di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2015
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.
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
2015
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.
Veas Eduardo Enrique, di Sciascio Maria Cecilia
2015
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.
Veas Eduardo Enrique, di Sciascio Maria Cecilia
2015
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.
Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline
2015
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.
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
2015
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.
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
2015
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.
Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran
2015
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.