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AI & SOCIETY

, Volume 33, Issue 4, pp 511–525 | Cite as

Risk analysis and prediction in welfare institutions using a recommender system

  • Maayan Zhitomirsky-GeffetEmail author
  • Avital Zadok
Original Article
  • 164 Downloads

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.

Keywords

Recommender system Inference rules Welfare institution evaluation Risk prediction 

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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Information Science DepartmentBar-Ilan UniversityRamat GanIsrael

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