Skip to main content
Log in

RETRACTED ARTICLE: Research on personalized recommendation algorithm based on user preference in mobile e-commerce

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

This article was retracted on 16 November 2022

This article has been updated

Abstract

With the development of Internet, the problem of information overload becomes more and more serious. The personalized recommendation technology can establish user files through the user’s behavior and other information, and automatically recommend the items that best match the user’s preferences, thus effectively reducing the information overload problem. Based on this, this paper studies the personalized recommendation algorithm based on user preferences in mobile e-commerce. In this paper, user preference model under UTA algorithm is constructed on the basis of user rating on multiple criteria of the project, and user preference clustering is used to improve the scalability problem of personalized recommendation. Finally, the simulation is conducted according to the proposed personalized recommendation algorithm based on user preference. The simulation data use the multi-criteria rating data from 6078 users of Yahoo! Movies website for 976 movies (including 62,156 rows of data). The experimental results show that the multi-criteria recommendation algorithm (MC-CF-dis), which uses user distance similarity, has the best effect, and the MAE and RMSE value of this algorithm is about 1.2 lower than that of the other three algorithms. Accuracy is 6–10% higher than other algorithms. Thus, using this personalized recommendation algorithm based on user preference can effectively improve the quality of recommendation.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Change history

References

  • Al Omoush KS, Al-Qirem RM, Al Hawatmah ZM (2018) The degree of e-business entrepreneurship and long-term sustainability: an institutional perspective. Inf Syst E-Bus Manag 16:29–56

    Article  Google Scholar 

  • Alvaradouribe J, Gómezoliva A, Barreraanimas AY et al (2018) HyRA: a hybrid recommendation algorithm focused on smart POI. Ceutí as a study scenario. Sensors 18(3):890

    Article  Google Scholar 

  • Araibi N, Ahmed EB, Abdessalem WKB (2016) \(\left( {{\mathcal{I}\mathcal{R}\mathcal{O}\mathcal{R}\mathcal{S}}} \right)\): intelligent recommendation of RSS feeds. Vietnam J Comput Sci 3(1):47–56

    Article  Google Scholar 

  • Chi HY, Chen CC, Cheng WH et al (2016) UbiShop: commercial item recommendation using visual part-based object representation. Multimed Tools Appl 75(23):16093–16115

    Article  Google Scholar 

  • Chu WT, Tsai YL (2017) A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web Internet Web Inf Syst 20(6):1–19

    Google Scholar 

  • Cremonesi P, Elahi M, Garzotto F (2017) User interface patterns in recommendation-empowered content intensive multimedia applications. Multimed Tools Appl 76(4):1–35

    Article  Google Scholar 

  • Escobar-Rodríguez T, Bonsón-Fernández R (2017) Analysing online purchase intention in Spain: fashion e-commerce. Inf Syst E Bus Manag 15(3):599–622

    Article  Google Scholar 

  • Gordillo A, Barra E, Quemada J (2017) A hybrid recommendation model for learning object repositories. IEEE Lat Am Trans 15(3):462–473

    Article  Google Scholar 

  • Ilias OP, Patrick M, Giannakos MN et al (2018) Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Inf Syst Bus Manag 16:479–491

    Article  Google Scholar 

  • Kaššák O, Kompan M, Bieliková M (2016) Personalized hybrid recommendation for group of users: top-N multimedia recommender. Inf Process Manag 52(3):459–477

    Article  Google Scholar 

  • Li ST, Pham TT, Hui CC et al (2016) Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews. IseB 14(1):71–99

    Article  Google Scholar 

  • Pessemier TD, Dhondt J, Martens L (2016) Hybrid group recommendations for a travel service. Multimed Tools Appl 76(2):1–25

    Google Scholar 

  • Vinamrata A, Mishra K (2015) E-business: an analytical study of online business transactions and its role in promotion of rural entrepreneurship in India. Int J Res Soc Sci 5(2):185–201

    Google Scholar 

  • Wei S, Zheng X, Chen D et al (2016) A hybrid approach for movie recommendation via tags and ratings. Electron Commer Res Appl 18(1):83–94

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan Chen.

Additional information

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10257-019-00401-2

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y. RETRACTED ARTICLE: Research on personalized recommendation algorithm based on user preference in mobile e-commerce. Inf Syst E-Bus Manage 18, 837–850 (2020). https://doi.org/10.1007/s10257-019-00401-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-019-00401-2

Keywords

Navigation