Publikationen

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.

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.

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.

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.

Recommender systems are becoming common tools supportingautomatic, context-based retrieval of resources.When the number of retrieved resources grows large visualtools are required that leverage the capacity of humanvision to analyse large amounts of information. Weintroduce a Web-based visual tool for exploring and organisingrecommendations retrieved from multiple sourcesalong dimensions relevant to cultural heritage and educationalcontext. Our tool provides several views supportingfiltering in the result set and integrates a bookmarkingsystem for organising relevant resources into topic collections.Building upon these features we envision a systemwhich derives user’s interests from performed actions anduses this information to support the recommendation process.We also report on results of the performed usabilityevaluation and derive directions for further development.

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.