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Comparative Research for Social Recommendations on VK

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10612))

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Abstract

Recommender system is one of the most important component for many companies and social networks such as Facebook and YouTube. A recommendation system consists of algorithms which allow to predict and recommend friends or products. This paper studies to facilitate finding like-minded people with same interests in social networks. In our research we used real data from the most popular social network in Russia, VK (Vkontakte). The result shows that majority of users in VK tend not to add possible users with whom they have common acquaintances. We also propose a topology based similarity measure to predict future friends. Then we compare our results with the results of other well known methods and discuss differences.

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Notes

  1. 1.

    The most popular Chinese microblogging service.

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Authors and Affiliations

  1. Innopolis University, Innopolis, 420500, Russia

    Rustam Tukhvatov & Joo Young Lee

Authors
  1. Rustam Tukhvatov
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  2. Joo Young Lee
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Corresponding author

Correspondence to Joo Young Lee .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Shaoxu Song

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Tukhvatov, R., Lee, J.Y. (2017). Comparative Research for Social Recommendations on VK. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_25

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  • DOI: https://doi.org/10.1007/978-3-319-69781-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69780-2

  • Online ISBN: 978-3-319-69781-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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