Exchanging user information between multiple sources can potentially bring many benefits that enhance the user experience in software applications. Richer and more dynamic user models can be constructed allowing more ...
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This paper demonstrates that a user of multilingual search has different interests depending on the language used, and that the user model should reflect this. To demonstrate this phenomenon, the paper proposes and ev...
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This article describes the narrative approach to personalisation. This novel approach to the generation of personalised adaptive hypermedia experiences employs runtime reconciliation between a personalisation strategy...
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When designing search user interfaces (SUIs), there is a need to target specific user groups. The cognitive abilities, fine motor skills, emotional maturity and knowledge of a sixty years old man, a fourteen years old...
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Search user interfaces (SUIs) are usually designed and optimized for generic users or for a certain user group. Users within the group are similar, e.g. concerning their information need, search goals or cognitive ski...
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Persistent identification is necessary for recognition, dissemination and (external) cross-references to digital objects. Uniform Re-source Identifiers (URIs) provide an established scheme for this task, but do not gu...
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Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a ...
Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.
Similarity plays an important role in many multimedia retrieval applications. However, it often has many facets and its perception is highly subjective - very much depending on a person's background or retrieval g...
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Feature selection is a powerful tool of dimension reduction from datasets. In the last decade, more and more researchers have paid attentions on feature selection. Further, some researchers begin to focus on feature s...
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