Behavior-driven visualization recommendation

Gotz, David; Wen, Zhen

doi:10.1145/1502650.1502695

Cited by 179 publications

(134 citation statements)

References 18 publications

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“…Existing work on identifying the factors that define visualization effectiveness has mostly focused on properties of the data to be visualized or the tasks to be performed, sometimes obtaining inconclusive and conflicting results (see [14] and [19], for an overview). Traditionally, extensive work has been done comparing the effectiveness of graphical data in terms of accuracy and speed across different chart types (e.g., bar, radar), yet this research typically did not take into account individual differences (see [6] and [20]).…”

Section: Related Workmentioning

confidence: 99%

“…However, these ideas have rarely been applied to data visualization, largely due to the limited understanding of which user characteristics are relevant for adaptivity in this domain. Two notable exceptions are the work by Gotz and Wen [14], and by Brusilovsky et al [4]. Gotz and Wen [14] propose a technique to automatically detect a user's changing goals during interaction with a multipurpose visualization, and adapt the visualization accordingly.…”

Section: Related Workmentioning

confidence: 99%

“…Two notable exceptions are the work by Gotz and Wen [14], and by Brusilovsky et al [4]. Gotz and Wen [14] propose a technique to automatically detect a user's changing goals during interaction with a multipurpose visualization, and adapt the visualization accordingly. In contrast, we focus on adapting the visualizations to other relevant user-dependent factors in addition to goals.…”

Section: Related Workmentioning

confidence: 99%

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Towards Adaptive Information Visualization: On the Influence of User Characteristics

Toker

Conati

Carenini

et al. 2012

Lecture Notes in Computer Science

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Abstract. The long-term goal of our research is to design information visualization systems that adapt to the specific needs, characteristics, and context of each individual viewer. In order to successfully perform such adaptation, it is crucial to first identify characteristics that influence an individual user's effectiveness, efficiency, and satisfaction with a particular information visualization type. In this paper, we present a study that focuses on investigating the impact of four user characteristics (perceptual speed, verbal working memory, visual working memory, and user expertise) on the effectiveness of two common data visualization techniques: bar graphs and radar graphs. Our results show that certain user characteristics do in fact have a significant effect on task efficiency, user preference, and ease of use. We conclude with a discussion of how our findings could be effectively used for an adaptive visualization system. Keywords: User characteristics, User Evaluation, Adaptive Information Visualization. IntroductionInformation visualization is a thriving area of research in the study of human/computer communication. Though the field has made substantial progress in measuring and formalizing visualization effectiveness, results and suggestions from the literature are sometimes inconclusive and conflicting [19]. We believe this may be attributed to the fact that existing visualizations are designed mostly around the target data set and associated task model, with little consideration for user differences. Both long term user characteristics (e.g., cognitive abilities and expertise) and short term factors (e.g., cognitive load and attention) have often been overlooked in the design of information visualizations, despite studies linking individual differences to visualization efficacy for search and navigation tasks [1,8], for information seeking tasks [7,25], as well as anecdotal evidence of diverse personal visualization preferences [3]. Our long term goal is to explore the possibilities of user-centered visualizations, which understand that different users have different visualization needs and abilities, and which can adapt to these differences. However, before adaptation strategies can be effectively specified, we believe that the influence of user characteristics on visua-

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards Adaptive Information Visualization: On the Influence of User Characteristics

Toker

Conati

Carenini

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…More recent work aims to discover patterns in user behaviour in preparation of a dataset in order to predict visualization requirements [9]. Finally, closest to our work is work by Mackinlay from 2007 [10] where a rule based approach is taken to visualization recommendation.…”

Section: Introductionmentioning

confidence: 99%

Visualization for the Masses: Learning from the Experts

Freyne

Smyth

2010

Case-Based Reasoning. Research and Development

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Abstract. Increasingly, in our everyday lives, we rely on our ability to access and understand complex information. Just as the search engine played a key role in helping people access relevant information, there is evidence that the next generation of information tools will provide users with a greater ability to analyse and make sense of large amounts of raw data. Visualization technologies are set to play an important role in this regard. However, the current generation of visualization tools are simply too complex for the typical user. In this paper we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, ManyEyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations to assist novice users in the selection of appropriate visualization types.

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“…The problem, of course, is that the average user is not a visualization expert and producing the right sort of visualization for a given dataset is far from trivial. Previous work in the area of visualization recommendation includes research into articulated task-orientated systems [4], early data property based systems [9,8], hybrid task and data based systems which examine both user intent and the data at hand [11,2] and more recent work which aims to discover patterns in user behaviour in preparation of a dataset in order to predict visualization requirements [6]. This work returns to the early data property based research as we exploit case-based reasoning techniques to make visualization recommendations.…”

Section: Introductionmentioning

confidence: 99%

Creating Visualizations: A Case-Based Reasoning Perspective

Freyne

Smyth

2010

Artificial Intelligence and Cognitive Science

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Abstract. Visualization is among the most powerful of data analysis techniques and is readily available in standalone systems or components of everyday software packages. In recent years much work has been done to design and develop visualization systems with reduced entry and usage barriers in order to make visualization available to the masses. Here we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, Many Eyes and explore how case based representation of datasets including simple features such as size and content types can produce recommendations of visualization types to assist novice users in the selection of appropriate visualizations.

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Behavior-driven visualization recommendation

Cited by 179 publications

References 18 publications

Towards Adaptive Information Visualization: On the Influence of User Characteristics

Towards Adaptive Information Visualization: On the Influence of User Characteristics

Visualization for the Masses: Learning from the Experts

Creating Visualizations: A Case-Based Reasoning Perspective

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