Ben Steichen

User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities

Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual users needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the users visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.

Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities from Eye Gaze Data

Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual users needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the users visualization task; the users performance (in terms of predicted task completion time); and the users individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.

Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each users specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.

Personalisation in the wild: providing personalisation across semantic, social and open-web resources

One of the key motivating factors for information providers to use personalization is to maximise the benefit to the user in accessing their content. However, traditionally such systems have focussed on mainly corporate or professionally authored content and have not been able to leverage the benefits of other material already on the web, written about that subject by other authors. Such information includes open-web information as well as user-generated content such as forums, blogs, tags, etc. By providing personalized compositions and presentations across these heterogeneous information sources, a potentially richer user experience can be created, leveraging the mutual benefits of professionally authored content as well as open-web information and active user communities. This paper presents novel techniques and architectures that extend the personalization reserved for corporate or professionally developed content with that of user generated content and pages in the wild. Complementary affordances of Personalized Information Retrieval and Adaptive Hypermedia are leveraged in order to provide Adaptive Retrieval and Composition of Heterogeneous INformation sources for personalized hypertext Generation (ARCHING). The approach enables adaptive selection and navigation according to multiple adaptation dimensions and across a variety of heterogeneous data sources. The architectures have been applied in a real-life personalized customer care scenario and a user study evaluation involving authentic information needs has been conducted. The evidence clearly shows that the system successfully blends a users search experience with adaptive selection and navigation techniques and that the user experience is improved in terms of both task assistance and user satisfaction.

Dynamic hypertext generation for reusing open corpus content

Adaptive hypermedia systems traditionally focus on providing personalised learning services for formal or informal learners. The learning material is typically sourced from a proprietary set of closed corpus content. A fundamental problem with this type of architecture is the need for handcrafted learning objects, enriched with considerable amounts of metadata. The challenge of generating adaptive and personalised hypertext presentations from open source content promises a dramatic improvement of the choice of information shown to the learner. This paper proposes an architecture of such a dynamic hypertext generation system and its use in an authentic learning environment. The system is evaluated in terms of educational benefit, as well as the satisfaction of the users testing the system. Concluding from this evaluation, the paper will explore the future work necessary to further enhance the system performance and learning experience.

Te,Te,Hi,Hi: Eye Gaze Sequence Analysis for Informing User-Adaptive Information Visualizations

Information visualization systems have traditionally followed a one-size-fits-all paradigm with respect to their users, i.e., their design is seldom personalized to the specific characteristics of users (e.g. perceptual abilities) or their tasks (e.g. task difficulty). In view of creating information visualization systems that can adapt to each individual user and task, this paper provides an analysis of user eye gaze data aimed at identifying behavioral patterns that are specific to certain user and task groups. In particular, the paper leverages the sequential nature of user eye gaze patterns through differential sequence mining, and successfully identifies a number of pattern differences that could be leveraged by adaptive information visualization systems in order to automatically identify (and consequently adapt to) different user and task characteristics.

Towards Personalized Multilingual Information Access - Exploring the Browsing and Search Behavior of Multilingual Users

The shift from the originally English-language-dominated web towards a truly global world wide web has generated a pressing need to develop novel solutions that address multilingual user diversity. In particular, many web users today are polyglots, i.e. they are proficient in more than one language. However, little is known about the browsing and search habits of such users, and even less about how to best assist their multilingual behaviors through appropriate systems and tools. In order to gain a better understanding, this paper presents a survey of 385 polyglot web users, focusing specifically on the relationship between multiple language proficiency and browsing/search language choice. Results from the survey indicate that polyglot users make significant use of multiple languages during their daily browsing and searching, and that contextual factors such as language proficiency, usage purpose, and topic domain have a significant influence on their language choice and frequency. The paper provides a detailed analysis regarding each of these factors, and offers insights about how to support multilingual users through novel Personalized Multilingual Information Access systems.

Towards multilingual user models for Personalized Multilingual Information Retrieval

The majority of studies in Personalized Information Retrieval (PIR) literature have focused on monolingual IR, and only relatively little work has been done conceming multilingual IR. In this paper we propose a novel method to represent user models in a multilingual fashion. We argue that such representation would be more suitable for Personalized Multilingual Information Retrieval (PMIR). Furthermore, we outline two algorithms for query adaptation based on user information from the multilingual user model.

Supporting the Modern Polyglot: A Comparison of Multilingual Search Interfaces

The unrelenting rise in online user diversification has generated tremendous new challenges for search system providers. Among these, the need to address multiple user language abilities and preferences is paramount. The majority of research on multilingual search has so far focused on improving retrieval and translation techniques in cross-language information retrieval. However, less research has focused on the human-computer interaction aspects of multilingual search, particularly in terms of multilingual result display interfaces. To address this research gap, this paper presents a comparison of 5 different search interface designs for multilingual search. We analyze and evaluate these interfaces through a crowd-based experiment involving 885 participants. Our results show that the common approach of interleaving multilingual results is in fact the least preferred, whereas single-page displays with clear language separation are most preferred. In addition, we show that user proficiency and search content type play an important role in user preferences, and that different interfaces elicit different user behaviors.

1st International Workshop on Multilingual Web Access (MWA 2015)

Over the past 25 years, the World Wide Web has developed into a truly transnational information medium for users from across the globe. As of July 2013, Asia accounts for the largest share of online users in the world at 48%, followed by 22% from the Americas, and 19% from Europe. With this global development, the diversity of user languages on the Web has increased dramatically, leading to new challenges and opportunities for information access providers and consumers. The Multilingual Web Access (MWA 2015) workshop brought together researchers working on Cross-/Multilingual Search & Discovery, the Multilingual Social Web, as well as the Multilingual Semantic Web, in order to promote the exchange of complementary ideas and applicable/transferrable techniques between these areas. The goal of the workshop was to advance the current state of the art in Multilingual Web Access techniques, and, most importantly, to increase the adoption of multilingual techniques, methods, and tools in real-world Web applications.