In many settings, individuals are confronted with decision problems that involve information relevant to their self-image. This paper uses an experiment to explore whether the selfrelevance of information influences information processing. The experiment implements two information processing tasks that are identical from a theoretical perspective, but differ in the type of information provided:...

Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of retrieved images as being relevant or not. In this paper, a relevance feedback technique based on the “dissimilarity representation” of images is proposed. Each image is represented by a vector whose components are the similarity values between the image itself and a “representation set” m...

Our goal in this study was to explore the potentials of extracting features from eye-tracking data that have the potential to improve performance in implicit relevance feedback. We view this type of data as an example of the searcher’ immediate context and as containing useful clues of the indications of the interaction between the searcher and the IR system. In particular, we explored if we co...

Systematic reviews require researchers to identify the entire body of relevant literature. Algorithms that filter the list for manual scanning with nearly perfect recall can significantly decrease the workload. This paper presents a novel stopping criterion that estimates the score-distribution of relevant articles from relevance feedback of random articles (S-D Minimal Sampling). Using 20 trai...

Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the inte...

This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three diffe...

We introduce our participation of the TREC Relevance Feedback(RF) TRACK in 2009. The RF09 TRACK is focused on the explicit relevant feedback, where a few relevant and irrelevant documents are available to each query. Our system is implemented under the framework of probabilistic language model. We apply the constrained clustering on the top returned documents and extract the expanded words to r...

Researches on document retrieval, text categorization and routing have shown the effects of learning by sampling relevant documents or non-relevant document from training set. Allan et al. (1995) considered only the top K non-relevant documents, which is the same number of all known relevant documents in the training set to learn a routing query. This is motivated by the need to have a balance ...

End-user feedback is becoming more important for the evolution of software systems. There exist various communication channels for end-users (app stores, social networks) which allow them to express their experiences and requirements regarding a software application. End-users communicate a large amount of feedback via these channels which leads to open issues regarding the use of end-user feed...

this study aimed at investigating the effect of learners’ gender on their preferences for corrective feedback. learners’ prefer- ences which were investigated included the necessity, frequency, timing, type, method, and delivering agent of error treatment. to this end, a questionnaire was administered to a random sample of 100 participants (50 males and 50 females) studying english (efl) at shi...