Algorithmic modeling of public recommender systems: insights from selected cities

Abstract

Purpose In the context of digital public governance of the 21st century, recommender systems serve as a digital tool to support decision-making and shift toward proactive public services delivery. This paper aims to synthesize an algorithm for public recommender systems deployment coherent with the digital transformation of public services in three Russian regions: the City of Moscow, Moscow region and Astrakhan region. Design/methodology/approach The studied regions serve as an adequate representation of the country’s population coverage carrying, at the same time, diversity of public governance structures in qualitative and quantitative terms. Thus, the authors were able to retrieve both commonalities and particularities in locally applied policies to create an algorithm model for governance high-tech decision support systems (DSS) deployment in management terms. Therefore, the authors use structural and functional analysis to derive the matters for further induction into our algorithmic model. Findings The proposed algorithmic model is developed under the framework of automated verification of current public service delivery mechanisms. The practical application of recommendation systems as a special case of DSS is shown in the example of public service delivery. It is assumed that following the developed algorithm leads to the “digital maturity” of a particular sector of public governance. Originality/value The paper holds a novel look at public services digital transformation through the application of recommender systems, which is evidenced by the algorithmic model approbation on the theoretical level.