Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls

Barzaiq, Osama O.; Loke, Seng W.

doi:10.1007/s00779-011-0401-5

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…In the same momentum, several other researches are found, e.g. in [29][30][31]. Although our social relations based design of the contact list is not oriented toward prediction of the next callee, it can help retrieving efficiently a contact in a "crowded" contact list provided a social relation of his is known.…”

Section: Related Workmentioning

confidence: 98%

On a Mobile Phone Contact List Based on Social Relations

Poda¹,

Zongo²,

Dagnogo³

et al. 2018

EAI Endorsed Transactions on Smart Cities

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Social relations are unquestionably the major provider of contacts in the mobile devices contact lists. However, they remain marginal in the literature of social software related to the contact list and are not at all integrated in its design. Inspired by African social and cultural practices, this paper is about a design of the contact list that integrates the social relationships existing between people in the real life. A desktop prototype of the proposed contact list designing allows to validate it. Illustrations of the developed application demonstrate the assets of the proposed contact list regarding issues such as contacts reminding/retrieval and homonymy resolving with respect to the real life. Additional capabilities of the proposed contact list reside in the creation of groups of contacts for group communications needs.

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Section: Related Workmentioning

confidence: 98%

On a Mobile Phone Contact List Based on Social Relations

Poda¹,

Zongo²,

Dagnogo³

et al. 2018

EAI Endorsed Transactions on Smart Cities

View full text Add to dashboard Cite

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“…Various variations of call prediction algorithms using call logs have been proposed with varying effectiveness [16,[18][19][20][36][37][38]. One of the more established algorithms is proposed by Stefanis et al [2,17] which uses recency and frequency of communication as contextual information and achieved a prediction accuracy of 80%.…”

Section: Call Predictionmentioning

confidence: 99%

“…For example, a user might start from the call-log only to find that the required alter was not recently contacted and hence might move to contact book to search that alter. Researchers are trying to improve a user's experience when initiating calls by analysing the calling behaviour of users [13][14][15] and some proposed methods to predict the alter that will be contacted at a given time [ 2,[16][17][18][19][20][21]. This prediction is then used to make a short list of alters as a recommendation for the ego with the goal of reducing the effort to find and call a particular alter.…”

mentioning

confidence: 99%

Text and phone calls: user behaviour and dual-channel communication prediction

Hayat

Rextin

Idris³

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Smartphones allow users to communicate with their social network [1] mostly through verbal, or written communication [2, 3] throughout the day [4, 5] except during resting periods [6]. The volume of this communication may roughly reflect the level of emotional attachment with various social relations [7, 8]. More specifically, if f 1 , f 2 ,. .. , f n , is the reverse sorted fractions of communication volume with n contacts of a user; then a large fraction of communication takes place with only a handful of initial contacts in this sequence [9-12]. Note that contacts can also be referred to as alters, in the sociological

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“…Phithakkitnukoon et al [36] discuss about the adequate amount of historical data than considering the entire historical for constructing a predictive model for caller behavior. Besides these approaches, a number of authors [37][38][39] deal with the problem of managing personal information in their mobile phones based on their different usage patterns for a static period of log data. Although, the most recent pattern is more significant than older ones, these approaches use an arbitrary period of recent log data from the entire data set.…”

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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Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls

Cited by 15 publications

References 6 publications

On a Mobile Phone Contact List Based on Social Relations

On a Mobile Phone Contact List Based on Social Relations

Text and phone calls: user behaviour and dual-channel communication prediction

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

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