IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i1d10.1007_s10660-019-09390-3.html
   My bibliography  Save this article

Leveraging friend and group information to improve social recommender system

Author

Listed:
  • Jianshan Sun

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies)

  • Rongrong Ying

    (Hefei University of Technology)

  • Yuanchun Jiang

    (Hefei University of Technology)

  • Jianmin He

    (Hefei University of Technology)

  • Zhengping Ding

    (Hefei University of Technology)

Abstract

In recent years, we have witnessed a flourish of social commerce services. Online users can easily share their experiences on products or services with friends. Social recommender systems are employed to tailor right products for user needs. However, existing recommendation methods try to consider the social information to improve the recommendation performance while they do not differ the impact of different social information and do not have deep analysis on social information. In this paper, we propose a social recommendation framework to leverage the friend and group information to extend the traditional BPR model from different perspectives. Through a detailed experiment on LAST.FM data set, we find that the proposed methods are effective in improving the recommendation accuracy and we also have a good understanding for the impact of friend and group information on recommendation performance.

Suggested Citation

  • Jianshan Sun & Rongrong Ying & Yuanchun Jiang & Jianmin He & Zhengping Ding, 2020. "Leveraging friend and group information to improve social recommender system," Electronic Commerce Research, Springer, vol. 20(1), pages 147-172, March.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:1:d:10.1007_s10660-019-09390-3
    DOI: 10.1007/s10660-019-09390-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-019-09390-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-019-09390-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo-yin Jiang & Fei-cheng Ma & Yun-zhou Fang & Xiu-zhen Li, 2013. "User Transfer in Collaborative Commerce: Evident from Virtual Community to Social Commerce," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 235-240, Springer.
    2. Chang, Yu-Ting & Yu, Hueiju & Lu, Hsi-Peng, 2015. "Persuasive messages, popularity cohesion, and message diffusion in social media marketing," Journal of Business Research, Elsevier, vol. 68(4), pages 777-782.
    3. Xuemei Tian & Libo Liu, 2017. "Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research," Electronic Commerce Research, Springer, vol. 17(1), pages 169-183, March.
    4. Qi Li & Ni Liang & Eldon Y. Li, 2018. "Does friendship quality matter in social commerce? An experimental study of its effect on purchase intention," Electronic Commerce Research, Springer, vol. 18(4), pages 693-717, December.
    5. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    6. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    7. Ananda Das Gupta, 2014. "Social Impact Management," India Studies in Business and Economics, in: Business Ethics, edition 127, chapter 0, pages 189-195, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ransome Epie Bawack & Emilie Bonhoure, 2023. "Influencer is the New Recommender: insights for Theorising Social Recommender Systems," Information Systems Frontiers, Springer, vol. 25(1), pages 183-197, February.
    2. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.
    3. Weiwei Deng, 2022. "Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network," Electronic Commerce Research, Springer, vol. 22(4), pages 1079-1105, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tajvidi, Mina & Richard, Marie-Odile & Wang, YiChuan & Hajli, Nick, 2020. "Brand co-creation through social commerce information sharing: The role of social media," Journal of Business Research, Elsevier, vol. 121(C), pages 476-486.
    2. Zaiyan Wei & Mo Xiao & Rong Rong, 2021. "Network Size and Content Generation on Social Media Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1406-1426, May.
    3. Liu Yuan & Zhao Huang & Wei Zhao & Pavel Stakhiyevich, 2020. "Interpreting and predicting social commerce intention based on knowledge graph analysis," Electronic Commerce Research, Springer, vol. 20(1), pages 197-222, March.
    4. Yazan Alshamaila & Ibrahim Aljarah & Ala’ M. Al-Zoubi, 2018. "Explaining Individuals’ Usage of Social Commerce: A Data Mining Approach," Modern Applied Science, Canadian Center of Science and Education, vol. 12(8), pages 116-116, August.
    5. Alalwan, Ali Abdallah, 2018. "Investigating the impact of social media advertising features on customer purchase intention," International Journal of Information Management, Elsevier, vol. 42(C), pages 65-77.
    6. Hajer Kefi & Sitesh Indra & Talel Abdessalem, 2016. "Social media marketing analytics : a multicultural approach applied to the beauty & cosmetic sector," Post-Print hal-01456580, HAL.
    7. Sheng, Jie, 2019. "Being Active in Online Communications: Firm Responsiveness and Customer Engagement Behaviour," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 40-51.
    8. Zhang, Hong & Zhao, Ling & Gupta, Sumeet, 2018. "The role of online product recommendations on customer decision making and loyalty in social shopping communities," International Journal of Information Management, Elsevier, vol. 38(1), pages 150-166.
    9. Abouk, Rahi & Jalali, Nima & Papatla, Purushottam, 2024. "Can tweets be word of mouth that changes risky behaviors?," Journal of Business Research, Elsevier, vol. 174(C).
    10. Busalim, Abdelsalam H. & Hussin, Ab Razak Che, 2016. "Understanding social commerce: A systematic literature review and directions for further research," International Journal of Information Management, Elsevier, vol. 36(6), pages 1075-1088.
    11. Reza Mousavi & Bin Gu, 2019. "The Impact of Twitter Adoption on Lawmakers’ Voting Orientations," Service Science, INFORMS, vol. 30(1), pages 133-153, March.
    12. Nadeem, Waqar & Andreini, Daniela & Salo, Jari & Laukkanen, Tommi, 2015. "Engaging consumers online through websites and social media: A gender study of Italian Generation Y clothing consumers," International Journal of Information Management, Elsevier, vol. 35(4), pages 432-442.
    13. Wang, Yichuan & Yu, Chiahui, 2017. "Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning," International Journal of Information Management, Elsevier, vol. 37(3), pages 179-189.
    14. Nour El Houda Ben Amor & Mohamed Nabil Mzoughi, 2023. "Do Millennials’ Motives for Using Snapchat Influence the Effectiveness of Snap Ads?," SAGE Open, , vol. 13(3), pages 21582440231, July.
    15. Majumdar, Adrija & Bose, Indranil, 2019. "Do tweets create value? A multi-period analysis of Twitter use and content of tweets for manufacturing firms," International Journal of Production Economics, Elsevier, vol. 216(C), pages 1-11.
    16. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    17. Pyo, Tae-Hyung & Tamrakar, Chanchal & Lee, Jae Young & Choi, Yun Seob, 2023. "Is social capital always “Capital”?: Measuring and leveraging social capital in online user communities for in-group diffusion," Journal of Business Research, Elsevier, vol. 158(C).
    18. Kozitsin, Ivan V., 2024. "Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    19. Eduard Marinov, 2017. "The 2017 Nobel Prize in Economics," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 117-159.
    20. Carolina Herrando & Julio Jiménez-Martínez & María José Martín-De Hoyos, 2017. "Passion at first sight: how to engage users in social commerce contexts," Electronic Commerce Research, Springer, vol. 17(4), pages 701-720, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:20:y:2020:i:1:d:10.1007_s10660-019-09390-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.