Conceptual information model and methods for developing a personalised information service for researchers

Modern scientific activity takes place under conditions of growing information volumes and fragmented digital infrastructures. Researchers are compelled to rely on multiple independent platforms – such as search engines, bibliographic managers, analytical tools, and academic communication services – which complicates workflow organisation, causes redundancy, and reduces research efficiency. The purpose of this study was to develop a conceptual model of a personalised information service for researchers, capable of integrating search, analytical, bibliographic, and communication functions within a unified adaptive environment. The methodology involved information modelling, integration of open scientific data via application programming interfaces, and the formalisation of a user profile for the automated selection of relevant services. The resulting model was formalised as an informational quintuple F = (P, S, D, I, U), where P represents the user profile module, S – the service selection module, D – the data integration module, I – the interface subsystem, and U – the analytics and adaptation subsystem. The user profile P was defined as a vector structure comprising theme-specific attributes, tool preferences, and languages. For each external service S_i, relevance R (S_i, P) was computed using a normalisation function fₙₒᵣₘ and a similarity metric Sim, enabling the construction of a personalised service configuration. A prototype implemented using Flutter and Firebase demonstrated the model’s practical capacity to reduce the time required to locate relevant information and to enhance overall research productivity. The proposed model can serve as a foundation for adaptive digital platforms that promote open science and foster interdisciplinary collaboration