Large-scale evaluation of call-availability prediction

Pielot, Martin

doi:10.1145/2632048.2632060

Cited by 36 publications

(26 citation statements)

References 20 publications

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“…27) suggesting to open a piece of content from Buzzfeed. Our participants were predicted to be bored by our algorithm in 48.0% of the cases.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, it has been shown that computing devices are able to detect a person's openness to receiving office visits [16], emails [21], messages from desktop instant messengers [1], SMS and mobile phone messages [30], mobile phone alerts [31], and mobile phone calls [20,27].…”

Section: Inferring Emotions From Mobile Phone Usagementioning

confidence: 99%

“…This line of research shows that attention and openness to interruptions can be inferred from: time since recent usage of device [1,16]; using of specific services -such as internet browsers, email inbox, calendar [23]-or usage of mobile phone messengers and notification center [27,30]; level of activity -such as switching windows [21,23]-or use of keyboard and mouse [16,21]; ambient noise level (as proxies for the level of actives around the user) [16]; location (differences between home and work) [27,31]; ringer mode (as an indicator of how we want to manage interruptions) [27,31]; time -such as the hour of the day or the day of the week [1,16,20]; and proximity, i.e., if a mobile phone's screen is free or covered (indicating if the phone is stowed away) [27,30].…”

Section: Inferring Emotions From Mobile Phone Usagementioning

confidence: 99%

See 2 more Smart Citations

When attention is not scarce - detecting boredom from mobile phone usage

Pielot

Dingler

Pedro

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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175

106

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Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom -leveraging features related to recency of communication, usage intensity, time of day, and demographics-can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation.

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“…27) suggesting to open a piece of content from Buzzfeed. Our participants were predicted to be bored by our algorithm in 48.0% of the cases.…”

Section: Resultsmentioning

confidence: 99%

Section: Inferring Emotions From Mobile Phone Usagementioning

confidence: 99%

Section: Inferring Emotions From Mobile Phone Usagementioning

confidence: 99%

See 1 more Smart Citation

When attention is not scarce - detecting boredom from mobile phone usage

Pielot

Dingler

Pedro

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Self Cite

175

106

View full text Add to dashboard Cite

show abstract

“…Proposals for automatically inferring a persons availability in office spaces [15] and desktop instant messengers [2], phone calls [25] and mobile instant messengers [26] have been explored. Other researchers have investigated the use of rich-presence information including motion, location and music to enable users to determine when and how to contact an individual in mobile environments [4].…”

Section: Communicating Availabilitymentioning

confidence: 99%

An in-situ study of mobile phone notifications

Pielot

Church

Oliveira

2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

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297

161

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Notifications on mobile phones alert users about new messages, emails, social network updates, and other events. However, little is understood about the nature and effect of such notifications on the daily lives of mobile users. We report from a one-week, in-situ study involving 15 mobile phones users, where we collected real-world notifications through a smartphone logging application alongside subjective perceptions of those notifications through an online diary. We found that our participants had to deal with 63.5 notifications on average per day, mostly from messengers and email. Whether the phone is in silent mode or not, notifications were typically viewed within minutes. Social pressure in personal communication was amongst the main reasons given. While an increasing number of notifications was associated with an increase in negative emotions, receiving more messages and social network updates also made our participants feel more connected with others. Our findings imply that avoiding interruptions from notifications may be viable for professional communication, while in personal communication, approaches should focus on managing expectations.

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“…However, these techniques do not take into account the freshness of rules, i.e., rules that represent recent patterns, in which we are interested to output a complete set of updated behavioral rules based on recency for individual mobile phone users utilizing their contextual smartphone datasets. In order to mine users' contextual mobile phone data to model their behavior, a number of authors use a static period of phone log data, such as phone call logs [22][23][24][25], SMS Log [26], mobile application (apps) usages logs [3,27,28], mobile phone notification logs [10], web logs [29][30][31], game Log [32], context logs [4], and smartphone life log [33,34] etc. for various purposes.…”

Section: Background and Related Workmentioning

confidence: 99%

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

2019

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IntroductionNowadays, smartphones are considered as essential devices in our daily life. Due to the recent advanced features in smartphones and the popularity of context-awareness in mobile technologies, individual's behavioral activities with their phones, such as phone call activities, mobile applications usage, mobile notification responses, social networking, and corresponding contextual information are recorded through the device logs. An individual smartphone's ability to store user's such diverse activities and associated contexts with their phones enables the study on data-driven smartphone usage behavior modeling and prediction [1]. In this paper, we aim to mine a set of personalized recent behavioral patterns, i.e., recency, based rules utilizing individual's contextual phone log data, for the purpose of building an effective context-aware personalized usage behavior prediction model. To illustrate the efficacy of our proposed approach, in this paper, we Abstract Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users' diverse behavioral activities with their phones and associated contexts are recorded through the device logs. Behavioral patterns of smartphone users may vary greatly between individuals in different contexts-for example, temporal, spatial, or social contexts. However, an individual's phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user. Thus, an individual's recent behavioral patterns and corresponding machine learning rules are more likely to be interesting and significant than older ones for modeling and predicting their phone usage behavior. Based on this concept of recency, in this paper, we present an approach for mining recency-based personalized behavior, and name it "RecencyMiner" for short, utilizing individual's contextual smartphone data, in order to build a context-aware personalized behavior prediction model. The effectiveness of RecencyMiner is examined by considering individual smartphone user's real-life contextual datasets. The experimental results show that our proposed recency-based approach better predicts individual's phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.

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Large-scale evaluation of call-availability prediction

Cited by 36 publications

References 20 publications

When attention is not scarce - detecting boredom from mobile phone usage

When attention is not scarce - detecting boredom from mobile phone usage

An in-situ study of mobile phone notifications

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

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