Skip to main content
SpringerLink
Log in

Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

With the rapid development of internet economy, personal recommender system plays an increasingly important role in e-commerce. In order to improve the quality of recommendation, a variety of scholars and engineers devoted themselves in developing the recommendation algorithms. Traditional collaborative filtering algorithms are only dependent on rating information or attribute information. Most of them were considered in perspective of a single-layer network, which destroyed the original hierarchy of data and resulted in sparse matrix and poor timeliness. In order to address these problems and improve the accuracy of recommendation, dynamic clustering collaborative filtering recommendation algorithm based on double-layer network is put forward in this paper. Firstly, attribute information of users and items are respectively used to construct the user layer network and the item layer network. Secondly, new hierarchical clustering method is further presented, which separates users into different communities according to dynamic evolutionary clustering. Finally, score prediction and top-N recommendation lists are obtained by similarity between users in each community. Extensive experiments are conducted with three real datasets, and the effectiveness of our algorithm is verified by different metrics.

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

Similar content being viewed by others

Notes

  1. https://grouplens.org/datasets/movielens/100k.

  2. https://archive.ics.uci.edu/ml/datasets/Restaurant%20&%20consumer%20data.

  3. http://www.public.asu.edu/jtang20/datasetcode/truststudy.htm.

References

  1. Kuo YT, Chen PY, Lin HC (2020) A spatiotemporal content-based CU size decision algorithm for HEVC. IEEE Trans Broadcast 66(1):100–112

    Article  Google Scholar 

  2. Messina P, Dominguez V, Parra D (2019) Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features. User Model User Adapt Interact 29:251–290

    Article  Google Scholar 

  3. Polatidis N, Georgiadis CK (2017) A dynamic multi-level collaborative filtering method for improved recommendations. Comput Stand Interfaces 51:14–21

    Article  Google Scholar 

  4. Ya-Wen D, Ke L (2019) A fusion collaborative filtering recommendation algorithm based on user and item’s similarity. Comput Inf Technol 27(1):6–10

    Google Scholar 

  5. Bag S, Kumar SK, Tiwari MK (2019) An efficient recommendation generation using relevant Jaccard similarity. Inf Sci 483:53–64

    Article  Google Scholar 

  6. Valcarce D, Parapar J (2016) Item-based relevance modelling of recommendations for getting rid of long tail products. Knowl Based Syst 103:41–51

    Article  Google Scholar 

  7. Shi HY, Chen L, Xu ZX et al (2019) Personalized location recommendation using mobile phone usage information. Appl Intell 49:3694–3707

    Article  Google Scholar 

  8. Liu HT, Wang Y, Peng QY et al (2020) Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374:77–85

    Article  Google Scholar 

  9. Cai XJ, Hu ZM, Zhao P et al (2020) A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst Appl 159:116648

    Article  Google Scholar 

  10. Zhao XY, Xia L, Yin DW, et al (2019) Model-based reinforcement learning for whole-chain recommendations. In: The 13th ACM international conference on web search and data mining, pp 4–8

  11. Cai X, Hu Z, Chen J (2020) A many-objective optimization recommendation algorithm based on knowledge mining. Inf Sci 537:148–161

    Article  MathSciNet  Google Scholar 

  12. Chen C, Zhang M, Zhang Y et al (2020) Efficient neural matrix factorization without sampling for recommendation. ACM Trans Inf Syst 38(2):1–28

    Google Scholar 

  13. Silveira T, Zhang M, Lin X et al (2019) How good your recommender system is? A survey on evaluations in recommendation. Int J Mach Learn Cybern 10(5):813–831

    Article  Google Scholar 

  14. Zhang YF, Chen X (2020) Explainable recommendation: a survey and new perspectives. Found Trends Inf Retr 14(1):1–101

    Article  MathSciNet  Google Scholar 

  15. Ebbinghaus H, Ruger HA, Bussenius CE (2013) Memory: a contribution to experimental psychology. Ann Neurosci 20(4):155–156

    Article  Google Scholar 

  16. Xue YL, Xu LY, Yu J et al (2016) Memory-forgetting curve based on virtual and real spaces for commercial recommendation. In: Proceedings of the 9th EAI international conference on mobile multimedia communications, pp 205–209

  17. Chen JR, Wei LD, Uliji et al (2018) Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation. Chaos Solitons Fractals 114:8–18

    Article  MathSciNet  Google Scholar 

  18. Liu TC, Liao JX, Wu ZG et al (2020) Exploiting geographical-temporal awareness attention for next point-of-interest recommendation. Neurocomputing 400:227–237

