiosas del mercadeo a través de un enfoque principalmente masivo, generalizado y vertical entre clientes y anunciantes. No obstante, una nueva corriente conocida como publicidad ubicua marca una evolución en el concepto clásico hacia entornos más interactivos, personalizados y horizontales que busca mejorar la eficiencia y el impacto de la publicidad convencional. Gracias al apoyo de tecnologías emergentes que se sustentan en la evolución de los smartphones y los smart TV, el potencial de la publicidad ubicua es indudable, lo cual la ha convertido en un terreno fértil de investigación. El presente artículo presenta un modelo conceptual que condensa las áreas de investigación más relevantes relacionadas con el despliegue de publicidad en entornos de computación ubicua soportados en esquemas de cooperación smart TV – smartphone.es-ES
dc.formatapplication/pdf
dc.formattext/html
dc.languagespa
dc.publisherUniversidad Militar Nueva Granadaes-ES
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/11/9
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/11/1819
dc.relation/*ref*/AMA - American Marketing Association. (2013). Marketing Power - American Marketing Association. Recuperado en junio de 2013, de http://www.marketingpower.com/Pages/default.aspx
dc.relation/*ref*/Müller, J., Alt, F. & Michelis, D. (2011). Pervasive Advertising. Londres, Inglaterra: Springer, pp. 1–29. http://dx.doi.org/10.1007/978-0-85729-352-7
dc.relation/*ref*/Kotler, P. & Keller, K. L. (2012). Marketing management. Upper Saddle River, N.J., EE.UU.: Prentice Hall.
dc.relation/*ref*/Meffert, H. (2012). Marketing: Grundlagen marktorientierter Unternehmensführung. Berlín, Alemania: Gabler Verlag.
dc.relation/*ref*/Zaichkowsky, J. L. (1985.) Measuring the Involvement Construct. J. Consum. Res., 12(3), pp. 341–352. http://dx.doi.org/10.1086/208520
dc.relation/*ref*/Haddadi, H.; Hui, P.; Henderson, T. & Brown, I. (2011). Targeted Advertising on the Handset: Privacy and Security Challenges. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (119–137). Londres, Inglaterra: Springer.
dc.relation/*ref*/Stalder, U. (2011). Digital Out-of-Home Media: Means and Effects of Digital Media in Public Space. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (31–56). Londres, Inglaterra: Springer.
dc.relation/*ref*/Rui, J. & Cardoso, J.C.S. (2011). Opportunities and Challenges of Interactive Public Displays as an Advertising Medium. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (139–157). Londres, Inglaterra: Springer.
dc.relation/*ref*/Jewett, F. (2011). Why Smart TV is the next big thing. Recuperado en junio de 2013, de http://www.uievolution.com/mobileconnect/Mobile_Connect_June_2011.pdf
dc.relation/*ref*/Müller, J., Alt, F. & Michelis, D. (2011). Pervasive Advertising. Londres, Inglaterra: Springer, pp. 1–29. http://dx.doi.org/10.1007/978-0-85729-352-7
dc.relation/*ref*/Weiser, M. (1999). The computer for the 21st century. Sigmobile Mob Comput Commun Rev, 3(3), pp. 3–11. http://dx.doi.org/10.1145/329124.329126
dc.relation/*ref*/Boll, S., Schmidt, A., Kern, D., Streng, S., & Holleis, P. (2008). Magic Beyond the Screen. Ieee Multimed., 15(4), pp. 8–13. http://dx.doi.org/10.1109/mmul.2008.93
dc.relation/*ref*/Ranganathan, A. & Campbell, R. H. (2002). Advertising in a pervasive computing environment. En Proceedings of the 2nd international workshop on Mobile commerce. New York, NY, EE.UU, pp. 10–14. http://dx.doi.org/10.1145/570705.570708
dc.relation/*ref*/Fogg, B. J. (diciembre, 2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(12), pp. 89-120. http://dx.doi.org/10.1145/764008.763957
dc.relation/*ref*/Otero, N. & Rui, J. (2009). Worth and Human Values at the Centre of Designing Situated Digital Public Displays. Int J Adv Pervasive Ubiquitous Comput, 1(4), pp. 1–13. http://dx.doi.org/10.4018/japuc.2009100101
dc.relation/*ref*/Ashton, W. B. (1997). Keeping abreast of science and technology: technical intelligence for business. Columbus, OH, EE.UU.: Battelle Press.
