03604nas a2200241   4500008004100000022001400041245004300055210004200098300001700140490000600157520297600163653001303139653001403152653001603166653002103182653001503203653001903218100002803237700001503265700001703280700001803297856004703315       2011                        eng d  a1556-466500aBehavior-based Adaptive Call Predictor0 aBehaviorbased Adaptive Call Predictor  a21:1–21:280 v63 a<p>Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people’s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller’s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user’s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user’s calling behavior and reciprocity. We show that the recent trend of the caller/user’s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user’s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.</p>
10abehavior10acall logs10acall matrix10aconvergence time10aPrediction10atrace distance1 aPhithakkitnukoon, Santi1 aDantu, Ram1 aClaxton, Rob1 aEagle, Nathan  uhttp://doi.acm.org/10.1145/2019583.201958801676nas a2200289   4500008004100000245005900041210005800100260002900158520082200187653001701009653001301026653001601039653001101055653001101066653001901077653001901096653002301115653003301138653003001171653001901201653003901220653003101259653001601290100002801306700001501334856003701349       2007                        eng d00aPredicting Calls: New Service for an Intelligent Phone0 aPredicting Calls New Service for an Intelligent Phone  a San José, USAc11/20073 a<p>Predicting future calls can be the next advanced feature of the intelligent phone as the phone service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedule and attending unwanted communications (e.g. voice spam). Predicting calls is a very challenging task. We believe that this is a new area of research. In this paper, we propose a Call Predictor (CP) that computes the probability of receiving calls and makes call prediction based on caller’s behavior and reciprocity. The proposed call predictor is tested with the actual call logs. The experimental results show that the call predictor performs reasonably well with false positive rate of 2.4416%, false negative rate of 2.9191%, and error rate of 5.3606%.</p>
10aArrival time10abehavior10acall matrix10aCallee10aCaller10aCommunications10aIncoming calls10aInter-arrival time10aInter-arrival/departure time10aKernel density estimation10aOutgoing calls10aProbability density function (pdf)10aReceiving call probability10aReciprocity1 aPhithakkitnukoon, Santi1 aDantu, Ram  uhttps://nsl.cse.unt.edu/node/168