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An effective content-based event recommendation model

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Abstract

Event-based social networks (EBSNs) facilitate people to interact with each other by sharing similar interests in online groups or taking part in offline events together. Event recommendation in EBSNs has been studied by many researchers. However, the problem of recommending the event to the top N active-friends of the key user has rarely been studied in EBSNs. In this paper, we propose a new method to solve this problem. In this method, we first construct an association matrix from the content of events and user features. Then, we define a new content-based event recommendation model, which combines the matrix, spatio-temporal relations and user interests to recommend an event to the active-friends of a key user. A series of experiments were conducted on real datasets collected from Meetup, and the comparison results have demonstrated the effectiveness of the new model.

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Notes

  1. www.meetup.com

  2. www.douban.com

  3. www.meetup.com/meetup_api/

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Thanh Trinh, Dingming Wu & Joshua Zhexue Huang

  2. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand

    Ruili Wang

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  1. Thanh Trinh
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  2. Dingming Wu
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  4. Joshua Zhexue Huang
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Correspondence to Dingming Wu.

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Trinh, T., Wu, D., Wang, R. et al. An effective content-based event recommendation model. Multimed Tools Appl 80, 16599–16618 (2021). https://doi.org/10.1007/s11042-020-08884-9

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