Browsing by Subject "user experience"

Now showing 1 - 4 of 4
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    Enhancing User Experience With Recommender Systems Beyond Prediction Accuracies
    (2016-08) Nguyen, Tien
    In this dissertation, we examine to improve the user experience with recommender systems beyond prediction accuracy. We focus on the following aspects of the user experience. In chapter 3 we examine if a recommender system exposes users to less diverse contents over time. In chapter 4 we look at the relationships between user personality and user preferences for recommendation diversity, popularity, and serendipity. In chapter 5 we investigate the relations between the self-reported user satisfaction and the three recommendation properties with the inferred user recommendation consumption. In chapter 6 we look at four different rating inter- faces and evaluated how these interfaces affected the user rating experience. We find that over time a recommender system exposes users to less-diverse contents and that users rate less-diverse items. However, users who took recommendations were exposed to more diverse recommendations than those who did not. Furthermore, users with different personalities have different preferences for recommendation diversity, popularity, and serendipity (e.g. some users prefer more diverse recommendations, while others prefer similar ones). We also find that user satisfaction with recommendation popularity and serendipity measured with survey questions strongly relate to user recommendation consumption inferred with logged data. We then propose a way to get better signals about user preferences and help users rate items in the recommendation systems more consistently. That is providing exemplars to users at the time they rate the items improved the consistency of users’ ratings. Our results suggest several ways recommender system practitioners and re- searchers can enrich the user experience. For example, by integrating users’ personality into recommendation frameworks, we can help recommender systems deliver recommendations with the preferred levels of diversity, popularity, and serendipity to individual users. We can also facilitate the rating process by integrating a set of proven rating-support techniques into the systems’ interfaces.
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    Understanding User Experience: An Essential Component within Academic Support Resources at the University of Minnesota
    (2020) Yoong, Christina
    User experience (UX) has become increasingly important for organizations due to the growing need to design and maintain digital products for different audiences. This study focuses on user experience perspectives within Academic Support Resources (ASR), a service department that supports students, faculty, and staff at the University of Minnesota. Objectives for this study were to investigate user experience perspectives and practices within ASR, explore where UX fits within ASR’s human experience design ecosystem, and provide recommendations for improving UX maturity within the department. A survey was sent to all ASR staff members and several follow-up interviews were conducted with business analysts in ASR-IT. Major findings include that UX is very important to ASR's mission to make a positive difference in students' lives, and everyone in ASR is responsible for UX at some level. A number of recommendations are provided to increase UX maturity within ASR, including to build, document, and share UX knowledge and have leadership prioritize UX in ASR's work and processes. In the end, UX is an essential component of ASR and the department's human experience design ecosystem.
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    Usability of Automatic Speech Recognition Systems for Individuals with Speech Disorders: Past, Present, Future, and A Proposed Model
    (2019) Jefferson, Madeline
    People are using voice assistants (VAs) such as Siri & Alexa more than ever before. With 46% of U.S. adults using VAs, commercially available voice-activated technologies are becoming pervasive in our homes and beyond (Pew Research, 2017). VAs provide convenience, novelty, and unique solutions for the medical industry. But, some users may be left out of the conversation. People with speech disorders or atypical speech historically have found difficulty with using automatic speech recognition (ASR) technologies, the precursor to VAs. Usability testing for these systems has consistently shown that they are not easy to use for people with speech disorders. This investigation sought to perform a literature review of the existing research on the usability of commercially available ASRs for people with speech disorders to provide historical perspectives and to take an inventory of how this issue is being addressed today. A literature review was performed on the usability of commercially available ASRs for people with speech disorders and was divided into two stages: studies before the introduction of VAs and those that tested VAs themselves. Understanding where we have been and where we are now will also inform technical communication and usability professionals on what the future of ASRs may hold and how we can best address the needs of this audience. To do so, this paper proposes solutions for inclusive design in the voice assistant design space including a conceptual model for integrating specific techniques into commercially available VAs.
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    User-Centric Design and Evaluation of Online Interactive Recommender Systems
    (2018-05) Zhao, Qian
    User interaction is present in all user interfaces including recommender systems. Understanding user factors in interactive recommender systems is important for achieving better user experience and overall user satisfaction. Many prior works in recommender systems consider recommendation as a content selection process and there is not much prior work focusing on studying user interaction, except user on-boarding interaction design, rating interface design etc. Even for the content selection part, however, it seems obvious that there are a fair amount of factors lying in the scope of user interaction as well, to name a few, visual attention and item exposure, perceived temporal change, reactivity, confusion; i.e., factors regarding content browsing in a typical information system. My research studies several factors while real users are interacting with online recommender systems and answers a series of questions regarding those factors. Specifically, my research focuses on gaining a better understanding on a) whether users pay attention to grids of recommendations displayed in modern recommender interfaces; b) how to interpret and infer user inaction after we show those recommendations to users and further utilize this inaction model to improve recommendation; c) how to organize and present the top-N recommendations to better utilize user attention and increase user engagement; d) how does recommenders optimizing for being engaging (i.e., as many user interactions as possible) affect user experience compared with recommenders optimizing for being right in estimating user preference and maximizing the preference of users on recommendations displayed; e) how to better support work that combines user-centric design, evaluation and building complex, scalable recommendation models going from offline settings into the online environments of providing interactive real-time responses to user recommendation requests, by building a generic recommender server framework.