Who To Connect To? Joint Recommendations In Cross-layer Social Networks

Authors:
Jiaqi Liu Shanghai Jiao Tong University, P.R. China
Qi Lian Shanghai Jiao Tong University, P.R. China
Luoyi Fu Shanghai Jiao Tong University, P.R. China
Xinbing Wang Shanghai Jiaotong University, P.R. China

Abstract:

Social recommendation has been widely applied to offer users suggestions on who to connect to, where most existing strategies overlook the existence of multi-type connections among users. 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 cross-layer social networks. 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 thus 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. Firstly, we prove that the algorithm is efficiently implementable with a constant complexity in each recommendation. Then, we evaluate its recommendation performance by two metrics, i.e., acceptance 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 acceptance and for diversity, its performance is in the same order of the theoretical upperbound. And finally, the effectiveness of the proposed algorithm is validated by our simulations on three real datasets, where it outperforms baseline algorithms with an up to 38% acceptance gain and obtains an around 0.5 diversity ratio to the theoretical upperbound.

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