Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2018

di Sciascio Maria Cecilia, Brusilovsky Peter, Veas Eduardo Enrique

A Study on User-Controllable Social Exploratory Search

ACM Conference on Intelligent User Interfaces IUI, ACM, 2018

Konferenz
Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i.e., leveraging information left by past users of information systems. Social search technologies are highly popular nowadays, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. In an online study investigating system accuracy and subjective aspects with a structural model we found that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satisfied.
2017

di Sciascio Maria Cecilia, Mayr Lukas, Veas Eduardo Enrique

Exploring and Summarizing Document Colletions with Multiple Coordinated Views

Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017

Konferenz
Knowledge work such as summarizing related research inpreparation for writing, typically requires the extraction ofuseful information from scientific literature. Nowadays theprimary source of information for researchers comes fromelectronic documents available on the Web, accessible throughgeneral and academic search engines such as Google Scholaror IEEE Xplore. Yet, the vast amount of resources makesretrieving only the most relevant results a difficult task. Asa consequence, researchers are often confronted with loadsof low-quality or irrelevant content. To address this issuewe introduce a novel system, which combines a rich, inter-active Web-based user interface and different visualizationapproaches. This system enables researchers to identify keyphrases matching current information needs and spot poten-tially relevant literature within hierarchical document collec-tions. The chosen context was the collection and summariza-tion of related work in preparation for scientific writing, thusthe system supports features such as bibliography and citationmanagement, document metadata extraction and a text editor.This paper introduces the design rationale and components ofthe PaperViz. Moreover, we report the insights gathered in aformative design study addressing usability
2017

Strohmaier David, di Sciascio Maria Cecilia, Errecalde Marcelo, Veas Eduardo Enrique

WikiLyzer: Interactive Information Quality Assessment in Wikipedia

ACM Intelligent User Interfaces, 2017

Konferenz
Innovations in digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success, but also a hindrance to good quality: contributions can be of poor quality because everyone, even anonymous users, can participate. Though Wikipedia has defined guidelines as to what makes the perfect article, authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever growing amount of articles pending review. Great efforts have been invested in algorith-mic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. However, little has been done to support quality assessment of user-generated content through interactive tools that allow for combining automatic methods and human intelligence. We developed WikiLyzer, a toolkit comprising three Web-based interactive graphic tools designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement , (ii) users searching for good articles, and (iii) users that need to identify weaknesses to improve a particular article. A case study suggests that experts are able to create complex quality metrics with our tool and a report in a user study on its usefulness to identify high-quality content.
2017

Müller-Putz G. R., Ofner P., Schwarz Andreas, Pereira J., Luzhnica Granit, di Sciascio Maria Cecilia, Veas Eduardo Enrique, Stein Sebastian, Williamson John, Murray-Smith Roderick, Escolano C., Montesano L., Hessing B., Schneiders M., Rupp R.

MoreGrasp: Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities

7th Graz Brain-Computer Interface Conference 2017, Graz, 2017

Konferenz
The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface(BCI)for intuitive control of a grasp neuroprosthesisto supportindividuals with high spinal cord injury(SCI)in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation ofa user-centered design.
2017

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Supporting Exploratory Search with a Visual User-Driven Approach

ACM Transactions on Interactive Intelligent Systems, ACM, ACM, 2017

Journal
Whenever we gather or organize knowledge, the task of search-ing inevitably takes precedence. As exploration unfolds, it be-comes cumbersome to reorganize resources along new interests,as any new search brings new results. Despite huge advances inretrieval and recommender systems from the algorithmic point ofview, many real-world interfaces have remained largely unchanged:results appear in an infinite list ordered by relevance with respect tothe current query. We introduceuRank, a user-driven visual tool forexploration and discovery of textual document recommendations.It includes a view summarizing the content of the recommenda-tion set, combined with interactive methods for understanding, re-fining and reorganizing documents on-the-fly as information needsevolve. We provide a formal experiment showing thatuRankuserscan browse the document collection and efficiently gather items rel-evant to particular topics of interest with significantly lower cogni-tive load compared to traditional list-based representations.
2017

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Supporting Exploratory Search with a Visual User-Driven Approach

Transactions on Interactive Intelligent Systems, ACM, 2017

Journal
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce an interactive user-driven tool that aims at supporting users in understanding, refining, and reorganizing documents on the fly as information needs evolve. Decisions regarding visual and interactive design aspects are tightly grounded on a conceptual model for exploratory search. In other words, the different views in the user interface address stages of awareness, exploration, and explanation unfolding along the discovery process, supported by a set of text-mining methods. A formal evaluation showed that gathering items relevant to a particular topic of interest with our tool incurs in a lower cognitive load compared to a traditional ranked list. A second study reports on usage patterns and usability of the various interaction techniques within a free, unsupervised setting.
2016

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Rank As You Go: User-Driven Exploration of Search Results

ACM IUI 2016, ACM New York, NY, USA ©201, New York, 2016

Konferenz
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.
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

Tschinkel Gerwald, di Sciascio Maria Cecilia, Mutlu Belgin, Sabol Vedran

The Recommendation Dashboard: A System to Visualise and Organise Recommendations

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

Konferenz
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.
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, 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

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

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.
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