A Natural Language Generation (NLG) system is able to generate text from nonlinguistic data, ideally personalising the content to a user’s specific needs. In some cases, however, there are multiple stakeholders with their own individual goals, needs and preferences. In this paper, we explore the feasibility of combining the preferences of two different user groups, lecturers and students, when ...

OBJECTIVES To explore public opinions about different systems for feeding back views about health services to the National Health Service. DESIGN Questionnaire survey. SETTING NHS Grampian, Scotland, UK. PARTICIPANTS A random sample of 10 000 adults registered with a general practitioner in Grampian was invited to opt in to the study; 2449 were sent questionnaires. OUTCOME MEASURES Opin...

One of the main reasons why content-based image retrieval (CBIR) is such a challenging problem has to do with the fact that there is no canonical way to capture the visual content encapsulated in an image. As a consequence, most CBIR search-engines keep the human in the loop by regularly requesting his feedback to expedite the search-action. More precisely, the feedback is harnessed to estimate...

User modeling can be used in information filtering and retrieval systems to improve the representation of a user’s information needs. User models can be constructed by hand, or learned automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to infer implicit feedback without requiring explicit rele...

In this article we report on a new digital interactive self-report method for the measurement of human affect. The AffectButton (Broekens & Brinkman, 2009) is a button that enables users to provide affective feedback in terms of values on the well-known three affective dimensions of Pleasure (Valence), Arousal and Dominance. The AffectButton is an interface component that functions and looks li...

Measuring the quality of recommendations produced by a recommender system (RS) is challenging. Labels used for evaluation are typically obtained from users of a RS, by asking for explicit feedback, or inferring labels from implicit feedback. Both approaches can introduce significant biases in the evaluation process. We investigate biases that may affect labels inferred from implicit feedback. I...