Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data
Analysis of user behavior models based on user session data can be conducted using clustering (or community detecting) algorithms that do not require a predefined number of clusters. An unknown number and quality of potential behavioral models, non-efficient utilization of memory and processing uni...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2024-12-01
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| Series: | Complex Systems Informatics and Modeling Quarterly |
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| Online Access: | https://csimq-journals.rtu.lv/csimq/article/view/291 |
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| _version_ | 1850279893607120896 |
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| author | Vitaly Zabiniako Toms Rožkalns Erika Nazaruka Jurijs Kornienko |
| author_facet | Vitaly Zabiniako Toms Rožkalns Erika Nazaruka Jurijs Kornienko |
| author_sort | Vitaly Zabiniako |
| collection | DOAJ |
| description |
Analysis of user behavior models based on user session data can be conducted using clustering (or community detecting) algorithms that do not require a predefined number of clusters. An unknown number and quality of potential behavioral models, non-efficient utilization of memory and processing units, and a large amount of data are the main difficulties developers could meet. Besides, the suitability of clustering algorithms for the task highly depends on the characteristics of datasets and still requires additional research. The first step and the goal of this research is to analyze the capabilities of the clustering algorithms in order to determine more promising ones and techniques able to minimize computing resources while solving the task. During the research, the properties of algorithms were analyzed through a literature review and also experimentally. It was found that, although the algorithm’s suitability is theoretically proved, experiments could show unsatisfactory results. Therefore, a certain trade-off analysis and problem-specific modifications of original processing must be done. As a result, the clickstream clustering method of the Louvain clustering algorithm and the modified Longest Common Subsequence algorithm showed more appropriate results in detecting the well-clustered user behavior models based on user sessions data. Thus, these algorithms are suitable for the further development of the detecting method.
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| format | Article |
| id | doaj-art-7d65cb8250434c8dbd600a72ac76ae40 |
| institution | OA Journals |
| issn | 2255-9922 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Complex Systems Informatics and Modeling Quarterly |
| spelling | doaj-art-7d65cb8250434c8dbd600a72ac76ae402025-08-20T01:48:57ZengRiga Technical University PressComplex Systems Informatics and Modeling Quarterly2255-99222024-12-014110.7250/csimq.2024-41.04Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions DataVitaly Zabiniako0https://orcid.org/0000-0002-1307-1815Toms Rožkalns1Erika Nazaruka2https://orcid.org/0000-0002-1731-989XJurijs Kornienko3https://orcid.org/0000-0002-2845-9820ABC software Ltd., 51a Tallinas Street, Riga, LV-1012, LatviaABC software Ltd., 51a Tallinas Street, Riga, LV-1012, LatviaABC software Ltd., 51a Tallinas Street, Riga, LV-1012, LatviaABC software Ltd., 51a Tallinas Street, Riga, LV-1012, Latvia Analysis of user behavior models based on user session data can be conducted using clustering (or community detecting) algorithms that do not require a predefined number of clusters. An unknown number and quality of potential behavioral models, non-efficient utilization of memory and processing units, and a large amount of data are the main difficulties developers could meet. Besides, the suitability of clustering algorithms for the task highly depends on the characteristics of datasets and still requires additional research. The first step and the goal of this research is to analyze the capabilities of the clustering algorithms in order to determine more promising ones and techniques able to minimize computing resources while solving the task. During the research, the properties of algorithms were analyzed through a literature review and also experimentally. It was found that, although the algorithm’s suitability is theoretically proved, experiments could show unsatisfactory results. Therefore, a certain trade-off analysis and problem-specific modifications of original processing must be done. As a result, the clickstream clustering method of the Louvain clustering algorithm and the modified Longest Common Subsequence algorithm showed more appropriate results in detecting the well-clustered user behavior models based on user sessions data. Thus, these algorithms are suitable for the further development of the detecting method. https://csimq-journals.rtu.lv/csimq/article/view/291User Behavior ModelSession SimilarityCommunity DetectionParallel AlgorithmsDistributed AlgorithmsScalability |
| spellingShingle | Vitaly Zabiniako Toms Rožkalns Erika Nazaruka Jurijs Kornienko Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data Complex Systems Informatics and Modeling Quarterly User Behavior Model Session Similarity Community Detection Parallel Algorithms Distributed Algorithms Scalability |
| title | Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data |
| title_full | Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data |
| title_fullStr | Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data |
| title_full_unstemmed | Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data |
| title_short | Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data |
| title_sort | analysis of algorithms for detecting users behavioral models based on sessions data |
| topic | User Behavior Model Session Similarity Community Detection Parallel Algorithms Distributed Algorithms Scalability |
| url | https://csimq-journals.rtu.lv/csimq/article/view/291 |
| work_keys_str_mv | AT vitalyzabiniako analysisofalgorithmsfordetectingusersbehavioralmodelsbasedonsessionsdata AT tomsrozkalns analysisofalgorithmsfordetectingusersbehavioralmodelsbasedonsessionsdata AT erikanazaruka analysisofalgorithmsfordetectingusersbehavioralmodelsbasedonsessionsdata AT jurijskornienko analysisofalgorithmsfordetectingusersbehavioralmodelsbasedonsessionsdata |