DYNAMIC ADAPTATION OF THE USER PROFILE IN A RECOMMENDATION SYSTEM BASED ON ANALYSIS OF INFORMATION ABOUT HIS BEHAVIOR
DOI:
https://doi.org/10.32689/maup.it.2026.1.2Keywords:
user profile, recommender system, behavioral patterns, short-term interests, long-term interests, dynamic adaptation, streaming data processingAbstract
The rapid growth of digital content volumes and the increasing complexity of user behavioral patterns have intensified the requirements for the accuracy and adaptability of modern recommender systems. Under these conditions, methods for dynamic user profile updating become particularly important, as they enable the maintenance of relevant personalization in the presence of changing interests, contextual influences, and the irregular nature of user interaction with information services. The problem lies in the need to ensure the adaptation of such systems to changes in user interests in a dynamic environment, where short-term preferences change rapidly, and long-term ones retain inertia, which makes it difficult to maintain high personalization accuracy. The aim of the research is to develop a mathematical model and architectural solution for dynamic user profile adaptation, which allow to effectively combine the analysis of long-term and short-term interests taking into account contextual factors to increase the accuracy of personalization in recommender systems. The work is aimed at overcoming such shortcomings of existing systems as high sensitivity to random «noise» in user actions, complexity of interpreting updates and inability to respond in a timely manner to smooth changes in behavioral patterns. The work introduces a mechanism for dynamically managing the balance between short-term (STI) and long-term (LTI) interests using an adaptive coefficient, which allows the system to automatically switch to the current needs of the user in the event of a sharp change in behavior or rely on stable habits during stable interaction. A method for integrating contextual factors into the user profile update process has been developed, which provides the ability to enhance or weaken the significance of events depending on external conditions (time, device, location), transforming the profile into a context-sensitive structure. The technological approach is based on the architecture of pipeline data processing for streaming analysis of events in real time. Experimental validation of the proposed solutions was carried out by testing on the MovieLens dataset with a comparative analysis of the accuracy of recommendations of dynamic models relative to static ones. A characteristic difference of the proposed approach is the integration of contextual parameters into the profile update process, which turns it into a context-sensitive structure capable of adapting to interaction conditions. Technical tests have proven the ability of the recommendation system to work stably in conditions of intensive data flow, ensuring rapid updating of the system’s knowledge about a person without loss of overall performance. Practical application is possible in recommendation systems that operate in real time and work with intensive data flows, in particular in media services, e-commerce and information platforms, provided that there are means of collecting user events and a stream processing infrastructure.
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