Abstract
In the social economics field that deals with the nonprofit organizations (NPOs), the fund-raising is a crucial activity that requires the management of a great number of quantitative and qualitative information regarding Donors and Contacts (i.e., potential donors). This data is normally stored in a structured database (DB) by each NPO, and it is clear that their effective processing by data science methods significantly improves the performances of the fund-raising campaigns. For this reason, the use of rigorous mathematical methods and decision support systems (DSS) has been playing a very important role in this context. The process of fund-raising is very complex and in part different depending on the characteristics of each organization. However, a common important feature is the role of the Contacts, and therefore, the method for turning the Contacts into actual Donors contextualized in the so-called giving pyramid is crucial from a strategic point of view. Recently, a recommender system (RS) has been proposed to optimize the Contacts’ management, by computing the similarity of each Contact with respect to the Donors. In this contribution, we enhance and complete this model by considering both a large DB and two significant extensions of the model, obtaining in this way an effective and whole fuzzy RS. With respect to the DB, the availability of information is effectively exploited. As for the algorithm, a proper similarity measure is defined, based on the specificity of the context. Moreover, a complete estimation of the Contacts’ characteristics is taken into account, by considering not only the frequency but the averaged amount of the gift as well, in the context of a nonparametric approach. The experimental results show the effectiveness of the proposed system.
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
Notes
- 1.
Refer to Russell [26] for an exhaustive introduction to algorithms and aim of Artificial Intelligence.
- 2.
- 3.
The Italian fund-raiser association.
- 4.
Research center on non profit, fund-raising and social responsibility operative in the University of Bologna.
References
Aman, K., Singh, M.D.: A review of data classification using K-nearest neighbor algorithm. Int. J. Emerg. Technol. Adv. Eng. Website 3(6), 354–360 (2013)
Andreoni, J.: Philantropy. In: Kolm, S.C., Ythier, J. (ed.) Handbook the Economics of Giving, Altruism and Reciprocity 2, pp. 1201–1269. Elsevier, Amsterdam (2006)
Barzanti, L., Gaspari, M., Saletti, D.: Modelling decision making in fund raising management by a fuzzy knowledge system. Expert Syst. Appl. 36, 9466–9478 (2009)
Barzanti, L., Giove, S.: A decision support system for fund raising management based on the Choquet integral methodology. Expert Syst. 29(4), 359–373 (2012)
Barzanti, L., Giove, S., Pezzi, A.: A Recommender system for fund raising management (submitted)
Barzanti, L., Mastroleo, M.: An enhanced approach for developing an expert system for fund raising management. In: Segura, J.M., Reiter, A.C. (ed.) Expert System Software: Engineering, Advantages and Applications, pp. 131–156. NYC Nova Science Publishers (2013)
Beg, I., Ashraf, S.: Similarity measures for fuzzy sets. Appl. Comput. Math. 2(8), 192–202 (2009)
Bhatia, N., Vandana, A.: Survey of nearest neighbor techniques. Int. J. Comput. Sci. Inform. Secur. 8(2), 302–305 (2010)
Canestrelli, E., Canestrelli, P., Corazza, M., Filippone, M, Giove, S., Masulli, F.: Local learning of tide level time series using a fuzzy approach. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, 12–17 Aug 2007
Cappellari, L., Ghinetti, P., Turati, G.: On time and money donations. J. Socio-Economics 40(6), 853–867 (2011)
Couso, I., Garrido, L., Sànchez, L.: Similarity and dissimilarity measures between fuzzy sets: a formal relational study. Inf. Sci. 229, 122–141 (2013)
Duffy, J., Ochs, J., Vesterlund, L.: Giving little by little: dynamic voluntary contribution game. J. Public Econ. 91(9), 1708–1730 (2007)
Duncan, B.: Modeling charitable contributions of time and money. J. Public Econ. 72, 213–242 (1999)
Fan, J., Gijbels, I.: Local Polynomial Modelling and Its Applications. Chapmann & Hall, London (1996)
Flory P.: Fundraising databases. DSC London (2001)
Flory P.: Building a fundraising database using your PC. DSC London (2001)
Giove, S., Pellizzari, P.: Time series filtering and reconstruction using fuzzy weighted local regression. In: Soft Computing in Financial Engineering, pp. 73–92. Physica-Verlag, Heidelberg (1999)
Jananch D., Zanker M., Felfernig A., Friedric G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2001)
Keller, J.M., Gray, M.R., Givens Jr., J.A.: A fuzzy K-Nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. Smc-15(4), 580–585 (1985)
Kercheville, J., Kercheville, J.: The effective use of technology in nonprofits. In Tempel, E. (ed.) Hank Rosso’s Achieving Excellence in Fund Raising, pp. 366–379. Wiley, New York (2003)
Lee, L., Piliavin, J.A., Call, V.R.: Giving time, blood and money: similarities and differences. Soc. Psychol. Quart. 62(3), 276–290 (1999)
Melandri, V.: Fundraising. Civil Sector Press, Toronto (2017)
Moro, S., Cortez, P., Rita, P.: A divide-and conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing. Expert Syst. 1–13 (2017). https://doi.org/10.1111/exsy.12253
Nudd, S.P.: Thinking strategically about information. In Tempel, E. (ed.) Hank Rosso’s Achieving Excellence in Fund Raising, pp. 349–365. Wiley, New York (2003)
Rosso, H., Tempel, R., Melandri, V.: The Fund Raising Book. ETAS, Bologna (2004) (in Italian)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, New York (2003)
Sargeant, A.: Using Donor Lifetime Value to Inform Fundraising Strategy. Nonprofit Manage. Leadersh. 12(1), 25–38 (2001)
Verhaert, G.A., Van den Poel, D.: The role of seed money and threshold size in optimizing fundraising campaigns: Past behavior matters! Expert Syst. Appl. 39, 13075–13084 (2012)
Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapmann & Hall, London (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Barzanti, L., Giove, S., Pezzi, A. (2020). An Effective Fuzzy Recommender System for Fund-raising Management. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-8950-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8949-8
Online ISBN: 978-981-13-8950-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)