Website visitor segmentation is crucial for effective web presence management and online marketing. We explore methods for grouping website visitors based on user behaviour and incorporating their interests, using the GPT3 language model to analyze the text content of viewed pages content and build user profiles. In our method, the language model GPT3 is used to summarize the content a user visited to build a meaningful user profile, and to answer queries concerning the interests of the users. We then segment the users via text based topic modelling. Our findings indicate that our method of classifying user interests through text analysis and direct language model queries offers high transparency and versatility, surpassing traditional segmentation techniques. Users are characterized in clear profiles, and queries can be tailored to specific interests.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 3
EditorsKohei Arai
Place of PublicationCham
Number of pages15
ISBN (Print)9783031477140
Publication statusPublished - Jan 2024

Publication series

NameLecture Notes in Networks and Systems
Volume824 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


  • Language models
  • Online marketing
  • Segmentation
  • User behaviour


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