Joint Recommendations in Multilayer Mobile Social Networks

With the rapid spread of mobile devices, many traditional online social network services, like academic applications, start to develop mobile apps and improve their service quality through mobile sensing. A rich friendship among users can provide support for such applications, e.g., help to delegate sensing tasks in mobile crowd sensing. Therefore, studying friend recommendation that offers users' suggestions on who to connect to, so as to enrich the user's friendship, is necessary to Mobile Social Networks (MSNs). Most existing strategies utilize user's relationship, or similarity, to make the recommendation, which overlooks the existence of multi-type connections among users, e.g., common paper based and common topic based connections among authors in academic networks. To overcome such limitation, we characterize each type of connections by a corresponding network layer and then propose a novel algorithm for joint recommendations in multilayer MSNs. Particularly, two types of results are presented in the paper. (i) Our proposed algorithm, named as Cross-layer 2-hop Path (C2P) algorithm, implements the joint recommendation by suggesting a user establish connections to his cross-layer two-hop neighbors, i.e., those who link to the user by two-hop paths with the two hops belonging to two different layers, respectively. In doing so, each produced recommendation item is a combination of user relationships in both two layers and therefore can better meet user demands. (ii) By analytical derivations, along with further empirical validation on real datasets, we give the performance evaluation on our proposed algorithm. First, we prove that the algorithm is efficiently implementable with a constant complexity of each recommendation in most cases. Then, we evaluate its recommendation performance by two metrics, i.e., accuracy and diversity, where the former metric measures recommendation accuracy and the latter one measures an algorithm's capability to provide diverse recommendation items. Our results show that C2P algorithm is optimal in terms of accuracy and for diversity, its performance is no less than the algorithm that is applied in single layers. And finally, the effectiveness of the proposed algorithm is validated on both synthetic and large-scale real datasets, where it outperforms the baseline algorithms with an up to 32 percent accuracy gain and obtains an approximately 0.5 ratio of the algorithm's diversity to the theoretical upperbound.