Abstract
User profile has contributed to customize user access and adjusts applications to its needs. In this respect, automatically building of user profiles issue is an important research area. Nevertheless, standardizing these profiles in terms of representation and acquisition schemes, more especially in large scale systems like Peer-to-Peer systems (P2P), is a complex task. In this paper, we introduce a distributed user profile modelling approach based on user search topics history without the need of any external knowledge resource (e.g., ontology). This model learns from past interests to guess correlations between user requests, associated topics, relevant documents and nodes (i.e., peers) to enhance any information retrieval process. The solution is based on an extension of Formal Concept Analysis (FCA) theory. We also study, the integration of our model in query routing (i.e., content discovery) and results aggregation processes for P2P systems. Carried out experiments, performed under a P2P simulator environment, showed that our model outperforms its competitors in terms of effectiveness and efficiency.
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