dc.relation/*ref*/Matheo-Software. (2013). Matheo Analyzer. Recuperado en enero de 2013, de http://www.matheo-software.com/es/productos/matheo-analyzer.html
dc.relation/*ref*/Harzing A-W. (2013). Publish or Perish. Recuperado en enero de 2013, de http://www.harzing.com/pop.htm
dc.relation/*ref*/Mahmood, T. & Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. En Proceedings of the 20th ACM conference on Hypertext and hypermedia. Nueva York, NY, EE.UU., pp. 73–82. http://dx.doi.org/10.1145/1557914.1557930
dc.relation/*ref*/Resnick, P. & Varian, H. R. (1997). Recommender systems. Commun Acm, 40(3), pp. 56–58. http://dx.doi.org/10.1145/245108.245121
dc.relation/*ref*/Burke, R. (2007). Hybrid Web Recommender Systems. En Brusilovsky, P., Kobsa, A. & Nejdl, W. (Eds.). The Adaptive Web (377–408). Berlín, Alemania: Springer http://dx.doi.org/10.1007/978-3-540-72079-9_12
dc.relation/*ref*/Ricci, F. (2011). Recommender systems handbook. Nueva York, NY, EE.UU.: Springer. http://dx.doi.org/10.1007/978-0-387-85820-3
dc.relation/*ref*/Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A. & Riedl, J. (2002). Getting to know you: learning new user preferences in recommender systems. En Proceedings of the 7th international conference on Intelligent user interfaces. Nueva York, NY, EE.UU., pp. 127–134. http://dx.doi.org/10.1145/502716.502737
dc.relation/*ref*/Yu, K., Schwaighofer, A., Tresp, V., Xu, X. & Kriegel, H.P. (2004). Probabilistic memory-based collaborative filtering. Ieee Trans. Knowl. Data Eng., 16(1), pp. 56–69. http://dx.doi.org/10.1109/TKDE.2004.1264822
dc.relation/*ref*/Golbeck, J. (2006). Generating predictive movie recommendations from trust in social networks. En Proceedings of the 4th international conference on Trust Management. Berlín, Alemania, pp. 93–104. http://dx.doi.org/10.1007/11755593_8
dc.relation/*ref*/Massa, P. & Avesani, P. (2004). Trust-Aware Collaborative Filtering for Recommender Systems. En Meersman, R. & Tari, Z. (Eds.). On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE (492–508). Berlín, Alemania: Springer. http://dx.doi.org/10.1007/978-3-540-30468-5_31
dc.relation/*ref*/Pazzani, M. J. (diciembre, 1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev., 13(5–6), pp. 393–408. http://dx.doi.org/10.1023/A:1006544522159
dc.relation/*ref*/Huang, Z., Chen, H. & Zeng, D. (enero, 2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. Acm Trans Inf Syst, 22(1), pp. 116–142. http://dx.doi.org/10.1145/963770.963775
dc.relation/*ref*/Symeonidis, P. (2008). Content-based Dimensionality Reduction for Recommender Systems. En Preisach, C., Burkhardt, P.D.H., Schmidt-Thieme, P.D.L. & Decker, P.D.R. (Eds.). Data Analysis, Machine Learning and Applications (619–626). Berlín, Alemania: Springer. http://dx.doi.org/10.1007/978-3-540-78246-9_73
dc.relation/*ref*/Billsus, D. & Pazzani, M. J. (1998). Learning Collaborative Information Filters. En Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco, CA, EE.UU., pp. 46–54. [31] Balabanović, M. & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Commun Acm, 40(3), pp. 66–72. http://dx.doi.org/10.1145/245108.245124
dc.relation/*ref*/Basu, C., Hirsh, H., & Cohen W. (1998). "Recommendation as classification: using social and content-based information in recommendation. En Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence. Menlo Park, CA, EE.UU., pp. 714–720.
