Kapterev A.I. How Digital Personalized Services Can Be Used in Libraries

An overview of selective dissemination of information (SDI) services and text mining systems is presented. There are many SDI systems in the world, differing in their tasks and scale. Since the mid-1990s, online SDI services based on the application of artificial intelligence (AI) systems have been developing abroad: SDI, Current Awareness Services (CAS), Alerting Services and etc. A comparative analysis of the services according to a number of criteria is given. It is shown that, firstly, modern digital SDI services have wider capabilities and require less effort than those used by libraries in the past, while text mining systems provide analytical data for decision-making. A comparative analysis of most common SDI services is carried out to identify their advantages and disadvantages. A tale of the tape of the most common text mining services is also provided: MonkeyLearn, Thematic, Lexalytics, Chattermill, QDA Miner, Bismart Intelligent Folksonomy and etc. These tools offer different levels of sophistication and functionality. Choosing the right one depends on the specific needs and objectives of the organization or user. The identified criteria for comparison are: accessibility; interface usability; compatibility with other software tools; functionality. The following methods were used in the study: content analysis of documents to characterize the degree of study of the issue and use in the activities of libraries; typological analysis of the used systems according to a number of criteria; comparative analysis to identify the effectiveness of the analyzed services in library and information activities.

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Publication Details

Journal
Bibliotekovedenie [Russian Journal of Library Science]
Published
2025-02-05
DOI
https://doi.org/10.25281/0869-608x-2025-74-1-25-36
Citations
3
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
9.74
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article

Kapterev A.I. How Digital Personalized Services Can Be Used in Libraries

Andrey I. Kapterev
3 citations
Bibliotekovedenie [Russian Journal of Library Science]
Research Data Management Practices
9.74
article

Kapterev A.I. How Digital Personalized Services Can Be Used in Libraries

Andrey I. Kapterev
article en
3 citations

Abstract

An overview of selective dissemination of information (SDI) services and text mining systems is presented. There are many SDI systems in the world, differing in their tasks and scale. Since the mid-1990s, online SDI services based on the application of artificial intelligence (AI) systems have been developing abroad: SDI, Current Awareness Services (CAS), Alerting Services and etc. A comparative analysis of the services according to a number of criteria is given. It is shown that, firstly, modern digital SDI services have wider capabilities and require less effort than those used by libraries in the past, while text mining systems provide analytical data for decision-making. A comparative analysis of most common SDI services is carried out to identify their advantages and disadvantages. A tale of the tape of the most common text mining services is also provided: MonkeyLearn, Thematic, Lexalytics, Chattermill, QDA Miner, Bismart Intelligent Folksonomy and etc. These tools offer different levels of sophistication and functionality. Choosing the right one depends on the specific needs and objectives of the organization or user. The identified criteria for comparison are: accessibility; interface usability; compatibility with other software tools; functionality. The following methods were used in the study: content analysis of documents to characterize the degree of study of the issue and use in the activities of libraries; typological analysis of the used systems according to a number of criteria; comparative analysis to identify the effectiveness of the analyzed services in library and information activities.

Bibliotekovedenie [Russian Journal of Library Science]Vol. 74(1)
Russian State Library (RU), Moscow City University (RU)
Openalex Percentile: Top 3%
Research Data Management Practices
9.74
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