Cecilia di Sciascio

The Recommendation Dashboard: A System to Visualise and Organise Recommendations

Recommender systems are becoming common tools supporting automatic, context-based retrieval of resources. When the number of retrieved resources grows large visual tools are required that leverage the capacity of human vision to analyse large amounts of information. We introduce a Web-based visual tool for exploring and organising recommendations retrieved from multiple sources along dimensions relevant to cultural heritage and educational context. Our tool provides several views supporting filtering in the result set and integrates a bookmarking system for organising relevant resources into topic collections. Building upon these features we envision a system which derives users interests from performed actions and uses this information to support the recommendation process. We also report on results of the performed usability evaluation and derive directions for further development.

WikiLyzer: Interactive Information Quality Assessment in Wikipedia

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 anyone, 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 algorithmic 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 combine automatic methods and human intelligence. We developed WikiLyzer, a Web toolkit comprising three interactive applications designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement, (ii) Wikipedia users searching for good articles, and (iii) Wikipedia authors that need to identify weaknesses to improve a particular article. A design study sheds a light on how experts could create complex quality metrics with our tool, while a user study reports on its usefulness to identify high-quality content.

Supporting Exploratory Search with a Visual User-Driven Approach

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.

uRank: Visual analytics approach for search result exploration

uRank is a Web-based tool combining lightweight text analytics and visual methods for topic-wise exploration of document sets. It includes a view summarizing the content of the document set in meaningful terms, a dynamic document ranking view and a detailed view for further inspection of individual documents. Its major strength lies in how it supports users in reorganizing documents on-the-fly as their information interests change. We present a preliminary evaluation showing that uRank helps to reduce cognitive load compared to a traditional list-based representation.

Visual Recommendations for Scientific and Cultural Content

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

A Study on User-Controllable Social Exploratory Search

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.

Exploring and Summarizing Document Colletions with Multiple Coordinated Views

Knowledge work such as summarizing related research in preparation for writing, typically requires the extraction of useful information from scientific literature. Nowadays the primary source of information for researchers comes from electronic documents available on the Web, accessible through general and academic search engines such as Google Scholar or IEEE Xplore. Yet, the vast amount of resources makes retrieving only the most relevant results a difficult task. As a consequence, researchers are often confronted with loads of low-quality or irrelevant content. To address this issue we introduce a novel system, which combines a rich, interactive Web-based user interface and different visualization approaches. This system enables researchers to identify key phrases matching current information needs and spot potentially relevant literature within hierarchical document collections. The chosen context was the collection and summarization of related work in preparation for scientific writing, thus the system supports features such as bibliography and citation management, document metadata extraction and a text editor. This paper introduces the design rationale and components of the PaperViz. Moreover, we report the insights gathered in a formative design study addressing usability.