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Modeling the Structure of Recommending Interfaces with Adjustable Influence on Users

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Intelligent Information and Database Systems (ACIIDS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7803))

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Abstract

Recommending interfaces are usually integrated with marketing processes and are targeted to increasing sales with the use of persuasion and influence methods to motivate users to follow recommendations. In this paper is presented an approach based on decomposition of recommending interface into elements with adjustable influence levels. A fuzzy inference model is proposed to represent the system characteristics with the ability to adjust the parameters of the interface to acquire results and increase customer satisfaction.

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References

  1. Barneveld, J., Setten, J.: Designing usable interfaces for TV recommender systems. In: Ardissono, L., Kobsa, A., Maybury, M. (eds.) Personalized Digital Television: Targeting Programs to Individual Viewers. Human-Computer Interaction Series, vol. 6, pp. 259–286. Kluwer, Dordrecht (2004)

    Chapter  Google Scholar 

  2. Chen, L., Pu, P.: Eye-Tracking Study of User Behavior in Recommender Interfaces. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 375–380. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Fogg, B.J.: A behavior model for persuasive design. In: 4th International Conference on Persuasive Technology Persuasive 2009, pp. 40–47. ACM, New York (2009)

    Google Scholar 

  4. Hayes, C., Massa, P., Avesani, P., Cunningham, P.: An online evaluation framework for recommender systems. In: Workshop on Personalization and Recommendation in E-Commerce, Malaga, Spain, pp. 57–67. Springer, Heidelberg (2002)

    Google Scholar 

  5. Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the ACM Conference on Computer Supported Cooperative, pp. 241–250. ACM Press, New York (2000)

    Google Scholar 

  6. Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference Systems. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  Google Scholar 

  7. Jankowski, J.: Integration of Collective Knowledge in Fuzzy Models Supporting Web Design Process. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part II. LNCS (LNAI), vol. 6923, pp. 395–404. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Jankowski, J.: Analysis of Multiplayer Platform Users Activity Based on the Virtual and Real Time Dimension. In: Datta, A., Shulman, S., Zheng, B., Lin, S.-D., Sun, A., Lim, E.-P. (eds.) SocInfo 2011. LNCS, vol. 6984, pp. 312–315. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. Jawdat, A., Obeidat, Q., Aljanaby, A.: On The Design of User Experience Based Persuasive Systems. Computer and Information Science 4(4), 90–99 (2011)

    Article  Google Scholar 

  10. Kazienko, P., Musiał, K., Kajdanowicz, T.: Multidimensional Social Network in the Social Recommender System. IEEE Transactions on Systems. Man, and Cybernetics - Part A: Systems and Humans 41(4), 746–759 (2011)

    Article  Google Scholar 

  11. Kukla, G., Kazienko, P., Bródka, P., Filipowski, T.: SocLaKE - Social Latent Knowledge Explorator. The Computer Journal 55(3), 258–276 (2012)

    Article  Google Scholar 

  12. Mamdani, E.H., Folger, T.A., Gaines, R.R.: Fuzzy reasoning and its applications. Academic Press, London (1981)

    MATH  Google Scholar 

  13. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101. ACM, New York (2006)

    Google Scholar 

  14. Montaner, M., Lopez, B., De La Rosa, J.L.: A taxonomy of recommender agents on the Internet. Artificial Intelligence Review 19(4), 285–330 (2003)

    Article  Google Scholar 

  15. Nageswara, R.K., Talwar, V.G.: Application domain and functional classification of recommender systems - a survey. Journal of Library & Information Technology 28(3), 17–35 (2008)

    Google Scholar 

  16. Nanou, T., Lekakos, G., Fouskas, K.: The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system. Multimedia Systems 16, 219–230 (2010)

    Article  Google Scholar 

  17. Ozok, A.A., Fan, Q., Norcio, A.: Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model. Behaviour and Information Technology 29(1), 57–83 (2010)

    Article  Google Scholar 

  18. Piegat, A.: Fuzzy Modeling and Control, Verlag Heildelberg, New York (2001)

    Google Scholar 

  19. Pu, P., Chen, L.: Trust building with explanation interfaces. In: Proceedings of the 11th International Conference on Intelligent user Interfaces (IUI 2006), pp. 93–100. ACM, New York (2006)

    Google Scholar 

  20. Qinyu, L.: Empirical findings on persuasiveness of recommender systems for customer decision support in electronic commerce, PHD dissertation, Missisipi State University, USA (2005)

    Google Scholar 

  21. Różewski, P., Małachowski, B.: Competence Management in Knowledge-Based Organisation: Case Study Based on Higher Education Organisation. In: Karagiannis, D., Jin, Z. (eds.) KSEM 2009. LNCS (LNAI), vol. 5914, pp. 358–369. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  22. Swearingen, K., Sinha, R.: Interaction design for recommender systems. In: Proceedings of the 4th ACM Conference on Designing Interactive Systems (DIS 2002). ACM, New York (2006)

    Google Scholar 

  23. Swearingen, K., Sinha, R.: Beyond algorithms: An HCI perspective on recommender systems. In: ACM Workshop on Recommender Systems SIGIR 2001. ACM, New York (2001)

    Google Scholar 

  24. Tintarev, N., Masthoff, J.: A Survey of Explanations in Recommender Systems. In: Proceedings of the IEEE 23rd International Conference on Data Engineering (ICDEW 2007), pp. 801–810. IEEE Computer Society, IEEE Press, Washington (2007)

    Google Scholar 

  25. Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 33–353 (1965)

    Article  MathSciNet  Google Scholar 

  26. Zhang, X., Wang, H.: Recommender Systems for B2B Electronic Commerce. Communications of the IIMA 5(4), 53–62 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, ul. Zolnierska 49, 71-410, Szczecin, Poland

    Jaroslaw Jankowski

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  1. Jaroslaw Jankowski
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Systems, Department of Software Engineering, Universiti Teknologi Malaysia, 81310, Johar Baharu, Johor, Malaysia

    Ali Selamat  & Habibollah Haron  & 

  2. Institute of Informatics, Division of Knowledge Managements Systems, Wrocław University of Technology, Str. Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

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Jankowski, J. (2013). Modeling the Structure of Recommending Interfaces with Adjustable Influence on Users. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_44

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  • DOI: https://doi.org/10.1007/978-3-642-36543-0_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36542-3

  • Online ISBN: 978-3-642-36543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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