huashen218/convxai-cia-dataset huggingface dataset:
Title: User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering
Authors: family: Liao
given: Mengqi; family: Sundar
given: S. Shyam; family: B. Walther
given: Joseph
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Empirical study that tells us about how people use a system, User Experience Design, Personalization

Abstract: Three of the most common approaches used in recommender systems are content-based filtering (matching users’ preferences with products’ characteristics), collaborative filtering (matching users with similar preferences), and demographic filtering (catering to users based on demographic characteristics). Do users’ intuitions lead them to trust one of these approaches over others, independent of the actual operations of these different systems? Does their faith in one type or another depend on the quality of the recommendation, rather than how the recommendation appears to have been derived? We conducted an empirical study with a prototype of a movie recommender system to find out. A 3 (Ostensible Recommender Type: Content vs. Collaborative vs. Demographic Filtering) x 2 (Recommendation Quality: Good vs. Bad) experiment (N=226) investigated how users evaluate systems and attribute responsibility for the recommendations they receive. We found that users trust systems that use collaborative filtering more, regardless of the system's performance. They think that they themselves are responsible for good recommendations but that the system is responsible for bad recommendations (reflecting a self-serving bias). Theoretical insights, design implications and practical solutions for the cold start problem are discussed.
Title: Improving understandability of feature contributions in model-agnostic explainable AI tools
Authors: family: Hadash
given: Sophia; family: Willemsen
given: Martijn C.; family: Snijders
given: Chris; family: IJsselsteijn
given: Wijnand A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: argumentation, interpretable machine learning, natural language, explanations

Abstract: Model-agnostic explainable AI tools explain their predictions by means of ’local’ feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by the user (“positive framing”). The second one is to add “semantic labeling”, that explains the directionality of each feature contribution (“this feature leads to +5% eligibility”), reducing additional cognitive processing steps. In a user study, participants evaluated the understandability of explanations for different framing and labeling conditions for loan applications and music recommendations. We found that positive framing improves understandability even when the prediction is negative. Additionally, adding semantic labels eliminates any framing effects on understandability, with positive labels outperforming negative labels. We implemented our suggestions in a package ArgueView[11].
Title: How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings
Authors: family: Yeh
given: Su-Fang; family: Wu
given: Meng-Hsin; family: Chen
given: Tze-Yu; family: Lin
given: Yen-Chun; family: Chang
given: XiJing; family: Chiang
given: You-Hsuan; family: Chang
given: Yung-Ju
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: non-progress, chatbot, guidance, lab study

Abstract: The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users’ task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.
Title: Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
Authors: family: Cai
given: Wanling; family: Jin
given: Yucheng; family: Chen
given: Li
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: trust, Conversational recommender systems, mixed-initiative interaction, personal characteristics

Abstract: Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a “one-size-fits-all” design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users’ trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.
Title: Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships
Authors: family: Jun
given: Eunice; family: Seo
given: Audrey; family: Heer
given: Jeffrey; family: Just
given: René
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: statistical analysis, end-user programming, transparent statistics, domain-specific language, validity, linear modeling, end-user elicitation

Abstract: Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.
Title: Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices
Authors: family: Head
given: Andrew; family: Xie
given: Amber; family: Hearst
given: Marti A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: visual links, mathematical notation, details-on-demand, authoring

Abstract: With the increasing growth and impact of machine learning and other math-intensive fields, it is more important than ever to broaden access to mathematical notation. Can new visual and interactive displays help a wider readership successfully engage with notation? This paper provides the first detailed qualitative analysis of math augmentation—the practice of embellishing notation with novel visual design patterns to improve its readability. We present two qualitative studies of the practice of math augmentation. First is an analysis of 1.1k augmentations to 281 formulas in 47 blogs, textbooks, and other documents containing mathematical expressions. Second is an interview study with 12 authors who had previously designed custom math augmentations (“maugs”). This paper contributes a comprehensive inventory of the kinds of maugs that appear in math documents, and a detailed account of how authors’ tools ought to be redesigned to support efficient creation of math augmentations. These studies open a critical new design space for HCI researchers and interface designers.
Title: Varv: Reprogrammable Interactive Software as a Declarative Data Structure
Authors: family: Borowski
given: Marcel; family: Murray
given: Luke; family: Bagge
given: Rolf; family: Kristensen
given: Janus Bager; family: Satyanarayan
given: Arvind; family: Klokmose
given: Clemens Nylandsted
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: declarative programming, reprogramming, real-time collaboration, interactive software, liveness

