Abstract
With the popularity of social network platforms in the crowd, more and more platforms begin to develop friend recommendation services to fit the users’ demands. Current research on friend recommendation strategies are mainly based on the nodes structural characteristics and path information of the friendship network. The recommendation strategies that consider node information are more efficient for large-scale networks, such as the Adamic-Adar Index. However, it solely utilizes the degree information of common neighbors and ignores the structural characteristics of the target nodes themselves. In this paper we attempted to improve the friend recommendation performance by incorporating the structural characteristics of the target nodes and the interactions between these nodes into the Adamic-Adar Index. In order to verify the effectiveness of our proposed algorithms, we conducted several groups of comparative experiments. The experimental results show that our proposed algorithm can effectively improve the recommendation performance comparing with the benchmark.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153), and Natural Science Foundation of Guangdong Province of China (Project No. 2017A030310427).
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Yang, C., Liu, T., Liu, L., Chen, X., Hao, Z. (2018). A Personalized Friend Recommendation Method Combining Network Structure Features and Interaction Information. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_25
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DOI: https://doi.org/10.1007/978-3-319-93818-9_25
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