    Article  Google Scholar 

  19. Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Measurement 91:134–139

    Article  Google Scholar 

  20. Kaur M, Batra S (2017) A novel trust mechanism for collaborative recommendation systems. Comput Netw Sustain Lect Notes Netw Syst 12:343–351

    Google Scholar 

  21. Chen JR, Uliji A (2018) Evolutionary heterogeneous clustering for rating prediction based on user collaborative filtering. Swarm Evol Comput 38:34–41

    Google Scholar 

  22. Jiang WJ, Chen JH, Jiang YR et al (2019) A new time-aware collaborative filtering intelligent recommendation system. Compu Mater Continua 58(2):849–859

    Article  MathSciNet  Google Scholar 

  23. Yin YY, Chen L, Xu YS et al (2019) QoS prediction for service recommendation with deep feature learning in edge computing environment. Mobile Netw Appl 25:391–401

    Article  Google Scholar 

  24. Bi JW, Liu Y, Fan ZP (2020) A deep neural networks based recommendation algorithm using user and item basic data. Int J Mach Learn Cybern 11:763–777

    Article  Google Scholar 

  25. Wang XH, Peng ZH, Wang SZ et al (2020) CDLFM: cross-domain recommendation for cold-start users via latent feature mapping. Knowl Inf Syst 62(5):1723–1750

    Article  Google Scholar 

  26. Moradi P, Ahmadian S, Akhlaghian F (2015) An effective trust-based recommendation method using a novel graph clustering algorithm. Phys A 436:462–481

    Article  Google Scholar 

  27. Ren J, Long J, Xu Z (2019) Financial news recommendation based on graph embeddings. Decis Support Syst 125:113115

    Article  Google Scholar 

  28. Ding Y, Li X (2005) Time weight collaborative filtering. In: The 14th ACM international conference on information & knowledge management, vol 1, pp 485–492

  29. Leon D, Albert D, Jordi D et al (2005) Comparing community structure identification. J Stat Mech 09:09008

    MATH  Google Scholar 

  30. Wu JS, Jiao LC, Jin C et al (2012) Overlapping community detection via network dynamics. Phys Rev E 85(2):016115

    Article  Google Scholar 

  31. Wu JS, Zhang L, Li Y et al (2016) Partition signed social networks via clustering dynamics. Phys A 443:568–582

    Article  MathSciNet  Google Scholar 

  32. Chen JR, Wang H, Wang L et al (2016) A dynamic evolutionary clustering perspective: community detection in signed networks by reconstructing neighbor sets. Phys A 447:482–492

    Article  MathSciNet  Google Scholar 

  33. Maia DMN, Oliveira JEMD et al (2017) Community detection in complex networks via adapted Kuramoto dynamics. Commun Nonlinear Sci Numer Simul 53:130–141

    Article  MathSciNet  Google Scholar 

  34. Chen JR, Wang B, Uliji A et al (2019) Personal recommender system based on user interest community in social network model. Phys A 526:120961

    Article  MathSciNet  Google Scholar 

  35. Gerald T (2004) Ordinary differential equations and dynamical systems. Atlantis Stud Differ Equ 140(3):189–194

    Google Scholar 

  36. Srikanth T, Shashi M (2015) A new similarity measure for user-based collaborative filtering in recommender systems. Int J Comput Technol 14(9):6118

    Article  Google Scholar 

  37. Zheng CC, Li L (2014) Research on method of similarity measurement in collaborative filter algorithm. Comput Eng Appl 50(8):147–149

    Google Scholar 

  38. Upendra S, Pattie M (1995) Social information filtering: algorithm for automating ‘Word of Mouth’ 110(1):210–217

  39. Paolo C, Yehuda K, Roberto T (2010) Performance of recommender algorithms on top-n recommendation tasks. In: ACM conference on recommender systems, pp 39–46

  40. Shang FH, Liu YY, James C (2018) Fuzzy double trace norm minimization for recommendation systems. IEEE Trans Fuzzy Syst 26(4):2039–2049

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported through National Natural Science Foundation of China (No. 71561020, 61503203, 61702317, 61771297); Fundamental Research Funds for the Central Universities (No. GK201802013).

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710119, China

    Jianrui Chen

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Jianrui Chen & Zhihui Wang

  3. College of Science, Inner Mongolia University of Technology, Hohhot, 010051, China

    Bo Wang

  4. College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China

    Zhiping Ouyang

Authors
  1. Jianrui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiping Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianrui Chen.

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

Chen, J., Wang, B., Ouyang, Z. et al. Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network. Int. J. Mach. Learn. & Cyber. 12, 1097–1113 (2021). https://doi.org/10.1007/s13042-020-01223-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01223-2

Keywords

Search

Navigation