INTELLIGENT RECOMMENDER SYSTEM FOR SPATIAL ANALYSIS
Abstract
The work is devoted to the analysis of mechanisms of formation of recommendations and
evaluation of the user's reaction to them in the interactive mode of work with the geoinformation
system. One of the important areas of application of recommender systems is the search and decision-
making in spatial situations. A peculiarity of this class of problems is the uncertainty of taskdefinition and ambiguity of decision evaluation. Users are often faced with problems that do not
have a clear formulation. To try to solve them, it is necessary not only to designate the direction of
solution search, but also to find an adequate sequence of tasks with clearly formulated input and
output data. Recommendations in such cases are designed in a dialogue with the user-analyst to
develop a strategy for finding solutions. In this paper we study a smart recommendation system
using the experience of dialog interaction. We propose a model of adaptation to the mental image
of the problem, which builds the user, taking into account the levels of situational awareness and
cognitive load. The peculiarity of the model is the use of visual cartographic objects, which are
indicators of the state of the mental image. A recommendation is represented by a set of objects
that are introduced into the field of cartographic analysis. This implicitly induces a certain semantic
direction of increasing situational awareness. A criterion of satisfaction with the recommendation
is suggested. A diagram of recommender system states, which describes the selection of context,
adequate to the problem being solved, is given. The context is understood as an information
object, capable of providing program functions and data for solving problems of a limited class.
A sequence of contexts in an analysis session is considered as a precedent of experience. Indicators
of trend, tendency and rhythm are proposed for possible chains of contexts. The degree of
semantic proximity of precedents to the current course of search for a solution is estimated by
these indicators. Their use will increase the speed of adaptation.
References
predpochteniyakh mental'nogo vs voploshchennogo rezhimov resheniya myslitel'nykh zadach
[Stable individual differences in preferences of mental vs embodied modes of solving mental
tasks], Kognitivnaya nauka v Moskve: novye issledovaniya: Mater. konferentsii, Moskva,
23–24 iyunya 2021 goda [Cognitive Science in Moscow: New research: Conference Proceedings,
Moscow, June 23-24, 202], ed. by E.V. Pechenkovoy, M.V. Falikman, A.Ya. Koyfman.
Moscow: BukiVedi, Institut prakticheskoy psikhologii i psikhoanaliza, 2021, pp. 255-260.
2. Spiridonov V.F. Psikhologiya myshleniya. Reshenie zadach i problem: ucheb. posobie [Psychology
of thinking. Solving problems and problems: a textbook]. 2nd ed. Moscow: Izd-vo
Yurayt, 2019.
3. Dr Michael J de Smith, Prof Michael F Goodchild, Prof Paul A Longley & Associates. Geospatial
Analysis A Comprehensive Guide to Principles Techniques and Software Tools. – 6th
ed., 2018, UK.
4. Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, Lindi Quackenbush, Sarina Adeli,
Brian Brisco. Google Earth Engine for geo-big data applications: A meta-analysis and systematic
review, ISPRS Journal of Photogrammetry and Remote Sensing, 2020, Vol. 164, pp. 152-
170. Available at: https://doi.org/10.1016/j.isprsjprs.2020.04.001.
5. Haoxuan Wang, Yuna Ni, Ling Sun, Yuanyuan Chen, Ting Xu, Xiaohui Chen, Weihua Su,
Zhiguang Zhou. Hierarchical visualization of geographical areal data with spatial attribute association,
Visual Informatics, 2021, Vol. 5, Issue 3, pp. 82-91. Available at:
https://doi.org/10.1016/j.visinf.2021.09.001.
6. Michael F. Goodchild. Spatial Thinking and the GIS User Interface, Procedia - Social and Behavioral
Sciences, 2011, Vol. 21, pp. 3-9. Available at: https://doi.org/10.1016/j.sbspro.2011.07.002.
7. Muhammad Nazrul Islam, Harry Bouwman. Towards user–intuitive web interface sign design
and evaluation: A semiotic framework, International Journal of Human-Computer Studies,
2016, Vol. 86, pp. 121-137. Available at: https://doi.org/10.1016/j.ijhcs.2015.10.003.
8. Singh, Pradeep & Dutta Pramanik, Pijush & Dey, Avick & Choudhury, Prasenjit. Recommender
Systems: An Overview, Research Trends, and Future Directions, International Journal
of Business and Systems Research, 2021,Vol. 15, pp 14-52.
