Abstract
Recommender systems are recently developed computer-assisted tools that support social and informational needs of various communities and help users exploit huge amounts of data for making optimal decisions. In this study, we present a new recommender system for assessment and risk prediction in child welfare institutions in Israel. The system exploits a large diachronic repository of manually completed questionnaires on functioning of welfare institutions and proposes two different rule-based computational models. The system accepts users’ requests via a simple graphical interface, calculates the institutions’ profiles according to user preferences, and presents assessment scores, trends and comparative analyses of the corresponding data using assorted visual aids. Based on the analysis, the system offers three different strategies for objective assessment of the institutions’ functioning and risks. Qualitative and quantitative evaluation of the system’s effectiveness and accuracy demonstrates that it substantially improves the assessment process of a welfare institution. Moreover, it provides an effective tool for objective large-scale analysis of the institution’s overall state and trends, which were previously based primarily on the institution supervisors’ subjective judgment and intuition. In addition, the proposed recommender system has great practical and social impact as it may help identify and avert potential problems, malfunctions, flaws, risks and even tragic incidents in child welfare institutions, as well as increase their overall functioning levels. As a result, as a long-term social implication, the system may also help reduce inequality and social gaps in the Israeli society.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Al Ahmar MA (2011) A prototype student advising expert system supported with an object-oriented database. Int J Adv Comput Sci Appl 1(3):100–105
Araszkiewicz M, Łopatkiewicz A, Zienkiewicz A, Zurek T (2015) Representation of an actual divorce dispute in the parenting plan support system. In: Proceedings of the 15th international conference on artificial intelligence and law (ICAIL’15). ACM, San Diego, CA, USA, pp 166–170
Bobadilla J, Ortega F, Hernando A, Gutierezz A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132
Costello PJ (2003) Action research. AC Black, London
Dänciulescu AI (2014) Risk management an important tool in ICT SME’s in Romania. J Inf Syst Oper Manag. ISSN: 1843-4711
Elgembri T, Altamimi F (2011) Applications of Probability to Risk Management Analysis. Prob Stat Sci Eng Team Proj
Fazeli S, Drachsler H, Brouns F, Sloep P (2012) A trust-based social recommender for teachers. In: Proceedings of the 2nd workshop on recommender systems for technology enhanced learning, pp 49–60
Goncalves JJ, Rocha AM (2012) A decision support system for quality of life in head and neck oncology patients. Head Neck Oncol 4:3
Groh G, Birnkammerer S, Köllhofer V (2012) Social recommender systems. Recommender systems for the social web. Springer, Berlin, pp 3–42
Guy I (2015) Social recommender systems. Recommender systems handbook. Springer, New York, pp 511–543
Hosseinzadehdastak F, Underdown R (2012) Knowledge management as a tool to mitigate weaknesses of risk management. In: Proceedings of the 2012 industrial and systems engineering research conference. Lamar University, Beaumont, Texas, USA
Jafari M, Rezaeenour J, Mazdeh MM, Hooshmandi A (2011) Development and evaluation of a knowledge risk management model for project-based organizations: a multi-stage study. Manag Decis 49(3):309–329
Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, New York
Jianming H, Chu WW (2010) A social network-based recommender system (SNRS). Springer, New York
King I, Lyu MR, Ma H (2010) Introduction to social recommendation. In: Proceedings of WWW. ACM, pp 1355–1356
Laveti RN, Ch J, Pal SN, Babu NSC (2016) A hybrid recommender system using weighted ensemble similarity metrics and digital filters. In: High performance computing workshops (HiPCW), 2016 IEEE 23rd international conference on IEEE, pp 32–38
Liebowitz J (1998) Expert systems: an integral part of knowledge management. Kybernetes 27(2):170–175
Livia C, Ignacio JA, Juan FDP, Alvaro EG, Javier B, Gabriel V, Juan MC (2015) Retreatment predictions in odontology by means of CBR systems. Comput Intell Neurosci 2016:39
Madavi TJ, Kale MB, Bhole NP, Umate RM (2012) Intelligent quality management expert system using PA-AKD in large databases. J Data Min Knowl Discov 3(2):74
Manley M, Kim YS (2012) Modeling emergency evacuation of individuals with disabilities (exitus): an agent-based public decision support system. Expert Syst Appl 39(9):8300–8311. doi:10.1016/j.eswa.2012.01.169
Murayama H, Fujiwara Y, Kawachi I (2012) Social capital and health: a review of prospective multilevel studies. J Epidemiol 22:179–187
Neef D (2005) Managing corporate risk through better knowledge management. Learn Organ 12(2):112–124
Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58
Torre I, Torsani S (2016) A recommender system as a support and training tool. In: Signal-image technology & internet-based systems (SITIS), 2016 12th international conference on IEEE, pp 773–780
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhitomirsky-Geffet, M., Zadok, A. Risk analysis and prediction in welfare institutions using a recommender system. AI & Soc 33, 511–525 (2018). https://doi.org/10.1007/s00146-017-0735-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-017-0735-2