基于链路预测的有向互动影响力和用户信任的推荐算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》

魏映婷 倪静 霍良安

Abstract： Aiming at the problems that the traditional recommendation algorithm ignores the influence of the tight structure of social network structure on user trust transmission and the lack of social psychological explanation, a recommendation algorithm based on link prediction for directed interaction and user trust is proposed. Firstly, the similar user circle of the target user is identified by the integrated similarity between the user preference behavior and the social circle. Secondly, the directional interaction influence between the target users is obtained by combining the node gravity index and the directed influence factor. The integrated user trust value of the directional interaction influence and the user score trust finds a trustworthy similar user set in the similar circle of friends of the target user, which effectively improves the accuracy of the recommendation and finally generates the recommendation. The results show that the proposed method has a significant improvement in performance compared to the previous social network recommendation algorithm.

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