dc.relation/*ref*/Li, Q. & Kim, B.M. (2003). An approach for combining content-based and collaborative filters. En Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11, Stroudsburg, PA, EE.UU., pp. 17–24. http://dx.doi.org/10.3115/1118935.1118938
dc.relation/*ref*/Schein, A. I., Popescul, A., Ungar, L. H. & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. En Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. Nueva York, NY, EE.UU., pp. 253–260. http://dx.doi.org/10.1145/564376.564421
dc.relation/*ref*/Spaeth, A. & Desmarais, M. C. (2013). Combining Collaborative Filtering and Text Similarity for Expert Profile Recommendations in Social Websites. En Carberry, S., Weibelzahl, S., Micarelli, A. & Semeraro, G. (Eds.). User Modeling, Adaptation, and Personalization (178–189). Berlín, Alemania: Springer. http://dx.doi.org/10.1007/978-3-642-38844-6_15
dc.relation/*ref*/Rongfei, J., Maozhong, J., & Chao, L. (2010). A new clustering method for collaborative filtering. En 2010 International Conference on Networking and Information Technology (ICNIT), pp. 488–492. http://dx.doi.org/10.1109/ICNIT.2010.5508465
dc.relation/*ref*/Kuflik, T., Berkovsky, S., Carmagnola, F., Heckmann, D. & Krüger, A. (2009). Advances in Ubiquitous User Modelling: Revised Selected Papers. Berlín, Alemania: Springer. http://dx.doi.org/10.1007/978-3-642-05039-8
dc.relation/*ref*/Heckmann, D. & Krueger, A. (2003). A User Modeling Markup Language (UserML) for Ubiquitous Computing. En Brusilovsky, P., Corbett, A. & de Rosis, F. (Eds.). User Modeling 2003 (393–397). Berlín, Alemania: Springer.
dc.relation/*ref*/Hussein, T., Linder, T., Gaulke, W., & Ziegler, J. (2010). A Framework and an Architecture for Context-Aware Group Recommendations. En Kolfschoten, G., Herrmann, T. & Lukosch, S. (Eds.). Collaboration and Technology (121–128). Berlín, Alemania: Springer. http://dx.doi.org/10.1007/978-3-642-15714-1_10
dc.relation/*ref*/Carolis, B. D. (2011). Adapting News and Advertisements to Groups. En Pervasive Advertising, J. Müller, F. Alt, and D. Michelis, Eds. Springer London, pp. 227–246. http://dx.doi.org/10.1007/978-0-85729-352-7_11
dc.relation/*ref*/Partridge K. and Begole B., (2011) "Activity-Based Advertising," En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (83–101). Londres, Inglaterra: Springer. http://dx.doi.org/10.1007/978-0-85729-352-7_4
dc.relation/*ref*/Masthoff, J. (febrero, 2004). Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Model. User-Adapt. Interact., 14(1), pp. 37–85. http://dx.doi.org/10.1023/B:USER.0000010138.79319.fd
dc.relation/*ref*/Jameson, A. (2004). More than the sum of its members: challenges for group recommender systems. En Proceedings of the working conference on Advanced visual interfaces, Nueva York, NY, EE.UU., pp. 48–54. http://dx.doi.org/10.1145/989863.989869
dc.relation/*ref*/Santos, R., Marreiros, G., Ramos, C., Neves, J. & Bulas-Cruz, J. (2006). Multi-agent Approach for Ubiquitous Group Decision Support Involving Emotions. En Ma, J., Jin, H., Yang, L.T. & Tsai, J.J.P. (Eds.). Ubiquitous Intelligence and Computing (1174–1185). Berlín, Alemania: Springer. http://dx.doi.org/10.1007/11833529_118
dc.relation/*ref*/O'Connor, M., Cosley, D., Konstan, J.A. & Riedl, J. (2002). PolyLens: A Recommender System for Groups of Users. En Prinz, W., Jarke, M., Rogers, Y., Schmidt, K. & Wulf, V. (Eds.). ECSCW 2001 (199–218). Países Bajos: Springer. http://dx.doi.org/10.1007/0-306-48019-0_11
dc.relation/*ref*/McCarthy, J.F. & Anagnost, T.D. (1998). MusicFX: an arbiter of group preferences for computer supported collaborative workouts. Proceedings of the 1998 ACM conference on Computer supported cooperative work, Nueva York, NY, EE.UU., pp. 363–372. http://dx.doi.org/10.1145/289444.289511
