Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an up-to-date comparison of the most interesting, data-oriented approaches available in the literature.

Common to all actors in today's information world is the problem of lowering the "information noise," both reducing the amount of data to be stored and accessed, and enhancing the "precision" according to which the available data fit the application requirements

Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group's decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.

More and more often, we face the necessity of extracting appropriately reshaped knowledge from an integrated representation of the information space. Be such a global representation a central database, a global view of several ones or an ontological representation of an information domain, we face the need to define personalised views for the knowledge stakeholders: single users, companies or applications. We propose exploiting the information usage context within a methodology for context-aware data design, where the notion of context is formally defined together with its role within the process of view building by information tailoring. This paper presents our context model, called the context dimension tree, which plays a fundamental role in tailoring the information space according to user information needs.