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.
|Number of pages||18|
|Journal||Human-centric Computing and Information Sciences|
|Publication status||Published - Dec 2020|
- Adaptive interfaces
- Artificial intelligence
- Dual-channel prediction
- Machine learning