An event recommendation model using ELM in event-based social network
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EXTREME LEARNING MACHINE AND DEEP LEARNING NETWORKS
An event recommendation model using ELM in event-based social network Boyang Li1 • Guoren Wang2 • Yurong Cheng2 • Yongjiao Sun1 • Xin Bi3 Received: 31 December 2018 / Accepted: 1 July 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract In recent years, event-based social network (EBSN) platforms have increasingly entered people’s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommends interested events to users. Different from traditional social networks, both online and off-line factors play an important role in EBSNs. However, the existing methods do not make full use of the online and off-line information, which may lead to a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the above shortcomings. At first, a feature extraction phase is constructed to make full use of the EBSN information, including spatial feature, temporal feature, semantic feature, social feature and historical feature. And then, we transform the recommendation problem to 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 the existing methods. Keywords Extreme learning machine Event-based social network Event recommendation
1 Introduction Recently, social networks have experienced rapid development and attracted much attention from both industry and academia fields. Event-based social network (EBSN) [17] is a complex and heterogeneous social network, which links the online social groups and the off-line events. Taken Meetup1 as an example, it has attracted more than 16 million users with more than 300 thousand events held each month. In EBSNs, one typical task is to assist users in choosing suitable and personalized events to participate in. & Guoren Wang [email protected] Yurong Cheng [email protected] 1
School of Computer Science and Engineering, Northeastern University, Shenyang, China
2
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
3
Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
Personalized recommendation is becoming a hot topic in recent years [30, 35]. It has been proved that adding topology information [21, 23], community structure [1, 6, 25] and semantic contents [9, 29] can improve the performance of recommendation systems. Different from traditional social networks, EBSNs contain two parts: online social relations and off-line event participation relations. The online relations are generated by entering the same online interested groups, while off-line relations are generated by participating in the same off-line events. Most of recommendation works in traditional social networks focussing
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