dc.relation/*ref*/Kabassi, K. (febrero, 2010). Personalizing recommendations for tourists. Telematics Informatics, 27(1), pp. 51–66. http://dx.doi.org/10.1016/j.tele.2009.05.003
dc.relation/*ref*/Ardissono, L., Goy, A., Petrone, G., Segnan, M. & Torasso, P. (2003). Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices. Appl. Artif. Intell., 17(8–9), pp. 687–714. http://dx.doi.org/10.1080/713827254
dc.relation/*ref*/Kim, J.K., Kim, H.K., Oh, H.Y. & Ryu, Y.U. (2010). A group recommendation system for online communities. Int. J. Inf. Manag., 30(3), pp. 212–219. http://dx.doi.org/10.1016/j.ijinfomgt.2009.09.006
dc.relation/*ref*/Christensen, I.A. & Schiaffino S. (2011). Entertainment recommender systems for group of users. Expert Syst. Appl., 38(11), pp. 14127–14135. http://dx.doi.org/10.1016/j.eswa.2011.04.221
dc.relation/*ref*/Crossen, A., Budzik, J. & K. Hammond, J. (2002). Flytrap: intelligent group music recommendation. Proceedings of the 7th international conference on Intelligent user interfaces, Nueva York, NY, EE.UU., pp. 184–185. http://dx.doi.org/10.1145/502716.502748
dc.relation/*ref*/Chao, D. L., Balthrop, J. & Forrest, S. (2005). Adaptive radio: achieving consensus using negative preferences. Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work, Nueva York, NY, EE.UU., pp. 120–123. http://dx.doi.org/10.1145/1099203.1099224
dc.relation/*ref*/Mahout. (2013). Apache Mahout: Scalable machine learning and data mining. Recuperado el 5 de julio de 2013, de http://mahout.apache.org/.
dc.relation/*ref*/Ekstrand, M.D., Ludwig, M., Kolb, J. & Riedl, J.T. (2011). LensKit: a modular recommender framework. Proceedings of the fifth ACM conference on Recommender systems, Nueva York, NY, EE.UU., pp. 349–350. http://dx.doi.org/10.1145/2043932.2044001
dc.relation/*ref*/Weka. (2013). Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Recuperado el 5 de julio de 2013, de http://www.cs.waikato.ac.nz/ml/weka/.
dc.relation/*ref*/MOVL. (2013). MOVL. Recuperado el 5 de julio de 2013, de http://movl.com/.
dc.relation/*ref*/Zeebox. (2013). Zeebox - Get the free app. Recuperado el 5 de julio de 2013, de http://zeebox.com/.
dc.relation/*ref*/Yoon, C., Um, T. & Lee, H. (2012). Classification of N-Screen Services and its standardization. 2012 14th International Conference on Advanced Communication Technology (ICACT), pp. 597–602.
dc.relation/*ref*/Holleis, P., Broll, G. & Böhm, S. (2010). Advertising with NFC. Workshop on Pervasive Advertising and Shopping, in conjunction with the 8th International Conference on Pervasive Computing (Pervasive 2010), Helsinki, Finlandia.
dc.relation/*ref*/Rui, J., Otero, N., Izadi, S. & Harper, R. (2008). Instant Places: Using Bluetooth for Situated Interaction in Public Displays. Ieee Pervasive Comput., 7(4), pp. 52–57. http://dx.doi.org/10.1109/MPRV.2008.74
dc.relation/*ref*/Mahato, H., Kern, D., Holleis, P. & Schmidt, A. (2008). Implicit personalization of public environments using bluetooth. CHI '08 Extended Abstracts on Human Factors in Computing Systems, Nueva York, NY, EE.UU., pp. 3093–3098. http://dx.doi.org/10.1145/1358628.1358813
dc.relation/*ref*/Strohbach, M., Bauer, M., Martin, M. & Hebgen, B. (2011). Managing Advertising Context. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (185–205). Londres, Inglaterra: Springer. http://dx.doi.org/10.1007/978-0-85729-352-7_9
dc.relation/*ref*/Nguyen, Q.N. & Hoang P.M. (2010). Push delivery of product promotion advertisements to mobile users. Proceedings of the Pervasive Advertising and Shopping 2010 Workshop, Helsinki, Finlandia.