9. María del Carmen Rodríguez-Hernández, Sergio Ilarri. AI-based mobile context-aware recommender
systems from an information management perspective: Progress and directions, Knowledge-Based Systems,
2021, Vol. 215, 106740. Available at: https://doi.org/10.1016/j.knosys.2021.106740.
10. Shaina Raza, Chen Ding. Progress in context-aware recommender systems – An overview, Computer
Science Review, 2019, Vol. 31, pp. 84-97. Available at: https://doi.org/10.1016/j.cosrev.2019.01.001.
11. Jose Aguilar, Marxjhony Jerez, Taniana Rodríguez. CAMeOnto: Context awareness meta
ontology modeling, Applied Computing and Informatics, 2018, Vol. 14, pp. 202-213.
12. Saurabh Sharma, Harish Kumar Shakya, Venkatadri Marriboyina. A location based novel recommender
framework of user interest through data categorization, Materials Today: Proceedings, 2021,
Vol. 47, Part 19, pp. 7155-7161. Available at: https://doi.org/10.1016/j.matpr.2021.06.325.
13. Myung Jin Choi, Antonio Torralba, Alan S. Willsky. Context models and out-of-context objects,
Pattern Recognition Letters, 2012, Vol. 33, pp. 853-862.
14. Geir Solskinnsbakk, Jon Atle Gulla. Combining ontological profiles with context in information
retrieval, Data & Knowledge Engineering, 2010, Vol. 69, pp. 251-260.
15. Xuan Lv, Nora M. El-Gohary. Enhanced context-based document relevance assessment and
ranking for improved information retrieval to support environmental decision making, Advanced
Engineering Informatics, 2016, Vol. 30, pp. 737-750.
16. Moshe Unger, Ariel Bar, Bracha Shapira, Lior Rokach. Towards latent context-aware recommendation
systems, Knowledge-Based Systems, 2016, Vol. 104, pp. 165-178.
17. Bin Xia, Zhen Ni, Tao Li, Qianmu Li, Qifeng Zhou. VRer: Context-Based Venue Recommendation
using embedded space ranking SVM in location-based social network, Expert Systems
With Applications, 2017, Vol. 83, pp. 18-29.
18. Cioara T., Anghel I., Salomie I., Dinsoreanu M., Copil G., Moldovan D. A self-adapting algorithm
for context aware systems, in: Roedunet International Conference (RoEduNet), 2010 9th,
2010, pp. 374-379.
19. Kwon O., Yoo K., & Suh E. UbiDSS: A proactive intelligent decision support system as an
expert system deploying ubiquitous computing technologies, Expert Systems with Applications,
2005, Vol. 28 (1), pp. 149-161.
20. Quintana-Amate S., Bermell-Garcia P., Tiwari A., & Turner C.J. A new knowledge sourcing
framework for knowledge-based engineering: An aerospace industry case study, Computers &
Industrial Engineering, 2017, Vol. 104, pp. 35-50.
21. Ivens Portugal, Paulo Alencar, Donald Cowan. The use of machine learning algorithms in
recommender systems: A systematic review, Expert Systems with Applications, 2018, Vol. 97,
pp. 205-227. Available at: https://doi.org/10.1016/j.eswa.2017.12.020.
22. Zofie Cimburova, Meta Berghauser Pont. Location matters. A systematic review of spatial
contextual factors mediating ecosystem services of urban trees, Ecosystem Services, 2021, Vol.
50, pp. 101296.
23. Feng Liang, Honglong Chen, Kai Lin, Junjian Li, Zhe Li, Huansheng Xue, Vladimir Shakhov, Hannan
Bin Liaqat. Route recommendation based on temporal–spatial metric, Computers & Electrical Engineering,
2022, Vol. 97, 107549. Available at: https://doi.org/10.1016/j.compeleceng.2021.107549.
24. Yuan Guo, Bing Zhang, Sun Y., Jiang K., Wu K. Machine learning based feature selection and
knowledge reasoning for CBR system under big data, Pattern Recognition, 2021, Vol. 112,
107805. Available at: https://doi.org/10.1016/j.patcog.2020.107805.
25. Hart S.G., Staveland L.E. Development of NASA-TLX (task load index): results of empirical and
theoretical research / Hancock P.A., Meshkati N. (Eds.), Advances in psychology, Human mental
workload, Vol. 52. North-Holland, 1988, pp. 139-183. 10.1016/S0166-4115(08)62386-9.
