Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out–of-context preferences are discarded. Our approach is based on an ontologydriven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context.

How can we solve the problem of information overload in news syndication? This poster outlines the path from keywordbased body text matching to distance-measurable taxonomic tag matching, on to context scale and practical uses. Categories and Subject Descriptors

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in such a way that out of context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context. 1.

Finding relevant interesting items when searching or browsing within a large multi-modal personal lifelog archive is a significant challenge. The use of contextual cues to filter the collection and aid in the determination of relevant content is often suggested as means to address such challenges. This work presents an exploration of the various locations, garnered through context logging, several participants engaged in during personal information access over a 15 month period. We investigate the implications of the varying data accessed across multiple locations for context-based retrieval from such collections. Our analysis highlights that a large number of spaces and places may be used for information access, but high volume of content is accessed in few. 1.

Definition: Multimedia personalization is tailoring digital audiovisual content to its users, based on personal details or characteristics they provide. It allows a content provider to adapt specific multimedia content in accordance with individual standards, tastes and preferences. For example, movies can be personalized based on genre, cast, director, etc.

Abstract Traditional information retrieval systems aim at satisfying most users for most of their searches, leaving aside the context in which the search takes place. We propose to model two main aspects of context: The themes of the user’s information need and the specific data the user is looking for to achieve the task that has motivated his search. Both aspects are modeled by means of ontologies. Documents are semantically indexed according to the context representation and the user accesses information by browsing the ontologies. The model has been applied to a case study that has shown the added value of such a semantic representation of context. Keywords interface

and other research outputs An aspect query language model based on query decomposition and high-order contextual term associations.