Abstract: Most modern applications are immutable and turn-key despite the acknowledged benefits of empowering users to modify their software. Writing extensible software remains challenging, even for expert programmers. Reprogramming or extending existing software is often laborious or wholly blocked, requiring sophisticated knowledge of application architecture or setting up a development environment. We present Varv, a programming model representing reprogrammable interactive software as a declarative data structure. Varv defines interactive applications as a set of concepts that consist of a schema and actions. Applications in Varv support incremental modification, allowing users to reprogram through addition and selectively suppress, modify, or add behavior. Users can define high-level concepts, creating an abstraction layer and effectively a domain-specific language for their application domain, emphasizing reuse and modification. We demonstrate the reprogramming and collaboration capabilities of Varv in two case studies and illustrate how the event engine allows for extensive tooling support.
Title: VibEmoji: Exploring User-authoring Multi-modal Emoticons in Social Communication
Authors: family: An
given: Pengcheng; family: Zhou
given: Ziqi; family: Liu
given: Qing; family: Yin
given: Yifei; family: Du
given: Linghao; family: Huang
given: Da-Yuan; family: Zhao
given: Jian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: animation, mobile interfaces., emoticons, Social communication, haptic feedback, multi-modal interaction, emotional expression

Abstract: Emoticons are indispensable in online communications. With users’ growing needs for more customized and expressive emoticons, recent messaging applications begin to support (limited) multi-modal emoticons:, enhancing emoticons with animations or vibrotactile feedback. However, little empirical knowledge has been accumulated concerning how people create, share and experience multi-modal emoticons in everyday communication, and how to better support them through design. To tackle this, we developed VibEmoji, a user-authoring multi-modal emoticon interface for mobile messaging. Extending existing designs, VibEmoji grants users greater flexibility to combine various emoticons, vibrations, and animations on-the-fly, and offers non-aggressive recommendations based on these components’ emotional relevance. Using VibEmoji as a probe, we conducted a four-week field study with 20 participants, to gain new understandings from in-the-wild usage and experience, and extract implications for design. We thereby contribute to both a novel system and various insights for supporting users’ creation and communication of multi-modal emoticons.
Title: Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens
Authors: family: Li
given: Zhi; family: Zhao
given: Maozheng; family: Das
given: Dibyendu; family: ZHAO
given: HANG; family: Ma
given: Yan; family: Liu
given: Wanyu; family: Beaudouin-Lafon
given: Michel; family: Wang
given: Fusheng; family: Ramakrishnan
given: IV; family: Bi
given: Xiaojun
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Touch interaction, decision theory, target selection, reinforcement learning

Abstract: Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestion-based Accurate Target Selection method, where target selection is formulated as a sequential decision problem. The objective is to maximize the utility: the negative time cost for the entire target selection procedure. The SATS decision process is dictated by a policy generated using reinforcement learning. It automatically decides when to provide suggestions and when to directly select the target. Our user studies show that SATS reduced error rate and selection time over Shift [51], a magnification-based method, and MUCS, a suggestion-based alternative that optimizes the utility for the current selection. SATS also significantly reduced error rate over BayesianCommand [58], which directly selects targets based on posteriors, with only a minor increase in selection time.
Title: A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users
Authors: family: Bonaker
given: Nicholas Ryan; family: Nel
given: Emli-Mari; family: Vertanen
given: Keith; family: Broderick
given: Tamara
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: accessibility, text entry;, single-switch scanning systems, Augmentative and alternative communication

Abstract: Some individuals with motor impairments communicate using a single switch — such as a button click, air puff, or blink. Row-column scanning provides a method for choosing items arranged in a grid using a single switch. An alternative, Nomon, allows potential selections to be arranged arbitrarily rather than requiring a grid (as desired for gaming, drawing, etc.) — and provides an alternative probabilistic selection method. While past results suggest that Nomon may be faster and easier to use than row-column scanning, no work has yet quantified performance of the two methods over longer time periods or in tasks beyond writing. In this paper, we also develop and validate a webcam-based switch that allows a user without a motor impairment to approximate the response times of a motor-impaired single switch user; although the approximation is not a replacement for testing with single-switch users, it allows us to better initialize, calibrate, and evaluate our method. Over 10 sessions with the webcam switch, we found users typed faster and more easily with Nomon than with row-column scanning. The benefits of Nomon were even more pronounced in a picture-selection task. Evaluation and feedback from a motor-impaired switch user further supports the promise of Nomon.
Title: Enabling Hand Gesture Customization on Wrist-Worn Devices
Authors: family: Xu
given: Xuhai; family: Gong
given: Jun; family: Brum
given: Carolina; family: Liang
given: Lilian; family: Suh
given: Bongsoo; family: Gupta
given: Shivam Kumar; family: Agarwal
given: Yash; family: Lindsey
given: Laurence; family: Kang
given: Runchang; family: Shahsavari
given: Behrooz; family: Nguyen
given: Tu; family: Nieto
given: Heriberto; family: Hudson
given: Scott E; family: Maalouf
given: Charlie; family: Mousavi
given: Jax Seyed; family: Laput
given: Gierad
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: transfer learning, Gesture customization, few-shot learning

