The contact list size of modern mobile phone users has increased up to hundreds of contacts, making contact retrieval a relatively difficult task. Various algorithms have been designed to predict the contact that a user will call at a given time. These algorithms use historical call data to make this prediction. However, modern mobile users do not just make calls, but also rely on various communication channels like text messages and calls to maintain their social relations. Despite the prevalence of multiple communication channels, predictive analysis of these channels has not been studied so far. Hence, this study deliberated on proposing a predictive model for dual-channel (text and calls). This study initially investigated the dual-channel communication behaviour of smartphone users by using a mixed approach i.e. subjective and objective data analysis and found many peculiarities. It was observed that the preferred communication channel was different for various contacts, even for a single user. Although the cost-effective texts were found to be more popular over phone calls, a significant proportion of user pairs seemed to prefer calls for most of their communication. A generic predictive framework for the dual-channel environment was proposed based upon these findings. This model predicts the next communication event by modelling temporal information of call and text on a 2D plane. This framework has three variations which not only predict the person who will be contacted at a particular time but also predict the channel of communication (call or text). Finally, the performance of different versions of the algorithm was evaluated using real-world dual-channel data. One version of the predictive model outperformed the other variations with a prediction accuracy over 90 percent, while the other variations also performed well.

现代手机用户的联系人列表规模已增加到数百个联系人，这使得联系人检索成为一项相对困难的任务。已经设计了各种算法来预测用户在给定时间将呼叫的联系人。这些算法使用历史通话数据来进行预测。然而，现代移动用户不仅仅只是打电话，还依靠短信、电话等各种沟通渠道来维持社交关系。尽管多种沟通渠道盛行，但迄今为止尚未研究这些渠道的预测分析。因此，本研究考虑提出双通道（短信和通话）的预测模型。本研究首先采用主观和客观数据分析的混合方法研究了智能手机用户的双通道通信行为，并发现了许多特殊性。据观察，即使对于单个用户，不同联系人的首选通信渠道也是不同的。尽管成本效益高的短信比电话更受欢迎，但很大一部分用户似乎更喜欢通过电话进行大部分交流。基于这些发现提出了双通道环境的通用预测框架。该模型通过在 2D 平面上对呼叫和文本的时间信息进行建模来预测下一个通信事件。该框架具有三种变体，不仅可以预测在特定时间将联系的人，还可以预测通信渠道（电话或短信）。最后，使用真实双通道数据评估不同版本算法的性能。预测模型的一个版本优于其他变体，预测准确度超过 90%，而其他变体也表现良好。