Skip to main content
Log in

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

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.epinions.com.

  2. http://www.douban.com.

References

  1. Hu, Y., Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. in 2008 Eighth IEEE International Conference on Data Mining. 2008. Ieee.

  2. Ma, H., I. King, and M.R. Lyu. Learning to recommend with social trust ensemble. in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009.

  3. Guo, G., Zhang, J., & Yorke-Smith, N. (2016). A novel recommendation model regularized with user trust and item ratings. IEEE transactions on knowledge and data engineering, 28(7), 1607–1620.

    Article  Google Scholar 

  4. Camacho, L. A. G., & Alves-Souza, S. N. (2018). Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing & Management, 54(4), 529–544.

    Article  Google Scholar 

  5. Yang, B., et al. (2016). Social collaborative filtering by trust. IEEE transactions on pattern analysis and machine intelligence, 39(8), 1633–1647.

    Article  Google Scholar 

  6. Yan, S., et al. (2017). An approach for building efficient and accurate social recommender systems using individual relationship networks. IEEE transactions on knowledge and data engineering, 29(10), 2086–2099.

    Article  Google Scholar 

  7. Xu, C. (2018). A novel recommendation method based on social network using matrix factorization technique. Information processing & management, 54(3), 463–474.

    Article  Google Scholar 

  8. Tang, J., Hu, X., & Liu, H. (2013). Social recommendation: A review. Social Network Analysis and Mining, 3(4), 1113–1133.

    Article  Google Scholar 

  9. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415–444.

    Article  Google Scholar 

  10. Marsden, P. V., & Friedkin, N. E. (1993). Network studies of social influence. Sociological Methods & Research, 22(1), 127–151.

    Article  Google Scholar 

  11. Lewis, K., Gonzalez, M., & Kaufman, J. (2012). Social selection and peer influence in an online social network. Proceedings of the National Academy of Sciences, 109(1), 68–72.

    Article  Google Scholar 

  12. Ma, H., et al. Sorec: social recommendation using probabilistic matrix factorization. in Proceedings of the 17th ACM conference on Information and knowledge management. 2008.

  13. Jamali, M. and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. in Proceedings of the fourth ACM conference on Recommender systems. 2010.

  14. Guo, G., J. Zhang, and N. Yorke-Smith. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. in Proceedings of the AAAI Conference on Artificial Intelligence. 2015.

  15. Ma, H., et al. Recommender systems with social regularization. in Proceedings of the fourth ACM international conference on Web search and data mining. 2011.

  16. Cheng, X., Zhang, J., & Yan, L. (2020). Understanding the impact of individual users’ rating characteristics on the predictive accuracy of recommender systems. INFORMS Journal on Computing, 32(2), 303–320.

    Google Scholar 

  17. Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: Survey of the state of the art. User Modeling and User-Adapted Interaction, 22(4–5), 317–355.

    Article  Google Scholar 

  18. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.

    Article  Google Scholar 

  19. Lee, C., et al. (2015). A network structural approach to the link prediction problem. INFORMS Journal on Computing, 27(2), 249–267.

    Article  Google Scholar 

  20. Grewal, R., Lilien, G. L., & Mallapragada, G. (2006). Location, location, location: How network embeddedness affects project success in open source systems. Management science, 52(7), 1043–1056.

    Article  Google Scholar 

  21. Ma, L., Krishnan, R., & Montgomery, A. L. (2015). Latent homophily or social influence? An empirical analysis of purchase within a social network. Management Science, 61(2), 454–473.

    Article  Google Scholar 

  22. Ahmadian, S., Meghdadi, M., & Afsharchi, M. (2018). A social recommendation method based on an adaptive neighbor selection mechanism. Information Processing & Management, 54(4), 707–725.

    Article  Google Scholar 

  23. Ramos, G., Boratto, L., & Caleiro, C. (2020). On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms. Information Processing & Management, 57(2), 102058.

    Article  Google Scholar 

  24. Kluver, D. and J.A. Konstan. Evaluating recommender behavior for new users. in Proceedings of the 8th ACM Conference on Recommender Systems. 2014.

  25. Adomavicius, G., & Zhang, J. (2012). Impact of data characteristics on recommender systems performance. ACM Transactions on Management Information Systems (TMIS), 3(1), 1–17.

    Article  Google Scholar 

  26. Jannach, D., Zanker, M., & Fuchs, M. (2014). Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations. Information Technology & Tourism, 14(2), 119–149.

    Article  Google Scholar 

  27. Cunha, T., Soares, C., & de Carvalho, A. C. (2018). Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering. Information Sciences, 423, 128–144.

    Article  Google Scholar 

  28. Currarini, S., Jackson, M. O., & Pin, P. (2010). Identifying the roles of race-based choice and chance in high school friendship network formation. Proceedings of the National Academy of Sciences, 107(11), 4857–4861.

    Article  Google Scholar 

  29. Li, X., Sun, C., & Zia, M. A. (2020). Social influence based community detection in event-based social networks. Information Processing & Management, 57(6), 102353.

    Article  Google Scholar 

  30. Dotson, M. R., Büschken, J., & Allenby, G. M. (2020). Explaining preference heterogeneity with mixed membership modeling. Marketing Science, 39(2), 407–426.

    Article  Google Scholar 

  31. Horsky, D., Misra, S., & Nelson, P. (2006). Observed and unobserved preference heterogeneity in brand-choice models. Marketing Science, 25(4), 322–335.

    Article  Google Scholar 

  32. Yan, L., Peng, J., & Tan, Y. (2015). Network dynamics: How can we find patients like us? Information Systems Research, 26(3), 496–512.

    Article  Google Scholar 

  33. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.

    Book  Google Scholar 

  34. Funk, S., Netflix update: Try this at home. 2006.

  35. Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. Recommender systems handbook (pp. 257–297). Springer.

    Chapter  Google Scholar 

  36. Herlocker, J. L., et al. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5–53.

    Article  Google Scholar 

  37. Guo, G., et al. LibRec: A Java Library for Recommender Systems. in UMAP Workshops. 2015.

Download references

Acknowledgements

This work is supported by the Major Program of the National Natural Science Foundation of China (91846201), the National Natural Science Foundation of China (72071069), the National Key Research and Development Program of China (2018YFB1402604). The authors warmly thank all of the anonymous reviewers for their time and efforts.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chunhua Sun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, F., Sun, C. & Liu, Y. What social characteristics enhance recommender systems? The effects of network embeddedness and preference heterogeneity. Electron Commer Res 23, 1807–1827 (2023). https://doi.org/10.1007/s10660-021-09517-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-021-09517-5

Keywords

Navigation