Abstract: We present a framework for gesture customization requiring minimal examples from users, all without degrading the performance of existing gesture sets. To achieve this, we first deployed a large-scale study (N=500+) to collect data and train an accelerometer-gyroscope recognition model with a cross-user accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on everyday non-gesture data. Next, we design a few-shot learning framework which derives a lightweight model from our pre-trained model, enabling knowledge transfer without performance degradation. We validate our approach through a user study (N=20) examining on-device customization from 12 new gestures, resulting in an average accuracy of 55.3%, 83.1%, and 87.2% on using one, three, or five shots when adding a new gesture, while maintaining the same recognition accuracy and false-positive rate from the pre-existing gesture set. We further evaluate the usability of our real-time implementation with a user experience study (N=20). Our results highlight the effectiveness, learnability, and usability of our customization framework. Our approach paves the way for a future where users are no longer bound to pre-existing gestures, freeing them to creatively introduce new gestures tailored to their preferences and abilities.
Title: TapType: Ten-finger text entry on everyday surfaces via Bayesian inference
Authors: family: Streli
given: Paul; family: Jiang
given: Jiaxi; family: Fender
given: Andreas Rene; family: Meier
given: Manuel; family: Romat
given: Hugo; family: Holz
given: Christian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Bayesian inference, Bayesian neural network, n-gram language model, virtual reality, invisible interfaces, mobile text entry

Abstract: Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces—without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters’ prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface.
Title: Gesture Elicitation as a Computational Optimization Problem
Authors: family: Tsandilas
given: Theophanis; family: Dragicevic
given: Pierre
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Gesture elicitation, optimization uncertainty, assignment problems, bootstrap, design optimization, learning curves

Abstract: Gesture elicitation studies are commonly used for designing novel gesture-based interfaces. There is a rich methodology literature on metrics and analysis methods that helps researchers understand and characterize data arising from such studies. However, deriving concrete gesture vocabularies from this data, which is often the ultimate goal, remains largely based on heuristics and ad hoc methods. In this paper, we treat the problem of deriving a gesture vocabulary from gesture elicitation data as a computational optimization problem. We show how to formalize it as an optimal assignment problem and discuss how to express objective functions and custom design constraints through integer programs. In addition, we introduce a set of tools for assessing the uncertainty of optimization outcomes due to random sampling, and for supporting researchers’ decisions on when to stop collecting data from a gesture elicitation study. We evaluate our methods on a large number of simulated studies.
Title: CatchLive: Real-time Summarization of Live Streams with Stream Content and Interaction Data
Authors: family: Yang
given: Saelyne; family: Yim
given: Jisu; family: Kim
given: Juho; family: Shin
given: Hijung Valentina
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: video summarization, user interaction data, live summarization, live streaming

Abstract: Live streams usually last several hours with many viewers joining in the middle. Viewers who join in the middle often want to understand what has happened in the stream. However, catching up with the earlier parts is challenging because it is difficult to know which parts are important in the long, unedited stream while also keeping up with the ongoing stream. We present CatchLive, a system that provides a real-time summary of ongoing live streams by utilizing both the stream content and user interaction data. CatchLive provides viewers with an overview of the stream along with summaries of highlight moments with multiple levels of detail in a readable format. Results from deployments of three streams with 67 viewers show that CatchLive helps viewers grasp the overview of the stream, identify important moments, and stay engaged. Our findings provide insights into designing summarizations of live streams reflecting their characteristics.
Title: FitVid: Responsive and Flexible Video Content Adaptation
Authors: family: Kim
given: Jeongyeon; family: Choi
given: Yubin; family: Kahng
given: Minsuk; family: Kim
given: Juho
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Video-Based Learning, Responsive Design, Content Adaptation, Mobile Learning

Abstract: Mobile video-based learning attracts many learners with its mobility and ease of access. However, most lectures are designed for desktops. Our formative study reveals mobile learners’ two major needs: more readable content and customizable video design. To support mobile-optimized learning, we present FitVid, a system that provides responsive and customizable video content. Our system consists of (1) an adaptation pipeline that reverse-engineers pixels to retrieve design elements (e.g., text, images) from videos, leveraging deep learning with a custom dataset, which powers (2) a UI that enables resizing, repositioning, and toggling in-video elements. The content adaptation improves the guideline compliance rate by 24% and 8% for word count and font size. The content evaluation study (n=198) shows that the adaptation significantly increases readability and user satisfaction. The user study (n=31) indicates that FitVid significantly improves learning experience, interactivity, and concentration. We discuss design implications for responsive and customizable video adaptation.