dc.relation/*ref*/Otero, N. & Rui, J. (2009). Worth and Human Values at the Centre of Designing Situated Digital Public Displays. Int. J. Adv. Pervasive Ubiquitous Comput., 1(4), pp. 1–13, 34. http://dx.doi.org/10.4018/japuc.2009100101
dc.relation/*ref*/Santos, P., Ribeiro, F.R. & Metrolho, J. (2012). Using pervasive computing technologies to deliver personal and public ads in public spaces. 2012 7th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6.
dc.relation/*ref*/Michelis, D. & Send, H. (2009). Engaging Passers-by with Interactive Screens-A Marketing Perspective. GI Jahrestagung, pp. 3875–3881.
dc.relation/*ref*/Kaasinen, A. & Yoon, Y.I. (2012). Mobile advertising model in N-Screen environment for CSCW. 2012 7th International Conference on Computing and Convergence Technology (ICCCT), pp. 140–143.
dc.relation/*ref*/Rui, J. & Soares, A.M. (2010). Towards new advertising models for situated displays. Proceedings of the 3rd workshop on Pervasive Advertising, Helsinki, Finlandia.
dc.relation/*ref*/May, M., Körner, C., Hecker, D., Pasquier, M., Hofmann, U. & Mende, F. (2009). Modelling Missing Values for Audience Measurement in Outdoor Advertising Using GPS Data. GI Jahrestagung, pp. 3993–4006.
dc.relation/*ref*/Schrammel, J., Mattheiss, E., Döbelt, S., Paletta, L., Almer, A. & Tscheligi, M. (2011). Attentional Behavior of Users on the Move Towards Pervasive Advertising Media. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (287–307). Londres, Inglaterra: Springer. http://dx.doi.org/10.1007/978-0-85729-352-7_14
dc.relation/*ref*/Turow, J., King, J., Hoofnagle, C.J., Bleakley, A. & Hennessy, M. (2013). Americans Reject Tailored Advertising and Three Activities that Enable It by:: SSRN. Recuperado el 7 de julio de 2013, de http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1478214.
dc.relation/*ref*/FTC. Federal Trade Commission. (febrero, 2009). Staff Report: "Self-Regulatory Principles For Online Behavioral Advertising: Tracking, Targeting, and Technology" Recuperado el 7 de julio de 2013, de http://www.ftc.gov/os/2009/02/ P085400behavadreport.pdf.
dc.relation/*ref*/Wirespring. (2013). Digital signage networks must guarantee viewer privacy. Recuperado el 7 de julio de 2013, de http://www.wirespring.com/dynamic_digital_signage_and_interactive_kiosks_journal/articles/Digital_signage_networks_must_guarantee_viewer_privacy-569.html.
dc.relation/*ref*/TruMedia. (2013). Facial Recognition Boards Will Never Record, Share Data - MediaBuyerPlanner. Recuperado el 7 de julio de 2013, de http://www.mediabuyerplanner.com/entry/34111/trumedia-facial-recognition-boards-will-never-record-share-data/.
dc.relation/*ref*/Coursey, D. (2013). After Criticism, Facebook Tweaks Friends List Privacy Options, PCWorld. Recuperado el 7 de julio de 2013, de http://www.pcworld.com/article/184418/After_Criticism_Facebook_Changes_Friend_List_Privacy_Options.html.
dc.relation/*ref*/Freudiger, J., Vratonjic, N. & Hubaux J.P. (2009) Towards privacy-friendly online advertising. IEEE Web 2.0 Security and Privacy (W2SP).
