Abstract
In recent years, Event Based Social Networks (EBSNs) platforms have increasingly entered people’s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommend interested events to users. Different from traditional social networks, both online and offline factors play an important role in EBSNs. However, the existing methods do not make full use of the online and offline information, which may lead a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the shortcomings talked above. At first, a feature extraction phase is constructed to make full user of the EBSN information. And then, we regard the recommendation problem as a classification problem and ELM is extended as the classifier in the model. Extensive experiments are conducted on real EBSN datasets. The experimental results demonstrate that our approach is efficient and has a better performance than some existing methods.
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Li, B., Wang, G., Cheng, Y., Sun, Y. (2020). An Event Recommendation Model Using ELM in Event-Based Social Network. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_17
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DOI: https://doi.org/10.1007/978-3-030-23307-5_17
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