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ThinkMind // International Journal On Advances in Software, volume 12, numbers 3 and 4, 2019 // View article soft_v12_n34_2019_15


An Integrated Model for Content Management, Topic-oriented User Segmentation, and Behavioral Targeting

Authors:
Hans-Werner Sehring

Keywords: personalization; targeting; segmentation; web tracking; profiling; content management

Abstract:
The World Wide Web is the basis for increasingly many information and interaction services. Personalization provides users with information and services that are adequately tailored to their current needs. Targeting, a form of implicit personalization of content and content presentations for groups of users, comes to broader practical use for a growing number of commercial websites. The wider adoption results from the availability of platforms that incorporate targeting. Solutions are usually built on top of content management systems or eCommerce solutions that are used for the production of websites. The rule sets that typically are required for targeting are related to content, but they are superimposed in the sense that they are not an integral part of the content model or the content itself. This paper presents an initial model that is used to study the integration of content, content visualizations, user classification, and content targeting. Potential benefits from an integrated model are manifold. In the presented approach, personalization is applied by putting content in context rather than through superimposed targeting rules. By expressing personalization rules in the same context-dependent and evolvable way as content, they can also evolve over time and can be adapted to different user contexts. On top of that, they can be defined and maintained by content editors and other users of a content management system. The models used to study this form of integrated targeting do not rely on a certain technology or implementation. The features of the Minimalistic Meta Modeling Language (M3L) are employed as a meta model testbed, though. It allows expressing personalization rules along with both the content they refer to and the concepts they are based on, as well as users’ interest, in the required consistent way.

Pages: 372 to 383

Copyright: Copyright (c) to authors, 2019. Used with permission.

Publication date: December 30, 2019

Published in: journal

ISSN: 1942-2628

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