Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Visual analytics approach for search result exploration

Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE, 2015

Konferenz
uRankis a Web-based tool combining lightweight text analyticsand visual methods for topic-wise exploration of document sets.It includes a view summarizing the content of the document setin meaningful terms, a dynamic document ranking view and a de-tailed view for further inspection of individual documents. Its ma-jor strength lies in how it supports users in reorganizing documentson-the-fly as their information interests change. We present a pre-liminary evaluation showing that uRank helps to reduce cognitiveload compared to a traditional list-based representation.
2015

Tatzgern Markus, Grasset Raphael, Veas Eduardo Enrique, Schmalstieg Dieter

Exploring real world points of interest: Design and evaluation of object-centric exploration techniques for augmented reality

Pervasive and Mobile Computing, Elsevier, 2015

Journal
Augmented reality (AR) enables users to retrieve additional information about real world objects and locations. Exploring such location-based information in AR requires physical movement to different viewpoints, which may be tiring and even infeasible when viewpoints are out of reach. In this paper, we present object-centric exploration techniques for handheld AR that allow users to access information freely using a virtual copy metaphor. We focus on the design of techniques that allow the exploration of large real world objects. We evaluated our interfaces in a series of studies in controlled conditions and compared them to a 3D map interface, which is a more common method for accessing location-based information. Based on our findings, we put forward design recommendations that should be considered by future generations of location-based AR browsers, 3D tourist guides or situated urban planning.
2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Exploring Document Recommendations through an Interactive User-Driven Approach

RecSys Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'15), CEUR-WS, 2015

Konferenz
Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
2015

Veas Eduardo Enrique, di Sciascio Maria Cecilia

Interactive Preference Elicitation for Scientific and Cultural Recommendations

IJCAI 2015 Workshop on INTELLIGENT PERSONALIZATION (IP'2015), CEUR-WS, 2015

Konferenz
This paper presents a visual interface developed on the basis of control and transparency to elicit preferences in the scientific and cultural domain. Preference elicitation is a recognized challenge in user modeling for personalized recommender systems. The amount of feedback the user is willing to provide depends on how trustworthy the system seems to be and how invasive the elicitation process is. Our approach ranks a collection of items with a controllable text analytics model. It integrates control with the ranking and uses it as implicit preference for content based recommendations.
2015

Veas Eduardo Enrique, di Sciascio Maria Cecilia

Interactive topic analysis with visual analytics and recommender systems.

IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015), 2015

Konferenz
The ability to analyze and organize large collections,to draw relations between pieces of evidence, to buildknowledge, are all part of an information discovery process.This paper describes an approach to interactivetopic analysis, as an information discovery conversationwith a recommender system. We describe a modelthat motivates our approach, and an evaluation comparinginteractive topic analysis with state-of-the-art topicanalysis methods.
2015

Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline

Reading through Graphics: Interactive Landscapes to Explore Dynamic Topic Spaces

Proceedings Part I of the 17th HCI International Conference, HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, 2015

Konferenz
An information landscape is commonly used to represent relatedness in large, high-dimensional datasets, such as text document collections. In this paper we present interactive metaphors, inspired in map reading and visual transitions, that enhance the landscape representation for the analysis of topical changes in dynamic text repositories. The goal of interactive visualizations is to elicit insight, to allow users to visually formulate hypotheses about the underlying data and to prove them. We present a user study that investigates how users can elicit information about topics in a large document set. Our study concentrated on building and testing hypotheses using the map reading metaphors. The results show that people indeed relate topics in the document set from spatial relationships shown in the landscape, and capture the changes to topics aided by map reading metaphors.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

Towards a Recommender Engine for Personalized Visualizations

UMAP, 2015

Konferenz
isualizations have a distinctive advantage when dealing with the information overload problem: being grounded in basic visual cognition, many people understand visualizations. However, when it comes to creating them, it requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad as these methods hardly account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that is able suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

VizRec: A Two-Stage Recommender System for Personalized Visualizations

ACM IUI, ACM, Atlanta, Georgia, USA, 2015

Konferenz
Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer welldefined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach – VizRec– combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
2015

Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran

Visual Recommendations for Scientific and Cultural Content

IVAPP 2015, Berlin, 2015

Konferenz
Supporting individuals who lack experience or competence to evaluate an overwhelming amout of informationsuch as from cultural, scientific and educational content makes recommender system invaluable to cope withthe information overload problem. However, even recommended information scales up and users still needto consider large number of items. Visualization takes a foreground role, letting the user explore possiblyinteresting results. It leverages the high bandwidth of the human visual system to convey massive amounts ofinformation. This paper argues the need to automate the creation of visualizations for unstructured data adaptingit to the user’s preferences. We describe a prototype solution, taking a radical approach considering bothgrounded visual perception guidelines and personalized recommendations to suggest the proper visualization.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph

VizRec: Recommending Personalized Visualizations

ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Human Interaction with Artificial Advice Givers, ACM, 2015

Journal
Visualizations have a distinctive advantage when dealing with the information overload problem: since theyare grounded in basic visual cognition, many people understand them. However, creating the appropriaterepresentation requires specific expertise of the domain and underlying data. Our quest in this paper is tostudy methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization hasto follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a visu-alization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspectsof the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Canwe capture and use those aspects to recommend visualizations? This paper investigates strategies to recom-mend visualizations considering different aspects of user preferences. A multi-dimensional scale is used toestimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing chartsare used to recommend potentially interesting charts based on content. Finally, a hybrid approach combinesinformation on what a chart is about (tags) and how good it is (ratings). We present the design principlesbehindVizRec, our visual recommender. We describe its architecture, the data acquisition approach with acrowd sourced study, and the analysis of strategies for visualization recommendation
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