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Multi-agent celebrity recommender system (MACeRS): Twitter use case

Abstract

The advancement of social communities and virtual interaction of thoughts have apparently made social networking one of the fastest-growing concepts. The interaction carries meanings beyond friendship and is applied to larger areas, such as communities and networks for business, trade, cinema, and broadcasting. In a social network, the user wants to find her/his interests, and by doing so the community, to which he/she belongs, develops and grows. However, the lack of important and useful information, and sometimes its inaccessibility, hinders users from establishing good connections, and as a consequence, it hinders expanding the community. The current paper presents a method of celebrity-based friend recommendation system based on the preferences and tendencies of the user and his/her friends. The proposed method introduces a novel way of extracting and modeling the recommendation process as a game theory problem with two main agents (Celebrity and Non-Celebrity) for selecting the members with more than 10000 followers, as celebrities, to be recommended. We have used the real data from Twitter social network celebrity members to test and analyze our proposed system from two aspects, i.e., recommender system and social network. The outcomes show that almost all the items recommended by MACeRS are celebrities (99%). Moreover, the accuracy of MACeRS is significantly better than other baseline methods.

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Notes

  1. https://github.com/mirsamantajbakhsh/MACeRS.

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Correspondence to Mir Saman Tajbakhsh.

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Tajbakhsh, M.S., Emamgholizadeh, H., Solouk, V. et al. Multi-agent celebrity recommender system (MACeRS): Twitter use case. Soc. Netw. Anal. Min. 12, 11 (2022). https://doi.org/10.1007/s13278-021-00845-w

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  • DOI: https://doi.org/10.1007/s13278-021-00845-w

Keywords

  • Recommender System
  • Multi-Agent System
  • Celebrity Recommendation
  • Social Network
  • Friend Recommender System
  • Twitter