Learning a Probabilistic Topology Discovering Model for Scene CategorizationĪuthor(s): Zhang, L. Two-Stage Orthogonal Least Squares Methods for Neural Network ConstructionĪuthor(s): Zhang, L. Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial LocomotionĪuthor(s): Araujo, A.F.R. Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch AlignmentĪuthor(s): Gao, Y. ACM Press/Addison-Wesley Publishing Co.1.
In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1995), pp. Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: IEEE International Conference Systems, Man, and Cybernetics (SMC 2013), pp. Gerogiannis, V.C., Karageorgos, A., Liu, L., Tjortjis, C.: Personalised fuzzy recommendation for high involvement products. Monahan, T., Fisher, J.A.: Benefits of “observer effects”: lessons from the field. (eds.) Encyclopedia of Machine Learning, pp. Melville, P., Sindhwani, V.: Recommender systems. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Nadimi-Shahraki, M.-H., Bahadorpour, M.: Cold-start problem in collaborative recommender systems: efficient methods based on ask-to-rate technique. MovieLens: Non-commercial, personalized movie recommendations. ACM, New York (2006)Ĭantador, I., Fernández-Tobías, I., Bellogín, A.: Relating personality types with user preferences in multiple entertainment domains. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2006), pp. McNee, S.M., Riedl, J., Konstan, J.A.: Making recommendations better: an analytic model for human-recommender interaction. 4(1), 26 (1992)īurke, R.: Hybrid recommender systems: survey and experiments. Goldberg, L.R.: The development of markers for the Big-Five factor structure. Our findings show that personalization provides better recommendations, even though some extra user input is required upfront. Evaluation results showed that users preferred the 50/50 system 3.6% more than the state of the art method. We propose a method and developed the 50/50 recommender system, which combines the Big Five personality test with an existing movie recommender, and used it on a renowned movie dataset. Previous research attempted to incorporate personality in Recommender systems, but no actual implementation appears to have been achieved. We introduce the concept of combining collaborative techniques with a personality test to provide more personalized movie recommendations. Our main goal is to examine the role of personality in Movie Recommender systems. This work checks the hypothesis that taking personality into account can improve recommendation quality. Recommendation systems offer valuable assistance with selecting products and services.
0 kommentar(er)
