Discovering user communities on the Internet using unsupervised machine learning techniques
Abstract
Interest in the analysis of user behaviour on the Internet has been increasing rapidly, especially since the advent of electronic commerce. In this context, we argue here for the usefulness of constructing communities of users with common behaviour, making use of machine learning techniques. In particular, we assume that the users of any service on the Internet constitute a large community and we aim to construct smaller communities of users with common characteristics. The paper presents the results of three case studies for three different types of Internet service: a digital library, an information broker and a Web site. Particular attention is paid on the different types of information access involved in the three case studies: query-based information retrieval, profile-based information filtering and Web-site navigation. Each type of access imposes different constraints on the representation of the learning task. Two different unsupervised learning methods are evaluated: conceptual clustering and cluster mining. One of our main concerns is the construction of meaningful communities that can be used for improving information access on the Internet. Analysis of the results in the three case studies brings to surface some of the important properties of the task, suggesting the feasibility of a common methodology for the three different types of information access on the Internet.
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Unsupervised learning determines what usage profiles appear from the log data and if this data can be matched to a certain group of participants.
“Unsupervised learning determines what usage profiles appear from the log data and if this data can be matched to a certain group of participants [33,35].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [35]
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Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data
Sieverink, F., Kelders, S., Poel, M., & van Gemert-Pijnen, L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6452 · Cited by 88 · see citing works
- Authors
- Paliouras, G., Papatheodorou, C., Karkaletsis, V, & Spyropoulos, C. D.
- Affiliations
- Ionian University (GR)
- National Centre of Scientific Research "Demokritos" (GR)
- Venue
- Interacting with Computers
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-12
- Volume
- 14
- Issue
- 6
- Pages
- 761-791
- ISSN
- 0953-5438 (ISSN-L)
- OpenAlex
- W2080578035
- Cited by
- 38
- References
- 43
- Citations by year
- 2025: 1 · 2022: 4 · 2021: 4 · 2020: 1 · 2018: 1 · 2017: 3 · 2016: 3 · 2015: 1 · 2013: 1
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interacting with Computers (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- user communities, user behavior analysis, unsupervised learning, conceptual clustering, information filtering, digital libraries, information brokers, web navigation, e-commerce, access to information
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