Multi-application personalization: Data propagation evaluation on a real-life search query log

In the field of multi-application personalization, several techniques have been proposed to support user modeling. None of them have sufficiently investigated the opportunity for a multi-application profile to evolve over time in order to avoid data inconsistency and the subsequent loss of income for website users and companies. In this paper, we propose a model addressing this issue and we focus in particular on user profile data propagation management. Data propagation is a way to reduce the amount of inconsistent user profile information over several applications, even in the case of temporary coalitions of applications as happens in Digital Ecosystems. To evaluate our model, we first extract user profiles using logs of the large real-life AOL search engine. Then, we simulate data propagation along semantically related user information.

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