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...

Studies on the learning service selection approaches have been the subject of many researches in recent years. The works identified in the literature are developed to solve the learning service selection problem; however, these works need to have a clear view and an overview of how systems intended select services. This study details a multidimensional descriptive view of learning service selec...

Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user’s current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performi...

As user-centric innovation has recently emerged as a successful way of developing new products, services, and concepts, it is worth considering the perspectives of potential technology users during R&D project selection processes. Nevertheless, little effort has been made to reflect customer-perceived value in establishing selection criteria, with the focus mainly on technological potential ins...

Overconfident subjects were given immediate feedback of results in a general knowledge test in an attempt to de-bias them. In a 2 x 3 x 4 mixed factorial design (Feedback x Question Difficulty x Trial Blocks), the accuracy, confidence, and overconfidence of judgements of 150 subjects (48 male and 102 female) were measured. Hard questions produced significantly higher levels of overconfidence th...