ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing

Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively

Designing Explanation Interfaces for Transparency and Beyond

In this work-in-progress paper, we presented a participatory process of designing explanation interfaces for a social recommender system with multiple explanatory goals. We went through four stages to identify the key components of the recommendation model, expert mental model, user mental model, and target mental model. We reported the results of an online survey of current system users (N=14) and a controlled user study with a group of target users (N=15). Based on the findings, we proposed five set of explanation interfaces for five recommendation models (N=25) and discussed the user preference of the interface prototypes

Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance

The use of tagging to support the authoring of personalisable learning content

This research project is interested in the area of personalised and adaptable learning and in particular within an e-learning context. Brusilovsky (1996) and Santally (2005) stress the importance of adaptive systems within e-learning. Karagiannikis and Sampson et al. (2004) argue that personalised learning systems can be defined by their capability to adapt automatically to the changing attitudes of the “learning experience” which can, in turn, be defined by the individual learner characteristics, for example the type of learning material. The project evolved to cover areas including personalised learning, e-learning environments, authoring tools, tagging, learning objects, learning theories and learning styles. The main focus at the start of the project was to provide a personalised and adaptable learning environment for students based on their learning style. During the research, this led to a specific interest about how an academic can create, tag and author learning objects to provide the capability of personalised adaptable e-learning for a learner. Research undertaken was designed to gain an understanding of personalised and adaptive learning techniques, e-learning tools and learning styles. Important findings of this research showed that e-learning platforms do not offer much in the way of a personalised learning experience for a learner. Additionally, the research showed that general adaptive systems and adaptive systems incorporating learning styles are not commonly used or available due to issues with flexibility, reuse and integration. The concept of tagging was investigated during the research and it was found that tagging is underused within e-learning, although the research shows that it could be a good ‘fit’ within e-learning. This therefore led to the decision to create a general purpose discriminatory tagging methodology to allow authors to tag learning objects for personalisation and reuse. The main focus for the evaluation of this tagging methodology was the authoring side of the tagging. It was found that other research projects have evaluated the personalisation of learning content based on a learner’s learning style (see Graf and Kinshuk (2007)). It was therefore felt that there was a sufficient body of existing evidence in this area whereas there was limited research available on the authoring side. The evaluation of the discriminatory tagging methodology demonstrated that the methodology could allow for any discrimination between learners to be used. The example demonstrated within this thesis includes discriminating according to a learner’s learning style and accessibility type. This type of platform independent flexible discriminatory methodology does not exist within current e-learning platforms or other e-learning systems. Therefore, the main contribution of this thesis is therefore a platform independent general-purpose discriminatory tagging methodology

Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs

User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis

Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users’ mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems

Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

Design and implementation of a web-based tailored gymnasium to enhance self-management of fibromyalgia

The aim of this article is to describe the design and development of an online gymnasium that proposes personalized exercise videos to users affected by fibromyalgia. Fibromyalgia syndrome is a chronic condition characterized by widespread pain in muscles, ligaments and tendons, usually associated with sleep disorders and fatigue. Physical exercise is considered as an important component of non-pharmacological treatments of this pathology, and the internet is praised as a powerful resource to promote and improve physical exercise. Yet, while online personalization of health interventions to consumers must be grounded on empirically based guidelines, guidelines for fibromyalgia-targeted exercises are scant. The achievements presented in this paper are twofold. Firstly, we illustrate how we reached definition of the relevant factors for tailoring exercise videos in relation to fibromyalgia. Secondly, we explain the general framework of the application that is composed of an interview module (that investigates the determinant values of a specific user), an adaptation module (presenting the tailored set of exercises) and a logging component (used to monitor users’ interactions with the website). The paper concludes with a discussion on the strengths and weaknesses of the proposed approach

Finding cultural heritage images through a Dual-Perspective Navigation Framework

With the increasing volume of digital images, improving techniques for image findability is receiving heightened attention. The cultural heritage sector, with its vast resource of images, has realized the value of social tags and started using tags in parallel with controlled vocabularies to increase the odds of users finding images of interest. The research presented in this paper develops the Dual-Perspective Navigation Framework (DPNF), which integrates controlled vocabularies and social tags to represent the aboutness of an item more comprehensively, in order that the information scent can be maximized to facilitate resource findability. DPNF utilizes the mechanisms of faceted browsing and tag-based navigation to offer a seamless interaction between experts’ subject headings and public tags during image search. In a controlled user study, participants effectively completed more exploratory tasks with the DPNF interface than with the tag-only interface. DPNF is more efficient than both single descriptor interfaces (subject heading-only and tag-only interfaces). Participants spent significantly less time, fewer interface interactions, and less back tracking to complete an exploratory task without an extra workload. In addition, participants were more satisfied with the DPNF interface than with the others. The findings of this study can assist interface designers struggling with what information is most helpful to users and facilitate searching tasks. It also maximizes end users’ chances of finding target images by engaging image information from two sources: the professionals’ description of items in a collection and the crowd's assignment of social tags