Recommendation system model based on technical events
Abstract
Recommendation systems are widely used in the commercial field. The algorithms and architectures of recommendation systems are similar in various fields of application and have proven their effectiveness. Recommendations are based on the user’s profile, the manner of his behavior on various IT (Information Technology) resources, as well as on similar users. At the same time, the use of recommendation systems in specialized areas is not widespread. Technology divisions are a promising new area of application for recommendation systems, and IT experts themselves will be the users. The purpose of this article is to consider a combination of a recommendation system, machine learning (ML) and LLM (Large Language Model) and to design these tools in a single system. Data volumes are currently measured in petabytes (1015 bytes) and exabytes (1018 bytes). In order to process even technical information (metadata/technodata) from the surrounding IT landscape, from the IT systems used by experts, AI (Artificial Intelligence) agents are needed. This article provides a literature review regarding the use of recommendation systems in combination with LLM applications, and suggests an application architecture model that generates human-readable news from technical event logs. The system is designed for a group of users who work with big data (ML engineers, data analysts, and data researchers). It is a combination of recommendation system technologies, LLM, and machine learning models. The article also provides the first results of the research that was carried out.
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References
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