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In this paper, we present an approach to weight the influence of context regarding user interests in content-based recommendations. The balance between interest-content matching and situation matching is formalized using a Fuzzy model, allowing more intuitive personalization. Using content consumption history, this model can be optimized from an initial general model to a new one fitting better the...
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution?...
In existing mobile content service systems, the study is quite rare on automatic situation-service rule construction. Hence, a method is proposed that the semantic association rules between situations and preferences are built by quantitative frequent marked lattice. Different recommendation rules can be extracted along multi-dimensional context routes from this lattice structure. It is propitious...
Users in a campus need information about relevant individuals, buildings, events and available resources. In this paper, we propose a system to perform situation-aware adaptive recommendation of information to assist mobile users in a campus environment. The idea is to show information about the most relevant buildings and particular individuals situated nearby the user, taking into account the user...
We propose an ontology-based approach for inferences linking trust information in two different situations. That reasoning process can augment the typically sparse trust information, by inferring the missing information from other situational conditions, and can better support situation-aware trust management. Our work is more comprehensive in comparison with other models and considers various aspects...
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