dc.relation/*ref*/Boucher, R. (2013). Behavioral ads: The need for privacy protection. Recuperado el 7 de julio de 2013, de http://thehill.com/special-reports/technology-september-2009/60253-behavioral-ads-the-need-for-privacy-protection.
dc.relation/*ref*/Smith, G. (2013). Taking Consumer Privacy Seriously, POPAI's Digital Signage Group Releases Code of Conduct. Recuperado el 7 de julio de 2013, de http://www.popai.com/2010/02/08/taking-consumer-privacy-seriously-popais-digital-signage-group-releases-code-of-conduct/?cat_id=.
dc.relation/*ref*/Geiger, H.L. (2011). A Standard for Digital Signage Privacy. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (103–117). Londres, Inglaterra: Springer. http://dx.doi.org/10.1007/978-0-85729-352-7_5
dc.relation/*ref*/Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H. & Barocas, S. (febrero, 2010). Adnostic: Privacy Preserving Targeted Advertising. NDSS.
dc.relation/*ref*/Guha, S., Reznichenko, A., Tang, K., Haddadi, H. & Francis, P. (2009). Serving Ads from localhost for Performance, Privacy, and Profit. HotNets.
dc.relation/*ref*/Komulainen, H., Ristola, A. & Still, J. (2006). Mobile advertising in the eyes of retailers and consumers - empirical evidence from a real-life experiment. International Conference on Mobile Business. ICMB '06, pp. 37–37. http://dx.doi.org/10.1109/icmb.2006.31
dc.relation/*ref*/Haddadi, H., Hui, P. & Brown, I. (2010). MobiAd: private and scalable mobile advertising. Proceedings of the fifth ACM international workshop on Mobility in the evolving internet architecture, Nueva York, NY, EE.UU., pp. 33–38. http://dx.doi.org/10.1145/1859983.1859993
dc.relation/*ref*/Duan, W., Gu, B. & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. J. Retail., 84(2), pp. 233–242. http://dx.doi.org/10.1016/j.jretai.2008.04.005
dc.relation/*ref*/Wang, M.C.H., Wang E.S.T., Cheng, J.M.S. & Chen, A.F.L. (2009). Information quality, online community and trust: a study of antecedents to shoppers' website loyalty. Int. J. Electron. Mark. Retail., 2(3), pp. 203–219. http://dx.doi.org/10.1504/IJEMR.2009.021806
dc.relation/*ref*/Jepsen, A.L. (2006). Information Search in Virtual Communities: Is it Replacing Use of Off‐Line Communication?. J. Mark. Commun., 12(4), pp. 247–261. http://dx.doi.org/10.1080/13527260600694308
dc.relation/*ref*/Jasen, J. (2013). Online Product Research. Pew Research Center's Internet & American Life Project. Recuperado el 7 de julio de 2013, de http://www.pewinternet.org/Reports/2010/Online-Product-Research.aspx.
dc.relation/*ref*/Ailawadi, K. L., Beauchamp, J. P., Donthu, N., Gauri, D. K., & Shankar, V. (2009). Communication and Promotion Decisions in Retailing: A Review and Directions for Future Research. J. Retail., 85(1), pp. 42–55. http://dx.doi.org/10.1016/j.jretai.2008.11.002
dc.relation/*ref*/Bustos, L. (2013). 110 Ways Retailers are Using Social Media Marketing. Recuperado el 7 de julio de 2013, de http://www.getelastic.com/social-media-examples/.
dc.relation/*ref*/Grove, J.V. (2013). Mayors of Starbucks Now Get Discounts Nationwide with Foursquare. Recuperado el 7 de julio de 2013, de http://mashable.com/2010/05/17/starbucks-foursquare-mayor-specials/.
dc.relation/*ref*/Spiegler, E.D., Hildebrand, C. & Michahelles, F. (2011). Social Networks in Pervasive Advertising and Shopping. En Müller, J., Alt, F., & Michelis, D., (Eds.). Pervasive Advertising (207–225). Londres, Inglaterra: Springer. http://dx.doi.org/10.1007/978-0-85729-352-7_10
dc.relation/*ref*/Ferdinando, A.Di., Rosi, A., Lent, R., Manzalini, A. & Zambonelli, F. (2009). MyAds: A system for adaptive pervasive advertisements. Pervasive Mob. Comput., 5(5), pp. 385–401. http://dx.doi.org/10.1016/j.pmcj.2009.06.006
dc.relation/*ref*/Sinha, R.R. & Swearingen, K. (2001). Comparing Recommendations Made by Online Systems and Friends. DELOS workshop: personalisation and recommender systems in digital libraries, vol. 106.
