974 research outputs found
Visualization for Recommendation Explainability: A Survey and New Perspectives
Providing system-generated explanations for recommendations represents an
important step towards transparent and trustworthy recommender systems.
Explainable recommender systems provide a human-understandable rationale for
their outputs. Over the last two decades, explainable recommendation has
attracted much attention in the recommender systems research community. This
paper aims to provide a comprehensive review of research efforts on visual
explanation in recommender systems. More concretely, we systematically review
the literature on explanations in recommender systems based on four dimensions,
namely explanation goal, explanation scope, explanation style, and explanation
format. Recognizing the importance of visualization, we approach the
recommender system literature from the angle of explanatory visualizations,
that is using visualizations as a display style of explanation. As a result, we
derive a set of guidelines that might be constructive for designing explanatory
visualizations in recommender systems and identify perspectives for future work
in this field. The aim of this review is to help recommendation researchers and
practitioners better understand the potential of visually explainable
recommendation research and to support them in the systematic design of visual
explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page
Tagging and Tag Recommendation
Tagging has emerged as one of the best ways of associating metadata with objects (e.g., videos, texts) in Web 2.0 applications. Consisting of freely chosen keywords assigned to objects by users, tags represent a simpler, cheaper, and a more natural way of organizing content than a fixed taxonomy with a controlled vocabulary. Moreover, recent studies have demonstrated that among other textual features such as title, description, and user comments, tags are the most effective to support information retrieval (IR) services such as search, automatic classification, and content recommendation. In this context, tag recommendation services aim at assisting users in the tagging process, allowing users to select some of the recommended tags or to come up with new ones. Besides improving user experience, tag recommendation services potentially improve the quality of the generated tags, benefiting IR services that rely on tags as data sources. Besides the obvious benefit of improving the description of the objects, tag recommendation can be directly applied in IR services such as search and query expansion. In this chapter, we will provide the main concepts related to tagging systems, as well as an overview of tag recommendation techniques, dividing them into two stages of the tag recommendation process: (1) the candidate tag extraction and (2) the candidate tag ranking
Social software for music
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200
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Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions
Exploratory Browsing
In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing.
We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives.
We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities.
The main studied media types are photographs and music.
The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections
What do end-users really want? Investigation of human-centered XAI for mobile health apps
In healthcare, AI systems support clinicians and patients in diagnosis,
treatment, and monitoring, but many systems' poor explainability remains
challenging for practical application. Overcoming this barrier is the goal of
explainable AI (XAI). However, an explanation can be perceived differently and,
thus, not solve the black-box problem for everyone. The domain of
Human-Centered AI deals with this problem by adapting AI to users. We present a
user-centered persona concept to evaluate XAI and use it to investigate
end-users preferences for various explanation styles and contents in a mobile
health stress monitoring application. The results of our online survey show
that users' demographics and personality, as well as the type of explanation,
impact explanation preferences, indicating that these are essential features
for XAI design. We subsumed the results in three prototypical user personas:
power-, casual-, and privacy-oriented users. Our insights bring an interactive,
human-centered XAI closer to practical application
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