Recommendations based on Social Relationships in Mobile Services

The scarcity problem in user–product matrix has become severe, which is affecting the recommendation system efficiency in mobile services; it is also related to social networks and Internet of Things, where huge amount of data and complex relationships exist. This paper proposes a novel recommendation approach based on social relationships between users to handle the scarcity problem and facilitate recommendations in mobile services. We define a model of social relationships based on a set of call detail record factors of telecom users and design a vacancy‐filling method to reduce the scarcity of the user–product matrix. An integrated similarity measure is provided to improve the filtering rules of neighbours of the target user. Then, we build a new recommendation system based on social relationships, with mobile services in the telecom industry as the application. Furthermore, we conductexperiments with the real‐world data of voice calls, and experimental results show that the filling method proposed can effectively reduce the scarcity of the user–product matrix and our social relationships approach outperforms the collaborative filtering in terms of the call, precision and mean absolute error indicators.