Change Detection in Individual Users’ Behavior
| Autor: | Guénaël Cabanes, Parisa Rastin, Jean-Marc Marty, Basarab Matei |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Analyse, Géométrie et Applications (LAGA), Université Paris 8 Vincennes-Saint-Denis (UP8)-Centre National de la Recherche Scientifique (CNRS)-Institut Galilée-Université Paris 13 (UP13) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
020204 information systems 0202 electrical engineering electronic engineering information engineering Profiling (information science) 020201 artificial intelligence & image processing 02 engineering and technology Data mining computer.software_genre computer Change detection ComputingMilieux_MISCELLANEOUS [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | Springer International Publishing Springer International Publishing, pp.501-510, 2018, ⟨10.1007/978-3-030-01421-6_48⟩ Artificial Neural Networks and Machine Learning – ICANN 2018 ISBN: 9783030014209 ICANN (2) |
| Popis: | The analysis of a dynamic data is challenging. Indeed, the structure of such data changes over time, potentially in a very fast speed. In addition, the objects in such data-sets are often complex. In this paper, our practical motivation is to perform users profiling, i.e. to follow users’ geographic location and navigation logs to detect changes in their habits and interests. We propose a new framework in which we first create, for each user, a signal of the evolution in the distribution of their interest and another signal based on the distribution of physical locations recorded during their navigation. Then, we detect automatically the changes in interest or locations thanks a new jump-detection algorithm. We compared the proposed approach with a set of existing signal-based algorithms on a set of artificial data-sets and we showed that our approach is faster and produce less errors for this kind of task. We then applied the proposed framework on a real data-set and we detected different categories of behavior among the users, from users with very stable interest and locations to users with clear changes in their behaviors, either in interest, location or both. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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