Skip to main content
SpringerLink
Log in

The recommendation of satisfactory product for new users in social commerce website

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recommending the post-use satisfactory product for new users can allow more effectively escalating the sale of products and expanding the new customers. When recommending a post-use satisfactory product for new users, the existing methods often suffer cold-start problems, which have tremendous consequences for their recommendation accuracy. To fill this gap, this study develops the consensus interest prediction model (CIPM) to predict new user’s post-use satisfaction for the recommended products, which overcomes the cold-start problems by improving the robustness of the prediction model and deep extraction of user information based on the homogeneity in social commerce website. Specifically, we extract the five types of homogeneity characteristics between the active users and new users from the aspects of product homogeneity and user social relationship homogeneity. Then the multidimensional homogeneity user interest prediction models are designed with direct linear fusion, direct nonlinear fusion, and indirect fusion of multiple homogeneity indices. Subsequently, in order to improve the robustness of the model, the suitable interest prediction model is nominated for a given new user satisfactory product recommendation (NUSPR) scenario. The upper and lower bounds of the prediction error of each interest prediction model are calculated to estimate the similarity between it and other models in predicting results. The weight voting algorithm selects the model with the highest consensus as the final prediction model. Accordingly, the NUSPR strategies are proposed according to the model predicted result. Relying on Yelp’s social commerce website, we verify that our CIPM can accurately predict the new users’ satisfaction level.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Networks 25(3):211–230

    Article  Google Scholar 

  2. Alghofaily B, Ding C (2020) MLP4ML: Machine learning service recommendation system using MLP. In: 2020 IEEE International Conference on Services Computing (SCC), pp 84–91

  3. Aral S (2011)Commentary-Identifying Social Influence: A comment on opinion leadership and social contagion in new product diffusion. Mark Sci 30(2):217–223

    Article  Google Scholar 

  4. Baskin TW, Tierney SC, Minami T, Wampold BE (2003) Establishing specificity in psychotherapy: A meta-analysis of structural equivalence of placebo controls. J Consulting Clin Psychol 71(6):973–979

    Article  Google Scholar 

  5. Bikhchandani S, Sharma S (2000) Herd behavior in financial markets: A review. IMF Working Papers 47(3):279–310

  6. Bikhchandani S, Hirshleifer D, Welch I (1998) Learning from the behavior of others: Conformity, fads, and informational cascades. J Econ Perspect 12(3):151–170

    Article  Google Scholar 

  7. Boratto L, Fenu G, Marras M (2021) Connecting user and item perspectives in popularity debiasing for collaborative recommendation. Inf Process Manag 58(1):102387

    Article  Google Scholar 

  8. Burt RS (1987) Social contagion and innovation cohesion versus structural equivalence. Am J Sociol 92(6):1287–1335

    Article  Google Scholar 

  9. Byun H, Kim CK (2019) When friends move: A deep learning-based approach for friendship prediction in mobility network (poster). In: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Service, pp 520–521

  10. Cheng WJ, Yin GS, Dong YX, Dong HB, Zhang WS (2016) Collaborative filtering recommendation on users’ interest sequences. PLoS ONE 11(5):1–17

    Google Scholar 

  11. Cruz AFT, Coronel AD (2020) Towards developing a content-based recommendation system for classical music. Inf Sci Appl 621:451–462

    Google Scholar 

  12. Deshpande M, Karypis G (2004)Item-based top-n recommendation algorithms. ACM Trans Inform Syst (TOIS) 22(1):143–177

    Article  Google Scholar 

  13. Falk A, Fischbacher U (2006) A theory of reciprocity. Games Econ Behav 54(2):293–315

    Article  MathSciNet  Google Scholar 

  14. Ghritlahre HK, Prasad RK (2018) Exegetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. J Environ Manage 223:566–575

    Article  Google Scholar 

  15. Gopi AP, Jyothi RNS, Narayana VL, Sandeep KS (2020) Classification of tweets data based on polarity using improved RBF kernel of SVM. Int J Inf Tech: 1–16. https://doi.org/10.1007/s41870-019-00409-4

  16. Gouldner AW (1960) The norm of reciprocity: A preliminary statement. Am Social Rev 25(2):161–178

    Article  Google Scholar 

  17. Greene WH (2007) Econometric analysis, 5th edn. Pearson Education, Delhi. (Chap. 3)

  18. He C, Parra D, Verbert K (2016) Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst Appl 56:9–27

    Article  Google Scholar 

  19. Hung LP (2005) A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Syst Appl 29(2):383–392

    Article  MathSciNet  Google Scholar 

  20. Jabakji A, Dag H (2016) Improving item-based recommendation accuracy with user’s preferences on Apache Mahout. In: Proceeding-2016 IEEE International Conference on Big Data. IEEE, pp 1742–1749

