Abstract
As Internet technology permeates into our daily lives, the importance of online recommendation grows quite rapidly. Needless to say, the success of the online recommendation mechanism depends heavily on how much users are satisfied by the recommendation results. Moreover, it is essential that users feel less psychological reactance about the recommendation results when it comes to the effectiveness of the online recommendation mechanism. To accomplish those two goals, we propose a new online recommendation mechanism on the basis of the General Bayesian Network (GBN). It is common that users have a large number of personal wants and needs in their minds when using the recommendation mechanism. In this case, GBN is very effective in selecting a small number of relevant factors and organizing a set of causal relationships among them. An advantage of GBN like this allows users to have much satisfaction about the recommendation results, and enjoy rich implications from them. We implemented the proposed GBN-driven online recommendation mechanism (GBNOREM), and tested with users who are suffering from minor health problems and therefore in need of proper menus at the right restaurants. Empirical results clearly revealed that the proposed GBNOREM can enhance users’ perceived satisfaction about the recommendation results, while reducing the level of users’ perceived psychological reactance.
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References
Hostler, E.R., Yoon, V.Y., Guimaraes, T.: Recommendation Agent Impact on Consumer Online Shopping: The Movie Magic Case Study. Expert Systems with Applications 39, 2989–2999 (2012)
Ochi, P., Rao, S., Takayama, L.: Predictors of User Perceptions of Web Recommender Systems: How the Basis for Generating Experience and Search Product Recommenda-tions Affects User Responses. International Journal of Human-Computer Studies 68, 472–482 (2010)
Lee, Y., Huang, F.: Recommender System Architecture for Adaptive Green Marketing. Expert Systems with Applications 38, 9696–9703 (2011)
Kim, J., Lee, J., Park, J., Lee, Y., Rim, K.: Design of Diet Recommendation System for Healthcare Service Based on User Information. In: Fourth International Conference on Computer Sciences and Convergence Information Technology (ICCIT), pp. 516–518 (2009)
Bobadilla, J., Serradilla, F., Bernal, J.: A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems. Knowledge-Based Systems 23, 520–528 (2010)
Grandpre, J., Alvaro, E.M., Burgoon, M.: Adolescent Reactance and Anti-Smoking Campaigns: A Theoretical Approach. Health Communication 15, 349–366 (2003)
Vekariya, V., Kulkarni, G.R.: Notice of Violation of IEEE Publication Principles Hybrid Recommender Systems: Survey and Experiments. In: Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 469–473 (2012)
Carrer-Neto, W., Hernández-Alcaraz, M.L., Valencia-García, R.: Social Knowledge-Based Recommender System: Application to the Movies Domain. Expert Systems with Applications 39, 10990–11100 (2012)
Danna, K., Griffin, R.W.: Health and Well-being in the Workplace: A Review and Synthesis of the Literature. Journal of Management 25, 357–384 (1999)
Hsu, F., Lin, Y., Ho, T.: Design and Implementation of an Intelligent Recommendation System for Tourist Attractions: The Integration of EBM Model, Bayesian Network and Google Maps. Expert Systems with Applications 39, 3257–3264 (2012)
Nadkarni, S., Shenoy, P.P.: A Causal Mapping Approach to Constructing Bayesian Networks. Decision Support Systems 38, 259–281 (2004)
Roubroeks, M., Midden, C., Ham, J.: Does it make a Difference Who Tells You what to do? Exploring the Effect of Social Agency on Psychological Reactance. In: Proceedings of the 4th International Conference on Persuasive Technology, Pervasive 2009, article no.15 (2009)
Brehm, J.W.: A theory of psychological reactance. Academic Press, New York (1966)
Miller, C.H., Lane, L.T., Deatrick: Psychological Reactance and Promotional Health Messages: The Effects of Controlling Language, Lexical Concreteness, and the Restoration of Freedom. Human Communication Research 33, 219–240 (2007)
Cooper, G., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Net-works from Data. Machine Learning 9, 309–347 (1992)
Aldag, R.J., Power, D.J.: An Empirical Assessment of Computer-Assisted Decision Analysis. Decision Sciences 17, 572–588 (1986)
Bruner II, G.C.: Standardization & Justification: Do Aad Scales Measure Up? Journal of Current Issues & Research in Advertising 20, 1–19 (1998)
Li, H., Daugherty, T., Biocca, F.: The Role of Virtual Experience in Consumer Learning. Journal of Consumer Psychology 13, 395–407 (2003)
Hong, S.: Hong’s Psychological Reactance Scale: A Further Factor Analytic Validation. Psychological Reports 70, 512–514 (1992)
Hong, S., Faedda, S.: Refinement of the Hong Psychological Reactance Scale. Educational and Psychological Measurement 56, 173–182 (1996)
Fitzsimons, G.J.: Consumer Response to Stockouts. Journal of Consumer Research 27, 249–266 (2000)
James, L.R., Demaree, R.G., Wolf, G.: Estimating within-Group Interrater Reliability with and without Response Bias. Journal of Applied Psychology 69, 85–98 (1984)
Fitzsimons, G.J., Lehmann, D.R.: Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses. Marketing Science 23, 82–94 (2004)
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Chung, D., Lee, K.C., Seong, S.C. (2012). Empirical Analysis of General Bayesian Network-Driven Online Recommendation Mechanism: Emphasis on User Satisfaction and Psychological Reactance. In: Kim, Th., Ko, Ds., Vasilakos, T., Stoica, A., Abawajy, J. (eds) Computer Applications for Communication, Networking, and Digital Contents. FGCN 2012. Communications in Computer and Information Science, vol 350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35594-3_49
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DOI: https://doi.org/10.1007/978-3-642-35594-3_49
Publisher Name: Springer, Berlin, Heidelberg
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