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Intelligent Recommender System for Spatial Analysis

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Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22) (IITI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 566))

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Abstract

One of the main applications of recommender systems is the search and decision making in spatial situations. There is an extensive range of applied problems when events, phenomena and processes of the real world seriously affect decisions, and this requires a rather complex spatial analysis. The complexity arises from the uncertainty of the problem statement and the huge variety of ambiguously estimated solutions. An analyst user when trying to solve a problem, apply geoinformation service in order to extract the most useful information. The implementation of such a mechanism in a mobile device is problematic due to the need to process large amounts of cartographic information. This requires large computing and communication resources. In addition, costs are rising for cartographic visualization, which is essential in spatial analysis. The inclusion of intelligent recommender systems in the spatial analysis circuit will significantly reduce the severity of these problems. This paper discusses the principles of organizing an intelligent recommender system, which is used in the process of finding solutions for hard-to-formalize problems that require spatial analysis. A mobile intelligent recommender system for spatial analysis model is proposed. The peculiarity of the model lies in the implementation of the contextual dependence of recommendations on the dynamics of the process of spatial analysis. Level, trend and rhythm indicators have been introduced for context chains. Using these indicators, it is possible to achieve the semantic integrity of the recommendations. The concept of knowledge representation for the analysis workspace and the search for an adequate context is proposed. The features of logical inference using indefinitely described spatial situations are studied.

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Acknowledgments

The research was funded by the Russian Science Foundation project No. 22-71-10121, https://rscf.ru/en/project/22-71-10121/ implemented by the Southern Federal University.

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Authors and Affiliations

  1. Southern Federal University, Nekrasovskiy Street, 44, 347928, Taganrog, Russia

    Stanislav Belyakov, Alexander Bozhenyuk & Margarita Knyazeva

  2. Public Corporation Research and Development Institute of Railway Engineers, 27/1, Nizhegorodskaya Street, 109029, Moscow, Russia

    Igor Dolgiy

  3. Rostov State Transport University, Rostovskogo Strelkovogo Polka Narodnogo Opolcheniya Sq., 2, 344038, Rostov-on-Don, Russia

    Igor Dolgiy

Authors
  1. Stanislav Belyakov
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  2. Alexander Bozhenyuk
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  3. Igor Dolgiy
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  4. Margarita Knyazeva
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Corresponding author

Correspondence to Alexander Bozhenyuk .

Editor information

Editors and Affiliations

  1. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

  3. Southern University (IMBL), Rostov-on-Don, Russia

    Imran Akperov

  4. Istinye University, Sarıyer, Istanbul, Turkey

    Sebnem Ozdemir

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Belyakov, S., Bozhenyuk, A., Dolgiy, I., Knyazeva, M. (2023). Intelligent Recommender System for Spatial Analysis. In: Kovalev, S., Sukhanov, A., Akperov, I., Ozdemir, S. (eds) Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). IITI 2022. Lecture Notes in Networks and Systems, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-031-19620-1_9

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