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13 October 2022 Link prediction based on collaborative filtering
Xiran Jiang, Lihua Zhou
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Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872D (2022) https://doi.org/10.1117/12.2640757
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China
Abstract
The research of link prediction is widely used in many fields such as social relations and biological sciences. So it has become a very important research direction. Existing link prediction methods are usually based on the assumption that "the more similar two nodes are, the higher the probability that a link exists between them". That is, the similarity between nodes is considered as the existence probability of a link between nodes. Such an approach does not fully exploit the hidden information in the network, so the accuracy of prediction needs to be further improved. In this paper, we propose a link prediction model based on collaborative filtering. The model combines the similarities between nodes and the known network topology by the method of collaborative filtering, which effectively improves the accuracy of link prediction. Extensive experiments on six real-world datasets demonstrate the validity of the proposed model.
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Xiran Jiang and Lihua Zhou "Link prediction based on collaborative filtering", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872D (13 October 2022); https://doi.org/10.1117/12.2640757
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KEYWORDS
Data modeling
Quality measurement
Scientific research
Social networks
Social sciences
Technologies and applications
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