Skip to main content
Springer Nature Link
Log in

Research on diversity and accuracy of the recommendation system based on multi-objective optimization

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

As the information industry and the Internet develop rapidly, the use of big data enters people's vision and attracts attention. It makes the recommendation system come into being how to quickly extract the desired information from the excessive information. In the recommendation system, user-based collaborative filtering algorithm has become a research hotspot. Existing researches focus on improving collaborative filtering recommendation algorithm by using the kernel method, but still face the cold start problem, the diversity problem, the data sparsity problem, the concept drift problem and more others. To solve these problems, this paper proposes the user-based collaborative filtering based on kernel method and multi-objective optimization (MO-KUCF) which introduces kernel density estimation and multi-objective optimization. It can be increasing diversity of the recommendation systems, improving concept drift in dynamic data and the accuracy and diversity of the recommendation system. The dataset used in this article is the Netflix dataset. It analyzes the MO-KUCF algorithm with the user-based collaborative filtering (UCF) and user-based collaborative filtering based on kernel method (KUCF) by the mean absolute error (MAE). The MAE is compared with the internal user diversity \(I_{{\text{u}}}\) index, and the pre-processed data set is divided into the training set and the test set, which are provided to the recommendation system for recommendation and evaluation. The results show that the accuracy of MO-KUCF improves by 5.6%, and the diversity also increases with decreasing values. Combining multi-objective optimization techniques with kernel density estimation methods can improve the diversity of recommendation systems effectively and solve the concept drift problem to achieve the purpose of improving system accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Jiang S (2019) A survey of diversity in personalized recommendation systems. Softw Eng Appl 08(03):172–178. https://doi.org/10.12677/sea.2019.83021

    Article  Google Scholar 

  2. Yang Y, Xu Y, Wang E, Han J, Yu Z (2018) Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm. In: IEEE Transactions on Multimedia

  3. Duan M, Li K, Liao X, Li K (2017) A parallel multiclassification algorithm for big data using an extreme learning machine. IEEE Trans Neural Netw Learn Syst 29(6):2337–2351

    Article  MathSciNet  Google Scholar 

  4. Ren C, Ping Z, Hua Z, editors (2016) A new Collaborative Filtering technique to improve recommendation diversity. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC)

  5. Liu X, Su X, Ma J, Zhu Y, Tian H (2019) Information filtering based on eliminating redundant diffusion and compensating balance. Int J Mod Phys B 33(13):1950129. https://doi.org/10.1142/S0217979219501212

    Article  Google Scholar 

  6. Zhen L, Ke T, Xiao-Qing W, Shi-Hao T (2018) A learning framework for temporal recommendation without explicit iterative optimization. Appl Soft Comput 67:529–539. https://doi.org/10.1016/j.asoc.2018.03.024

    Article  Google Scholar 

  7. Lian D, Kai Z, Yong G, Cao L, Xing X (2018) GeoMF++: scalable location recommendation via joint geographical modeling and matrix factorization. Acm Trans Inf Syst 36(3):1–29

    Article  Google Scholar 

  8. Zhao X, Chen W, Yang F, Liu Z (2016) Improving diversity of user-based two-step recommendation algorithm with popularity normalization. Springer International Publishing, Cham

    Book  Google Scholar 

  9. Fernndez-Tobas I, Tomeo P, Cantador I, Noia TD, Sciascio ED. Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback. In: Proceedings of the 10th ACM Conference on Recommender Systems; Boston, Massachusetts, USA. 2959175: ACM; 2016. p. 119–22

  10. Zou J, Sheng J, Wang B, Li W (2017) A collaborative filtering recommendation method based on TagIEA expert degree model. Int J Comput Sci Eng 14(4):321

    Google Scholar 

  11. Liu X, Yu Y, Guo C, Sun Y, editors (2014) Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation. The 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014)

  12. Sahoo S (2018) Rapid frequent pattern growth and possibilistic fuzzy C-means algorithms for improving the user profiling personalized web page recommendation system. Int J Intell Eng Syst 11(2):237–245

    Google Scholar 

  13. Barjasteh I, Forsati R, Ross D et al (2016) Cold-start recommendation with provable guarantees: a decoupled approach. IEEE Trans Knowl Data Eng 28(6):1462–1474

