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Research on Relationship Strength under Personalized Recommendation Service

Author

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  • Wanqiong Tao

    (School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management Engineering and E-business, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Chunhua Ju

    (School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Chonghuan Xu

    (School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

Suggested Citation

  • Wanqiong Tao & Chunhua Ju & Chonghuan Xu, 2020. "Research on Relationship Strength under Personalized Recommendation Service," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1459-:d:321156
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    References listed on IDEAS

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    1. Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
    2. Andrew Cron & Liang Zhang & Deepak Agarwal, 2014. "Collaborative filtering for massive multinomial data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 701-715, April.
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    1. Mircea Constantin Duică & Nicoleta Valentina Florea & Anișoara Duică & Irina Antoaneta Tănăsescu, 2020. "The Role of E-Skills in Developing Sustainable Organizations and E-Activities in the New Digitized Business World," Sustainability, MDPI, vol. 12(8), pages 1-21, April.

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