Understanding User Preferences towards Rule-based Notification Deferral

Auda, Jonas; Weber, Dominik; Voit, Alexandra; Schneegaß, Stefan

doi:10.1145/3170427.3188688

Cited by 23 publications

(8 citation statements)

References 10 publications

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“…Previous work has explored decision making involving individual devices or applications. For example, Auda et al consider how users can create a rule-based model to manage their smartphone notification load and customise it to their personal needs [3]. Kulesza et al and Stumpf et al demonstrate through a set of experiments and with the use of classical machine learning models the benefit of explainable interfaces to users [33,47].…”

Section: Explainability Of Algorithmsmentioning

confidence: 99%

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

Berkel

Gonçalves

Hettiachchi

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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The increased reliance on algorithmic decision-making in socially impactful processes has intensified the calls for algorithms that are unbiased and procedurally fair. Identifying fair predictors is an essential step in the construction of equitable algorithms, but the lack of ground-truth in fair predictor selection makes this a challenging task. In our study, we recruit 90 crowdworkers to judge the inclusion of various predictors for recidivism. We divide participants across three conditions with varying group composition. Our results show that participants were able to make informed decisions on predictor selection. We find that agreement with the majority vote is higher when participants are part of a more diverse group. The presented workflow, which provides a scalable and practical approach to reach a diverse audience, allows researchers to capture participants' perceptions of fairness in private while simultaneously allowing for structured participant discussion.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Collaborative and social computing.

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Section: Explainability Of Algorithmsmentioning

confidence: 99%

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

Berkel

Gonçalves

Hettiachchi

et al. 2019

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Figure 2b shows the question that was presented to the user afterwards. In Android O and later versions, the notiication removal callback function onNotificationRemoved in NotificationListenerService receives a parameter reason which indicates how the notiication was dismissed 2 . We analyzed all possible values of the reason parameter and derived six notiication removal actions.…”

Section: Esm Taskmentioning

confidence: 99%

“…Two subjects (user1 and user2) did not want any of their notiications to interrupt them immediately, while three others (user33, user34, and user35) preferred to receive immediate alerts for every notiication. Thirteen of 35 participants (user1, 2,4,11,12,13,15,16,17,18,20,21,26) chose łsend now, alert laterž for some of their notiications, while others did not select this option at all. This suggests that individual diferences afect people's attitudes towards deferring the alert.…”

Section: Notification Delivery Preference Breakdown For All and Indiv...mentioning

confidence: 99%

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Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users

Haines

Eguino

et al. 2022

ACM Trans. Comput.-Hum. Interact.

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Notifications are an indispensable feature of mobile devices, but their delivery can interrupt and distract users. Prior work has examined interventions, such as deferring notification delivery to opportune moments, but has not systematically studied how users might prefer an intelligent system to manage their notifications. Hence, we directly probed Android smartphone users’ notification preferences via a one-week experience-sampling study ( N = 35). We found that users prefer mitigating undesired interruptions by suppressing alerts over deferring them and referred to notification content factors more frequently than contextual factors for explaining their preferences. Then we demonstrated the challenges and potentials of leveraging user actions to help predict notification preferences. Specifically, we showed that a model personalized using user actions achieved a performance gain of 39% than a generic model. This improvement is similar to the 42% performance gain using labels solicited from the user while using observable user actions causes no extra disruption.

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“…To solve the problems associated with new items or new users of recommender systems, Wang et al [69] tried to generalize individuals in their system. Unfortunately, this mechanism rather irritated some users as individual preferences differ [70], [71]. Thus, the user's demography has a part to play in context recognition, adaptation and utilization thereby diminishing the essence of collaborative filtering mechanisms.…”

Section: Developmental Trends Of the Applicationsmentioning

confidence: 99%

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

Opoku

2021

EJECE

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The hunt to categorize context-aware applications has been a prevalent issue to developers of context-aware applications. The previous categorizations were based on the functions of the applications. These mechanisms yielded limited results since many applications could not be categorized. This paper categorizes applications into four generations based on developmental trends through a literature survey. The first generation applications focused on data acquisition and used hardware sensors. The second generation applications focused on knowledge acquisition and used software sensors, semantic language and ontology-based modelling languages. The third generation applications focused on intelligent reasoning and used mechanisms to handle information uncertainty. The fourth generation applications deprecate cumbersome ruleset implementations and focus on artificial intelligence whilst taking into consideration the effect of the dynamics of users’ background and preference on contextual information. The study demonstrated that when applications, methods or technologies can be categorized over some time, it is better to classify them into generations.

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Understanding User Preferences towards Rule-based Notification Deferral

Cited by 23 publications

References 10 publications

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

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