IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v23y2023i3d10.1007_s10660-021-09517-5.html
   My bibliography  Save this article

What social characteristics enhance recommender systems? The effects of network embeddedness and preference heterogeneity

Author

Listed:
  • Feifei He

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education)

  • Chunhua Sun

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education)

  • Yezheng Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education)

Abstract

Recommender systems utilize social relationships to improve recommendation performance. This study explores social characteristics and how they affect recommendation performance. We define social characteristics as network embeddedness and preference heterogeneity. Taking rating characteristics as control variables, we build a regression model to explore the impact of two social characteristics on user-level predictive accuracy and the moderating effect of preference heterogeneity on the relationship between network embeddedness and user-level predictive accuracy. The results suggest that network embeddedness positively influences predictive accuracy, whereas preference heterogeneity negatively influences it. Our research reveals that as the preference heterogeneity increases, the positive effect of network embeddedness on predictive accuracy weakens. Preference heterogeneity has a greater impact on user-level predictive accuracy than network embeddedness. Our findings provide management implications for recommender system designers, which is of great significance for improving the accuracy of user-level prediction and reducing user complaints.

Suggested Citation

  • Feifei He & Chunhua Sun & Yezheng Liu, 2023. "What social characteristics enhance recommender systems? The effects of network embeddedness and preference heterogeneity," Electronic Commerce Research, Springer, vol. 23(3), pages 1807-1827, September.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:3:d:10.1007_s10660-021-09517-5
    DOI: 10.1007/s10660-021-09517-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-021-09517-5
    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/s10660-021-09517-5?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. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
    2. Xiaoye Cheng & Jingjing Zhang & Lu (Lucy) Yan, 2020. "Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 303-320, April.
    3. Lu (Lucy) Yan & Jianping Peng & Yong Tan, 2015. "Network Dynamics: How Can We Find Patients Like Us?," Information Systems Research, INFORMS, vol. 26(3), pages 496-512, September.
    4. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    5. Liye Ma & Ramayya Krishnan & Alan L. Montgomery, 2015. "Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network," Management Science, INFORMS, vol. 61(2), pages 454-473, February.
    6. Chungmok Lee & Minh Pham & Myong K. Jeong & Dohyun Kim & Dennis K. J. Lin & Wanpracha Art Chavalitwongse, 2015. "A Network Structural Approach to the Link Prediction Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 249-267, May.
    7. Rajdeep Grewal & Gary L. Lilien & Girish Mallapragada, 2006. "Location, Location, Location: How Network Embeddedness Affects Project Success in Open Source Systems," Management Science, INFORMS, vol. 52(7), pages 1043-1056, July.
    Full references (including those not matched with items on IDEAS)

    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. Xiaoye Cheng & Jingjing Zhang & Lu (Lucy) Yan, 2020. "Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 303-320, April.
    2. Shuiping Ding & Jie Lin & Zhenyu Zhang, 2020. "Influences of Reference Group on Users’ Purchase Intentions in Network Communities: From the Perspective of Trial Purchase and Upgrade Purchase," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Xuan Yang & Xiao Li & Daning Hu & Harry Jiannan Wang, 2021. "Differential impacts of social influence on initial and sustained participation in open source software projects," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1133-1147, September.
    4. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    5. Glòria Reig-Garcia & Cristina Bosch-Farré & Rosa Suñer-Soler & Dolors Juvinyà-Canal & Núria Pla-Vila & Rosa Noell-Boix & Esther Boix-Roqueta & Susana Mantas-Jiménez, 2021. "The Impact of a Peer Social Support Network from the Perspective of Women with Fibromyalgia: A Qualitative Study," IJERPH, MDPI, vol. 18(23), pages 1-15, December.
    6. Dequiedt, Vianney & Zenou, Yves, 2017. "Local and consistent centrality measures in parameterized networks," Mathematical Social Sciences, Elsevier, vol. 88(C), pages 28-36.
    7. Gandal Neil & Naftaliev Peter & Stettner Uriel, 2017. "Following the Code: Spillovers and Knowledge Transfer," Review of Network Economics, De Gruyter, vol. 16(3), pages 243-267, September.
    8. Wen Wen & Chris Forman & Stuart J. H. Graham, 2013. "Research Note ---The Impact of Intellectual Property Rights Enforcement on Open Source Software Project Success," Information Systems Research, INFORMS, vol. 24(4), pages 1131-1146, December.
    9. Girish Mallapragada & Nandini Lahiri & Atul Nerkar, 2016. "Peer Review and Research Impact," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(1), pages 29-41, March.
    10. Agnieszka Rusinowska & Vassili Vergopoulos, 2020. "Ingratiation and Favoritism in Organizations," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 176(3), pages 413-445.
    11. Lukas Maier & Christian V. Baccarella & Jörn H. Block & Timm F. Wagner & Kai-Ingo Voigt, 2023. "The Legitimization Effect of Crowdfunding Success: A Consumer Perspective," Entrepreneurship Theory and Practice, , vol. 47(4), pages 1389-1420, July.
    12. Jing Peng & Jianghua Zhang & Tengfei Nie, 2025. "Social influence and channel competition in the live-streaming market," Annals of Operations Research, Springer, vol. 344(2), pages 617-645, January.
    13. Lalit Jain & Zhaoqi Li & Erfan Loghmani & Blake Mason & Hema Yoganarasimhan, 2024. "Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models," Marketing Science, INFORMS, vol. 43(5), pages 1002-1030, September.
    14. Ernan Haruvy & Peter T. L. Popkowski Leszczyc, 2010. "Search and Choice in Online Consumer Auctions," Marketing Science, INFORMS, vol. 29(6), pages 1152-1164, 11-12.
    15. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
    16. Sodam Baek & Kibae Kim & Jorn Altmann, 2014. "Role of Platform Providers in Service Networks: The Case of Salesforce.com AppExchange," TEMEP Discussion Papers 2014112, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised May 2014.
    17. Kuk, George & Schaarschmidt, Mario & Homscheid, Dirk, 2024. "All of the same breed? A networking perspective of private-collective innovation," Journal of Business Research, Elsevier, vol. 172(C).
    18. Yuqing Ren & Jilin Chen & John Riedl, 2016. "The Impact and Evolution of Group Diversity in Online Open Collaboration," Management Science, INFORMS, vol. 62(6), pages 1668-1686, June.
    19. Hallin, Christina & Holm, Ulf & Sharma, Dharma Deo, 2011. "Embeddedness of innovation receivers in the multinational corporation: Effects on business performance," International Business Review, Elsevier, vol. 20(3), pages 362-373, June.
    20. Han-Lin Li & Yao-Huei Huang & Shu-Cherng Fang, 2017. "Linear Reformulation of Polynomial Discrete Programming for Fast Computation," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 108-122, February.

    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:23:y:2023:i:3:d:10.1007_s10660-021-09517-5. 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.