Fanjuan Shi
ABSTRACT
Current personalized recommendation approaches have reached a limit of effectiveness. By incorporating cognitive and behavioral knowledge, personalized recommender systems could be friendlier and more human-centric, which can potentially enhance user experience and loyalty. Our research proposes a psycho-cognitive method to recommend items based on users' emotion state and center of interest. Meanwhile, we propose a novel behavioral concept (i.e. "wandering status") and highlight its importance in online behavioral research.
- Burke, R. (2007). Hybrid web recommender systems. In The adaptive web LNCS 4321, 377--408. Springer Berlin Heidelberg. Google Scholar
Digital Library
- Leavitt, N. (2006). Recommendation technology: Will it boost e-commerce?. Computer, 39(5), 13--16. Google ScholarDigital Library
- Poirier, D. (2011). Thèse de Doctorat en Informatique intitule "Des Textes Communautaires à la Recommendation". Soutenance effectuée au LIFO (Laboiratoire d'Informatique fondamentale d'Orléans).Google Scholar
- Marini, JL. (2010). Capitalisation d'expériences pour l'indexation et la recherche d'information dans le domaine de la Gestion Electronique de Documents. Thèse de doctorat, Université Jean Moulin (Lyon 3).Google Scholar
- Marini, JL. and Shi, FJ. (2014). Do we need to believe Data/Tangible or Emotional/Intuition?. In: IFLA WLIC 2014, Lyon, France.Google Scholar
- Joines, JL., Scherer, CW., Scheufele DA. (2003). Exploring motivations for consumer web use and their implications for e-commerce. Journal of Consumer Marketing, 90--108.Google ScholarCross Ref
- Moraux, H., and de Tassigny, PDM. (2012). Typologie des pratiques de merchandising online permettant de favoriser la conversion des clients par la stimulation des achats d'impulsion. 15e colloque Etienne Thil, Lille.Google Scholar
- Khaslavsky, J., & Shedroff, N. (1999). Understanding the seductive experience. Communications of the ACM, 42(5), (pp. 45--49). Google ScholarDigital Library
- Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2011). Recommender systems, consumer preferences, and anchoring effects. In RecSys 2011 Workshop on Human Decision Making in Recommender Systems (pp. 35--42).Google Scholar
- Maim, Enrico (2012). Methods and systems for searching and associating information resources such as web pages. U.S. Patent No 8,290,956.Google Scholar
- Gabrieli, J. D. (1998). Cognitive neuroscience of human memory. Annual review of psychology, 49(1), 87--115.Google Scholar
- Mohs, R. C. (2007). How human memory works.Google Scholar
- Squire, L. R. (1986). Mechanisms of memory. Science, 232(4758), 1612--1619.Google ScholarCross Ref
- Dudai, Y., & Fitzpatrick, S. M. (Eds.). (2007). Science of memory: Concepts. New-York:: Oxford University Press.Google Scholar
- Courbet, D., & Fourquet-Courbet, M. P. (2014). Les influences non conscientes de la publicité et de la communication marketing: Etat des recherches et nouvelles perspectives (Doctoral dissertation, Rapport de recherches, CNRS (programme Société de l'Information), Université d'Aix-Marseille, Institut de Recherche en Sciences de l'Information et de la Communication (IRSIC), 39 p.).Google Scholar
- Buzan, T., & North, V. (1997). Mindmapping. hpt.Google Scholar
- Huang, Y. F., & Kuo, F. Y. (2012). How impulsivity affects consumer decision-making in e-commerce. Electronic Commerce Research and Applications, 11(6), 582--590. Google ScholarDigital Library
- Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453--458.Google ScholarCross Ref
- Kahneman, D. (2011). Thinking, fast and slow. Macmillan.Google Scholar
- Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. American psychologist, 58(9), 697.Google Scholar
- Damasio, A. (2008). Descartes' error: Emotion, reason and the human brain. Random House..Google Scholar
- Norman, D. (2002). Emotion & design: attractive things work better. interactions, 9(4), (pp. 36--42). Google ScholarDigital Library
- Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). The "Trier Social Stress Test"--a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28(1--2), 76--81.Google Scholar
- Labbé, E., Schmidt, N., Babin, J., & Pharr, M. (2007). Coping with stress: the effectiveness of different types of music. Applied psychophysiology and biofeedback, 32(3--4), 163--168.Google Scholar
- Zimmermann, P., Guttormsen, S., Danuser, B., & Gomez, P. (2003). Affective computing--a rationale for measuring mood with mouse and keyboard. International journal of occupational safety and ergonomics, 9(4), 539--551.Google Scholar
Index Terms
- Towards a Psycho-Cognitive Recommender System
Recommendations
Improving Accuracy of Recommender System by Item Clustering
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A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and SystemsIn today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...
Trust based recommender system using ant colony for trust computation
Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. Due to data sparsity of the user-item rating matrix, the process of finding ...
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