  21. Kavitha M, Subbaiah S (2020) Association rule mining using apriori algorithm for extracting product sales patterns in groceries. Int J Eng Res Technol 8(3):1–4

  22. Kim J, Lee D, Chung KY (2014) Item recommendation based on context-aware model for personalized u-healthcare service. Multimed Tools Appl 71(2):855–872

    Article  Google Scholar 

  23. Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311:88–90

    Article  MathSciNet  Google Scholar 

  24. Lai CH, Lee SJ, Huang HL (2018) A social recommendation method based on the integration of social relationship and product popularity. Int J Hum-Comput ST 121:42–57

    Article  Google Scholar 

  25. Lee DH, Brusilovsky P (2010) Social networks and interest similarity: the case of CiteULike. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp 151–155

  26. Lee Y, Won H, Shim J, Ahn H (2020) A hybrid collaborative filtering-based product recommender system using search keywords. J Intell Inform Syst 26(1):151–166

    Google Scholar 

  27. Lewis K, Gonzalez M, Kaufman J (2012) Social selection and peer influence in an online social network. Proc Natl Acad Sci 109(1):68–72

    Article  Google Scholar 

  28. Li J, Xu WT, Wan WB, Sun JD (2018) Movie recommendation based on bridging movie feature and user interest. J Comput Sci 26:128–134

    Article  Google Scholar 

  29. Ma L, Krishnan R, Montgomery AL (2015) Latent homophily or social influence? an empirical analysis of purchase within a social network. Manage Sci 61(2):454–473

    Article  Google Scholar 

  30. Ma X, Lei X, Zhao G (2018) Rating prediction by exploring user’s preference and sentiment. Multimed Tools Appl 77:6425–6444

    Article  Google Scholar 

  31. Mashal I, Alsaryrah O, Chung TY (2016) Testing and evaluating recommendation algorithms in internet of things. J Amb Intel Hum Comp 7(6):889–900

    Article  Google Scholar 

  32. Shuai Z, Yao L, Sun A, Yi T (2017) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 1(1):1–35

    Google Scholar 

  33. Steck H (2013) Evaluation of recommendations: rating-prediction and ranking. In: Proceedings of the 7th ACM conference on Recommender systems, pp 213–220

  34. Wang CL, Cao ZG, Shou F, Long T (2018) Wlrrs: a new recommendation system based on weighted linear regression models. Comput Electr Eng 66:40–47

    Article  Google Scholar 

  35. Xiao H, Wang L, Crespi N, Park S, Cuevas A (2015) Alike people, alike interests? inferring interest similarity in online social networks. Decis Support Syst 69(Jan):92–106

    Google Scholar 

  36. Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp 3203–3209

  37. Yadav MS, Valck KD, Thorsten HT, Hoffman DL, Spann M (2013) Social commerce: A contingency framework for assessing marketing potential. J Interact Mark 27(4):311–323

    Article  Google Scholar 

  38. Yang C, Wu L, Tan K, Yu C, Zhou Y, Tao Y, Song Y (2021) Online user review analysis for product evaluation and improvement. J Theor Appl Electron Commer Res 16(5):1598–1611

    Article  Google Scholar 

  39. Young PH (2009) Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. Am Econ Rev 99(5):1899–1924

    Article  Google Scholar 

  40. Zheng JX, Wang YJ (2018) Personalized recommendations based on sentimental interest community detection. Sci Program 2018:8503452. https://doi.org/10.1155/2018/8503452

  41. Zheng X, Zheng W, Yang Y (2019) Clustering based interest prediction in social networks. Multimed Tools Appl 78:32755–32774

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Chinese National Natural Science Foundation (No. 71871135).

Author information

Authors and Affiliations

  1. School of Management, Shanghai University, Shanghai, 200444, People’s Republic of China

    Shugang Li, Ru Wang & Hanyu Lu

  2. Department of automation, East China University of Science and Technology, 200237, Shanghai, People’s Republic of China

    Zhaoxu Yu

Authors
  1. Shugang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ru Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanyu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhaoxu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanyu Lu.

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

Li, S., Wang, R., Lu, H. et al. The recommendation of satisfactory product for new users in social commerce website. Multimed Tools Appl 81, 16219–16241 (2022). https://doi.org/10.1007/s11042-022-12491-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12491-1

Keywords

Search

Navigation