    Article  Google Scholar 

  14. Javari A, Jalili M (2015) A probabilistic model to resolve diversity-accuracy challenge of recommendation systems. Knowl Inf Syst 44(3):609–627

    Article  Google Scholar 

  15. Fleder DM, Hosanagar K. Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM conference on Electronic commerce; San Diego, California, USA. 1250939: ACM; 2007, p. 192–9

  16. Wang P, Qian Q, Shang Z, Li J editors (2016) An recommendation algorithm based on weighted Slope one algorithm and user-based collaborative filtering. In: 2016 Chinese Control and Decision Conference (CCDC)

  17. Parasuraman D, Elumalai S (2017) Improving the accuracy of item recommendations in collaborative filtering using time-variant system. Electron Gov Int J 13(4). https://doi.org/10.1504/EG.2017.10004520

    Article  Google Scholar 

  18. Xu Z, Cheng C, Sugumaran V (2020) Big data analytics of crime prevention and control based on image processing upon cloud computing. J Surveill Secur Saf 1:16–33. https://doi.org/10.20517/jsss.2020.04

    Article  Google Scholar 

  19. Zhang Q, Wu D, Zhang G, Lu J, editors. Fuzzy user-interest drift detection based recommender systems. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); 2016.

  20. Jia Y, Zhang C, Lu Q, Peng W, editors. Users' brands preference based on SVD++ in recommender systems. In: 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA); 2014.

  21. Geng B, Jiao L, Gong M, Li L, Wu Y (2019) A two-step personalized location recommendation based on multi-objective immune algorithm. Inf Sci 475:161–181. https://doi.org/10.1016/j.ins.2018.09.068

    Article  Google Scholar 

  22. D. MSUAKYWM. Online bad data detection using kernel density estimation. In: IEEE Power Energy Soc Gen Meeting (PESGM); July; Denver, CO, USA IEEE; 2015. p. 1–5.

  23. Lobo JL, Ser JD, Bilbao MN, Perfecto C, Salcedo-Sanz S (2018) DRED: an evolutionary diversity generation method for concept drift adaptation in online learning environments. Appl Soft Comput 68:693–709. https://doi.org/10.1016/j.asoc.2017.10.004

    Article  Google Scholar 

  24. Lin X, Chen H, Pei C et al (2019) A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems. ACM 20–28

  25. Ruder S. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017.

  26. Zhao Z, Hong L, Wei L, et al. Recommending what video to watch next: A multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems. ACM, 2019: 43–51.

  27. Ma J, Zhao Z, Yi X et al (2018) Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM 1930–1939

  28. Logesh R, Subramaniyaswamy V, Malathi D et al (2020) Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Applic 32:2141–2164

    Article  Google Scholar 

  29. Geng B, Li L, Jiao L, Gong M, Cai Q, Yue Wu (2015) Nnia-rs: a multi-objective optimization based recommender system. Phys A 424:383–397

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under the Grant Numbers 61772127, 61472184 and 61872069, the National Natural Science and Technology Major Projects under Grant No. 2013ZX03002006, the Liaoning Province Science and Technology Projects under Grant No.2013217004, the Fundamental Research Funds for the Central Universities under Grant No.N151704002, Heilongjiang Bayi Agricultural University Support Program for San Heng San Zong under the Grant Numbers ZRCQC201907 and TDJH201803, Heilongjiang Bayi Agricultural University Research Startup Project XDB202004, Heilongjiang Postdoctoral Science Foundation Funded Project under Grant No.LBH-Z19217.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Tie-min Ma, Fu-cai Zhou & Shuang Wang

  2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing, 163319, China

    Tie-min Ma & Xue Wang

  3. Daqing Center of Inspection and Testing for Agricultural Products Ministry of Agriculture, Daqing, 163319, China

    Xue Wang

Authors
  1. Tie-min Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xue Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fu-cai Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shuang Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fu-cai Zhou.

Ethics declarations

Conflict of interests

There are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher's Note

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

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Tm., Wang, X., Zhou, Fc. et al. Research on diversity and accuracy of the recommendation system based on multi-objective optimization. Neural Comput & Applic 35, 5155–5163 (2023). https://doi.org/10.1007/s00521-020-05438-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05438-w

Keywords