Howard Greisdorf

Modelling what users see when they look at images: a cognitive viewpoint

Analysis of user viewing and query‐matching behavior furnishes additional evidence that the relevance of retrieved images for system users may arise from descriptions of objects and content‐based elements that are not evident or not even present in the image. This investigation looks at how users assign pre‐determined query terms to retrieved images, as well as looking at a post‐retrieval process of image engagement to user cognitive assessments of meaningful terms. Additionally, affective/emotion‐based query terms appear to be an important descriptive category for image retrieval. A system for capturing (eliciting) human interpretations derived from cognitive engagements with viewed images could further enhance the efficiency of image retrieval systems stemming from traditional indexing methods and technology‐based content extraction algorithms. An approach to such a system is posited.

Relevance thresholds: a multi-stage predictive model of how users evaluate information

This investigation examines end-user judgment and evaluation behavior during information retrieval (IR) system interactions and extends previous research surrounding relevance as a key construct for representing the value end-users ascribe to items retrieved from IR systems. A self-reporting instrument collected evaluative responses from 32 end-users related to 1432 retrieved items in relation to five characteristics of each item: (1) whether it was on topic, (2) whether it was meaningful to the user, (3) whether it was useful in relation to the problem at hand, (4) whether the IR system returned the information in the right form or format, and (5) whether the information retrieved allowed the user to take further action on it. The manner in which these characteristics of the retrieved items were considered, differentiated and aggregated were examined in relation to the region of relevance attributed to those items by the users.The nominal nature of the data collected led to non-parametric statistical analyses that indicated that end-user evaluation of retrieved items to resolve an information problem at hand is most likely a multi-stage process. While end-users may differ in their judgments and evaluations of retrieved items, they appear to make those decisions by using somewhat consistent heuristic approaches that point to a predictive multi-stage model of relevance thresholds that exist on a continuum from topic to meaning (pertinence) to functionality (use).

Median measure: an approach to IR systems evaluation

In this paper results from three studies examining 1295 relevance judgments by 36 information retrieval (IR) system end-users is reported. Both the region of the relevance judgments, from non-relevant to highly relevant, and the motivations or levels for the relevance judgments are examined. Three major findings are studied. First, the frequency distributions of relevance judgments by IR system end-users tend to take on a bi-modal shape with peaks at the extremes (non-relevant/relevant) with a flatter middle range. Second, the different type of scale (interval or ordinal) used in each study did not alter the shape of the relevance frequency distributions. And third, on an interval scale, the median point of relevance judgment distributions correlates with the point where relevant and partially relevant items begin to be retrieved. The median point of a distribution of relevance judgments may provide a measure of user/IR system interaction to supplement precision/recall measures. The implications of investigation for relevance theory and IR systems evaluation are discussed.

What Do Users See? Exploring the Cognitive Nature of Functional Image Retrieval.

Images cannot just be labeled. The vexing issues surrounding functional access to digitized images have come to the fore as a major concern in the realm of information retrieval. Constant overlap as well as a lack of consistent membership among and between images continues to challenge the efficacy of retrieval systems development. Analysis of user viewing and categorization furnishes additional evidence that what viewers see is not necessarily the objects pictured, may not even be present in an image, or represents a wide variety of possible descriptions. This investigation looks at how users view images in relation to how they can be described (categorized) and in what manner they match other images in the same collection. The results provide an indication that what viewers see depends as much on who they are as it does on what they see. While traditional methods of image retrieval have mostly focused on indexing techniques that encompass object descriptions and more recent approaches through contentbased aspects of digitized imagery, it appears that these systems could be improved by enabling subjective engagement with retrieved images to identify additional access points derived from the adumbrative, impressionistic, and many times abstract nature of user cognitive engagements with the images retrieved.

Recent relevance research: implications for information professionals

We discuss results from recent relevance research with implications for information professionals. Our studies show that beyond the usual concern with high relevance and non‐relevance judgements, that partially relevant judgements by users are important. We call for the adoption of a more complex view of human relevance judgements in the education and practice of information professionals.

Nodes of topicality: modeling user notions of on topic documents

Topicality, while demonstrably an empirically manageable variable of investigation, engenders aspects of cognitive complexity that may, or may not, be easily managed during user interactions with IR systems. If an item retrieved from an IR system is considered to be on topic by a user, the meaning of that judgment may imply other underlying criteria. What makes an item on topic for users is the subject of this investigation. Although topicality has served to generate a great deal of attention in the body of information science literature, the meaning of topicality to IR system users has suffered from a lack of full understanding in designing more effective approaches to information search and retrieval. This investigation takes an inductive approach to the deductive extraction of characteristics that describe and explain how items retrieved from interactions with IR systems can be considered as on topic.

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