Abstract
Event-based social networks (EBSNs), which link the online and offline social networks, are increasing popular online services. Along with dramatic rise of the users and events in EBSNs, it is necessary to recommend event to users. Taking full advantage of social networks information can significantly improve predictive accuracy in recommender systems. The intuition here is that the user’s response to events are determined by his/her instinct and behaviours of friends. We propose a Heterogeneous Social Poisson Factorization(HSPF) model which combines online and offline social networks into one framework, and integrates the tie strength of online and offline friend relationships to the model. We test HSPF on Meetup dataset. Experimental results demonstrate that HSPF outperforms state-of-the-art recommendation methods.
This work is supported by National Basic Research Program of China(973)(No. 2014CB340403, No.2012CB316205), National High Technology Research and Development Program of China (863) (No.2014AA015204) and NSFC under the grant No.61272137, 61033010, 61202114, 61532021, 61502421 and NSSFC (No.12&ZD220), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China(15XNLQ06). It was partially done when the authors worked in SA Center for Big Data Research in RUC. This Center is funded by a Chinese National 111 Project Attracting.
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Wang, S., Wang, Z., Li, C., Zhao, K., Chen, H. (2016). Learn to Recommend Local Event Using Heterogeneous Social Networks. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_14
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