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. Fabio M. Manenti & Stefano Comino & Marialaura Parisi, 2005. "From Planning to Mature: on the Determinants of Open Source Take-Off," Industrial Organization 0507006, University Library of Munich, Germany, revised 29 Sep 2005.
    4. 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.
    5. Jentzsch, Nicola & Sapi, Geza & Suleymanova, Irina, 2013. "Targeted pricing and customer data sharing among rivals," International Journal of Industrial Organization, Elsevier, vol. 31(2), pages 131-144.
    6. Tumusiime, Emmanuel & B. Wade, Brorsen & Mosali, Jagadeesh & Johnson, Jim & Locke, James & Biermacher, Jon T., 2011. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 43(4), pages 541-552, November.
    7. Amalesh Sharma & V. Kumar & Jun Yan & Sourav Bikash Borah & Anirban Adhikary, 2019. "Understanding the structural characteristics of a firm’s whole buyer–supplier network and its impact on international business performance," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 50(3), pages 365-392, April.
    8. 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.
    9. 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.
    10. Dennis Fok & Richard Paap & Philip Hans Franses, 2014. "Incorporating Responsiveness to Marketing Efforts in Brand Choice Modeling," Econometrics, MDPI, vol. 2(1), pages 1-25, February.
    11. Dequiedt, Vianney & Zenou, Yves, 2017. "Local and consistent centrality measures in parameterized networks," Mathematical Social Sciences, Elsevier, vol. 88(C), pages 28-36.
    12. 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.
    13. Girish Mallapragada & Aditya Gupta & Brett Josephson, 2022. "The impact of social capital and transaction efficacy on salesperson performance," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3525-3542, September.
    14. Marjan Leber & Andrea Ivanišević & Jelena Borocki & Mladen Radišić & Beata Ślusarczyk, 2018. "Fostering Alliances with Customers for the Sustainable Product Creation," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    15. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    16. Comino, Stefano & Manenti, Fabio M. & Parisi, Maria Laura, 2007. "From planning to mature: On the success of open source projects," Research Policy, Elsevier, vol. 36(10), pages 1575-1586, December.
    17. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    18. Sun, Yutao, 2016. "The structure and dynamics of intra- and inter-regional research collaborative networks: The case of China (1985–2008)," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 70-82.
    19. Stefan Behfar & Ekaterina Turkina & Thierry Burger-Helmchen, 2017. "Network tie structure causing OSS group innovation and growth," Post-Print hal-02153061, HAL.
    20. 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.

    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.