01720nas a2200217 4500008004100000245009100041210007100132260000900203300001200212490000700224520107400231653000801305653000801313653001701321100002201338700002101360700001801381700001801399700002001417856006501437 2016 eng d00aMap‑ or List‑based Recommender Agents? Does the Map Metaphor Fulfill its Promise?0 aMap or List‑based Recommender Agents Does the Map Metaphor Fulfi c2016 a291-3080 v163 aWe present a spatialization of digital library content based on item similarity and an experiment which compares the performance of this spatialization relative to a simple list-based display. Items in the library are K-12 science and engineering learning resources. Spatialization and visualization are accomplished through 2D interactive Sammon mapping of pairwise item similarity scores based on the joint occurrence of word bigrams. The 65 science teachers participating in the experiment were asked to search the library for curricular items they would consider using in conducting one or more teaching assignments. Results indicate that whereas the spatializations adequately capture the salient features of the library’s content and teachers actively use them, item retrieval rates, task-completion time and perceived utility do not significantly differ from the semantically poorer but easier to comprehend and navigate list-based representations. These results put into question the usefulness of the rapidly increasing supply of information spatializations.10aBIS10aMBA10aSupply Chain1 aReitsma, Reindert1 aHsieh, Ping-Hung1 aDiekema, Anne1 aRobson, Robby1 aZarsky, Malinda uhttp://journals.sagepub.com/doi/abs/10.1177/147387161666919301481nas a2200169 4500008004100000245007300041210006900114260000900183520091500192653000801107653000801115653001701123100002201140700002101162700001801183856011001201 2015 eng d00aEstimation and Visualization of Digital Library Content Similarities0 aEstimation and Visualization of Digital Library Content Similari c20153 aWe report on a process for similarity estimation and two-dimensional mapping of lesson materials stored in a Web-based K12 Science, Technology, Engineering and Mathematics (STEM) digital library. The process starts with automated removal of all information which should not be included in the similarity estimations followed by automated indexing. Similarity estimation itself is conducted through a natural language processing algorithm which heavily relies on bigrams. The resulting similarities are then used to compute a Sammon-map; i.e., a projection in n dimensions, the item-to-item distances of which best reflect the input similarities. In this paper we concentrate on specification and validation of this process. The similarity results show almost 100% precision-by-rank in the top three to five ranks. Sammon mapping in two dimensions corresponds well with the digital library‘s table of content.10aBIS10aMBA10aSupply Chain1 aReitsma, Reindert1 aHsieh, Ping-Hung1 aRobson, Robby uhttps://business.oregonstate.edu/biblio/estimation-and-visualization-digital-library-content-similarities
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