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Social explorative attention based recommendation for content distribution platforms

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Abstract

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user’s attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users’ historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users’ higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.

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Notes

  1. Steemit (https://steemit.com/) is a blockchain based social media and decentralized content distribution platform for consumers and creators to earn Steemit tokens by playing with the platform and interacting with others. It is regarded as a more effective content distribution ecosystem that allows small content creators to share their creative contents while protecting the copyright without any intermediaries.

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Acknowledgements

The authors of this paper were supported by NSFC (U20B2053), Hong Kong RGC including Early Career Scheme (ECS, No. 26206717), General Research Fund (GRF, No. 16211520), and Research Impact Fund (RIF, No. R6020-19), and WeBank-HKUST Joint Lab. This article was partially done when the first author was an intern at WeBank AI Department. We also thank the anonymous reviewers for their valuable comments and suggestions that help improve the quality of this manuscript.

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Authors and Affiliations

  1. HKUST, Hong Kong, China

    Wenyi Xiao, Haojie Pan & Yangqiu Song

  2. 4Paradigm Inc., Beijing, China

    Huan Zhao

  3. Peng Cheng Laboratory, Shenzhen, China

    Yangqiu Song

  4. WeBank, Shenzhen, China

    Vincent W. Zheng & Qiang Yang

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  1. Wenyi Xiao
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  2. Huan Zhao
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Correspondence to Wenyi Xiao.

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Xiao, W., Zhao, H., Pan, H. et al. Social explorative attention based recommendation for content distribution platforms. Data Min Knowl Disc 35, 533–567 (2021). https://doi.org/10.1007/s10618-020-00729-1

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