Navigation in degree of interest trees

Budiu, Raluca; Pirolli, Peter; Fleetwood, Michael D.

doi:10.1145/1133265.1133358

Cited by 12 publications

(18 citation statements)

References 13 publications

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“…Previous modeling of a different task [2] suggests that people continue to scan a group with a probability inversely proportional to the number of items already scanned in the group. Such a strategy may not account for the current results in which people were most likely to abandon searching a group after only one fixation.…”

Section: Future Workmentioning

confidence: 99%

The effects of semantic grouping on visual search

Halverson

Hornof

2008

CHI '08 Extended Abstracts on Human Factors in Computing Systems

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This paper reports on work-in-progress to better understand how users visually interact with hierarchically organized semantic information. Experimental reaction time and eye movement data are reported that give insight into strategies people employ while searching visual layouts containing words that are either grouped by category (i.e. semantically cohesive) or randomly grouped. Additionally, sometimes the category labels of the cohesive groups are displayed as part of the group. Preliminary results suggest that: (a) When groups are cohesive, people tend to search labeled and unlabeled layouts similarly. (b) People seem to trust the categorical information of labels more than non-labels. This work will be used to extend current computational models of visual search to better predict users visual interaction with interfaces.

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Section: Future Workmentioning

confidence: 99%

The effects of semantic grouping on visual search

Halverson

Hornof

2008

CHI '08 Extended Abstracts on Human Factors in Computing Systems

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“…In contrast, prior models like DOI-ACT [5] and SNIF-ACT 2.0 [9] accounted for 56% (ClicksToSuccess) and 94% (%Success) of the variances in their human data, respectively, but fit model parameters to the same human data used to evaluate the models. CTE1.2 is the first process model of information foraging that has been applied to Correlation alone does not tell the entire story, however.…”

Section: Resultsmentioning

confidence: 99%

“…Several avenues of future work may increase its accuracy as a predictive model and its usefulness as a tool for design. 5 For example, CTE1.2 uses only infoscent to evaluate links; it is likely that humans use logical reasoning mechanisms, especially when information foraging fails, as suggested by CTE1.2's under-prediction of success on hard tasks. Using the categorical relationships of words as well as a statistical model of semantic similarity, as in [5], especially when UIs are arranged in groups, may be a path to improvement.…”

Section: Discussionmentioning

confidence: 99%

“…Layout is an important factor, however, in determining a user's success [24]. Budiu and Pirolli [5] take 2-D layout into account, but in the context of a model created for a specific UI (a Degree-of-Interest tree). Likewise, the grouping of links on a page can also influence a user's behavior; AutoCWW [4] takes grouping into account when analyzing infoscent, but does not consider the 2-D layout of the groups when making its predictions.…”

Section: Introductionmentioning

confidence: 99%

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CogTool-Explorer

Teo

John

Blackmon

2012

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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CogTool-Explorer 1.2 (CTE1.2) predicts novice exploration behavior and how it varies with different user-interface (UI) layouts. CTE1.2 improves upon previous models of information foraging by adding a model of hierarchical visual search to guide foraging behavior. Built within CogTool so it is easy to represent UI layouts, run the model, and present results, CTE1.2's vision is to assess many design ideas at the storyboard stage before implementation and without the cost of running human participants. This paper evaluates CTE1.2 predictions against observed human behavior on 108 tasks (36 tasks × 3 distinct website layouts). CTE1.2's predictions accounted for 63-82% of the variance in the percentage of participants succeeding on each task, the number of clicks to success, and the percentage of participants succeeding without error. We demonstrate how these predictions can be used to identify areas of the UI in need of redesign.CTE1.2 is a research extension of CogTool that connects a computational model of eye-movements, visual perception, cognition and motor actions (i.e., a simulated user) with a UI storyboard. The original CogTool [13] had only a simulation of a skilled user (i.e., a Keystroke-Level Model [6]). The first version of CogTool-Explorer [24] added a model of novice behavior that considered 2-D layout to CogTool so a designer can make predictions of novice exploration on the same tasks and UIs that he or she

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“…In [22], SVD is outperformed by a more basic word space model. However, in [19,15,3], SVD combined with small window-based contexts outperform other approaches. In all these experiments, the evaluation uses as gold standard popular tests as TOEFL where the system has to choose the most appropriate synonym for a given word given a restricted list of four candidates.…”

Section: Related Workmentioning

confidence: 99%

Comparing Different Properties Involved in Word Similarity Extraction

Gamallo

2009

Progress in Artificial Intelligence

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Abstract. In this paper, we will analyze the behavior of several parameters, namely type of contexts, similarity measures, and word space models, in the task of word similarity extraction from large corpora. The main objective of the paper will be to describe experiments comparing different extraction systems based on all possible combinations of these parameters. Special attention will be paid to the comparison between syntax-based contexts and windowing techniques, binary similarity metrics and more elaborate coefficients, as well as baseline word space models and Singular Value Decomposition strategies. The evaluation leads us to conclude that the combination of syntax-based contexts, binary similarity metrics, and a baseline word space model makes the extraction much more precise than other combinations with more elaborate metrics and complex models.

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Navigation in degree of interest trees

Cited by 12 publications

References 13 publications

The effects of semantic grouping on visual search

The effects of semantic grouping on visual search

CogTool-Explorer

Comparing Different Properties Involved in Word Similarity Extraction

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