Social recommendation between urban vehicular social communities

Abstract

Summary In recent years, social recommendation between communities plays a more and more significant role in urban vehicular social networks. It has become a hot research topic due to its advantage in enhancing the efficiency of information sharing and transmission in urban vehicular social networks. This paper focuses on social recommendation between communities where achieving the optimal configuration of social recommendation is NP‐hard. We proposed an algorithm for the near optimal configuration of social recommendation between urban vehicular social communities (ANOCSR) to make this NP‐hard problem polynomially solvable based on a modified Steiner minimal tree method. Moreover, with the help of the ANOCSR, we propose another algorithm to achieve the optimal configuration of social recommendation between urban vehicular social communities subject to a global external trust value constraint (AOCSRC). We demonstrate that our algorithms are more efficient and effective than existing algorithms through extensive experiments and detailed theoretical analyses.