Abstract
Mind Cognitive Impairment is one of the most common clinical manifestations affecting the elderly. In this paper, we report the work in progress (in the frame of our SENIOR project) to provide elderly with new Nudge theory driven advices for influencing their interest to a conscious and functional participation to “targeted” social communities where suggestions on the overall wellness can be shared, recognized as usefull by users and supported by health care providers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
See, for example, [10] for further details.
References
Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4, 2 (2003)
Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: Machine Learning, Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California, USA, pp. 38–46 (1995)
Barabási, A.-L., Albert, R.: GA: a package for genetic algorithms in R. Science 286, 509–512 (1999)
Bollobas, B.: Random Graphs. Cambridge University Press, Cambridge (2001)
Cava, C., Zoppis, I., Gariboldi, M., Castiglioni, I., Mauri, G., Antoniotti, M.: Copy-number alterations for tumor progression inference, pp. 104–109 (2013)
Cava, C., Zoppis, I., Gariboldi, M., Castiglioni, I., Mauri, G., Antoniotti, M.: Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference. J. Clin. Bioinform. 4, 2 (2014)
Dondi, R., Mauri, G., Zoppis, I.: Clique editing to support case versus control discrimination. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. SIST, vol. 56, pp. 27–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39630-9_3
Dondi, R., Mauri, G., Zoppis, I.: Orthology correction for gene tree reconstruction: theoretical and experimental results. Procedia Comput. Sci. 108, 1115–1124 (2017)
Dondi, R., Mauri, G., Zoppis, I.: On the tractability of finding disjoint clubs in a network. Theor. Comput. Sci. 777, 243–251 (2019)
Eiben, A.E., Smith, J.E., et al.: Introduction to Evolutionary Computing, vol. 53. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-05094-1
He, X., Chen, T., Kan, M.-Y., Chen, X.: TriRank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661–1670. ACM (2015)
Heckel, R., Vlachos, M., Parnell, T., Dünner, C.: Scalable and interpretable product recommendations via overlapping co-clustering. In: IEEE 33rd International Conference on Data Engineering (ICDE), pp. 1033–1044. IEEE (2017)
Hüffner, F., Komusiewicz, C., Nichterlein, A.: Editing graphs into few cliques: complexity, approximation, and kernelization schemes. In: Dehne, F., Sack, J.-R., Stege, U. (eds.) WADS 2015. LNCS, vol. 9214, pp. 410–421. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21840-3_34
Kolaczyk, E.D.: Statistical Analysis of Network Data: Methods and Models. Springer, New York (2009). https://doi.org/10.1007/978-0-387-88146-1
Leonard, T.C., Thaler,R.H., Sunstein, C.R.: Nudge: improving decisions about health, wealth, and happiness (2008)
Long, B., Zhang, Z., Yu, P.S.: Relational Data Clustering: Models, Algorithms, and Applications. Chapman & Hall/CRC, Boca Raton (2010)
Mitchell, M.: An Introduction to Genetic Algorithms. Complex Adaptive Systems. MIT press, Cambridge (1996)
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303–308 (1999)
Scrucca, L.: GA: a package for genetic algorithms in R. J. Stat. Softw. 53(4), 1–37 (2013)
Sharan, R., Shamir, R.: Center CLICK: a clustering algorithm with applications to gene expression analysis. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, La Jolla/San Diego, CA, USA, 19–23 August 2000, pp. 307–316 (2000)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. 100, 12123–12128 (2003)
Wang, X., He, X., Feng, F., Nie, L., Chua, T.-S.: TEM: tree-enhanced embedding model for explainable recommendation. In: Proceedings of the 2018 WWW Conference on WWW, pp. 1543–1552. International WWW Conference Steering Committee (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zoppis, I. et al. (2019). Nudges Driven Networks: Towards More Acceptable Recommendations for Inducing Targeted Social Communities. In: El Yacoubi, S., Bagnoli, F., Pacini, G. (eds) Internet Science. INSCI 2019. Lecture Notes in Computer Science(), vol 11938. Springer, Cham. https://doi.org/10.1007/978-3-030-34770-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-34770-3_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34769-7
Online ISBN: 978-3-030-34770-3
eBook Packages: Computer ScienceComputer Science (R0)