Recommendations based on Social Relationships in Mobile Services

Abstract

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.

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

“…Cluster 4 is quite diverse. We excluded specialized topics in the field of space science (Metzger 2016), mobile services (Qi et al 2014) and football robots (Bi et al 2017). Among representative works within this cluster, a visualization platform for IoT to control and monitor wireless sensor networks (Bi et al 2016), resource allocation (Pillai and Rao 2016) and resource bundling (Guo et al 2016) are examined.…”

Editorial

“…This special issue of Systems Research and Behavioral Science presents 11 articles reflecting the above‐mentioned three elements (Jin et al , ; Tan et al , ; Jiang et al , ; Pan et al , ; Wang et al , ; Yu et al , ; Qi et al , ; Li et al , ; Shan et al , , ; Yu et al , ). These papers show the evidence that it is desirable to apply systems approach or methodologies to many different fields (Yu et al , ; Wan and Jones, ; Zhao et al , ).…”

Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations

“…For example, mobile service recommendations based on social relationships provide a customized, socially integrated experience. These systems strive to provide users with ideas that suit their unique interests and are enhanced by the collective knowledge of their social networks by cleverly integrating social dynamics into recommendation algorithms (Qi et al, 2014). Although hybrid and group recommendations have made great strides in recent years, there has been little research on how interpersonal relationships affect the effectiveness of recommendation models.…”