dc.relation/*ref*/Ricci, F., Rokach, L. & Shapira, B. (2011). Introduction to Recommender Systems Handbook. En Ricci, F., Rokach, L., Shapira, B. & Kantor, P.B. (Eds.). Recommender Systems Handbook. Nueva York, NY, EE.UU: Springer. pp. 1–35. http://dx.doi.org/10.1007/978-0-387-85820-3
dc.relation/*ref*/Groh, G. & Ehmig, C. (2007). Recommendations in taste related domains: collaborative filtering vs. social filtering. Proceedings of the 2007 international ACM conference on Supporting group work, Nueva York, NY, EE.UU., pp. 127–136. http://dx.doi.org/10.1145/1316624.1316643
dc.relation/*ref*/Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S. & Ofek-Koifman, S. (2009). Personalized recommendation of social software items based on social relations. Proceedings of the third ACM conference on Recommender systems, Nueva York, NY, EE.UU., pp. 53–60. http://dx.doi.org/10.1145/1639714.1639725
dc.relation/*ref*/Avesani, P., Massa, P. & Tiella, R. (2005). Moleskiing it: a trust-aware recommender system for ski mountaineering. Int. J. Infonomics, vol. 20.
dc.relation/*ref*/O'Donovan, J. & Smyth, B. (2005). Trust in recommender systems. Proceedings of the 10th international conference on Intelligent user interfaces, Nueva York, NY, EE.UU., pp. 167–174. http://dx.doi.org/10.1145/1040830.1040870
dc.relation/*ref*/Victor, P., Cornelis, C., De Cock, M. & Teredesai, A. M. (2008). Key figure impact in trust-enhanced recommender systems. Ai Commun, 21(2–3), pp. 127–143.
dc.relation/*ref*/Clemons, E. K., Barnett, S. & Appadurai, A. (2007). The future of advertising and the value of social network websites: some preliminary examinations, Proceedings of the ninth international conference on Electronic commerce, Nueva York, NY, EE.UU., pp. 267–276. http://dx.doi.org/10.1145/1282100.1282153
dc.relation/*ref*/Yang, W.S. & Dia, J.B. (2008). Discovering cohesive subgroups from social networks for targeted advertising. Expert Syst. Appl., 34(3), pp. 2029–2038. http://dx.doi.org/10.1016/j.eswa.2007.02.028
dc.relation/*ref*/Yang, W.S., Dia, J.B., Cheng, H.C. & Lin, H.T. (2006). Mining Social Networks for Targeted Advertising. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. HICSS '06, 6, p. 137a.
dc.rightsDerechos de autor 2016 Ciencia e Ingeniería Neogranadinaes-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0es-ES
dc.sourceCiencia e Ingenieria Neogranadina; Vol. 24 No. 1 (2014); 116-142en-US
dc.sourceCiencia e Ingeniería Neogranadina; Vol. 24 Núm. 1 (2014); 116-142es-ES
dc.sourceCiencia e Ingeniería Neogranadina; v. 24 n. 1 (2014); 116-142pt-BR
dc.source1909-7735
dc.source0124-8170
dc.subjectPervasive advertisingen-US
dc.subjectsmart TVen-US
dc.subjectsmartphone.en-US
dc.subjectPublicidad ubicuaes-ES
dc.subjectsmart TVes-ES
dc.subjectsmartphone.es-ES
dc.titleConceptual model for pervasive advertising supported on a Smart TV- SmartPhone cooperation framework.en-US
dc.titleModelo conceptual para el despliegue de publicidad ubicua soportado en un esquema de cooperación Smart TV- SmartPhone.es-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


Este ítem pertenece a la siguiente institución