Skip to main content
SpringerLink
Log in

A collaborative filtering recommendation system with dynamic time decay

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people’s preferences usually vary with time; the traditional CF could not properly reveal the change in users’ interests. In this paper, we propose a novel CF-based recommendation, dynamic decay collaborative filtering (DDCF), which captures the preference variations of users and includes the concept of dynamic time decay. We extend the idea of human brain memory to specify the level of a user’s interests (i.e., instantaneous, short-term, or long-term). According to different interest levels, DDCF dynamically tunes the decay function based on users’ behaviors. The experimental results show that DDCF with the integration of the dynamic decay concept performs better than traditional CF. In addition, we conduct experiments on real-world datasets to demonstrate the practicability of the proposed DDCF.

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. Atkinson R, Shiffrin R (1968) Human memory: a proposed system and its control processes. Psychol Learn Motiv 2:89–195

    Article  Google Scholar 

  2. Ba Q, Li X, Bai Z (2013) Clustering collaborative filtering recommendation system based on SVD algorithm. In: 2013 4th IEEE International Conference on Software Engineering and Service Science (ICSESS’ 13), pp 963–967

  3. Baddeley A (1997) Human memory: theory and practice. Psychology Press, England

    Google Scholar 

  4. Cai Y, Leung H, Li Q, Min H, Tang J, Li J (2014) Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26(3):766–779

    Article  Google Scholar 

  5. Chen G, Wang F, Zhang C (2009) Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Inf Process Manag 45(3):368–379

    Article  Google Scholar 

  6. Chen Y, Zhu W, Peng W, Lee W, Lee S (2014) CIM: community-based influence maximization in social networks. ACM Trans Intell Syst Technol (TIST) 5(2):1–31

    Article  Google Scholar 

  7. Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM’05), pp 485–492

  8. Gong S, Cheng G (2008) Mining user interest change for improving collaborative filtering. In: Proceeding of Second Intelligent Information Technology Application (IITA'08), pp 24–27

  9. Gupta J, Gadge J (2015) Performance analysis of recommendation system based on collaborative filtering and demographics. In: 2015 international conference on Communication, Information & Computing Technology (ICCICT’15)

  10. Hofmann T (2003) Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’03), pp 259–266

  11. https://movielens.org

  12. Jiang S, Qian X, Shen J, Fu Y, Mei T (2015) Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans Multimed 17(6):907–918

    Google Scholar 

  13. Kabbur S, Karypis G (2014) Nlmf: nonlinear matrix factorization methods for top-n recommender systems. In: 2014 IEEE International Conference on Data Mining Workshop (ICDMW’14), pp 167–174

  14. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’08), pp 426–434

  15. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):42–49

    Article  Google Scholar 

  16. Kumar A, Gupta S, Singh S, Shukla K (2015) Comparison of various metrics used in collaborative filtering for recommendation system,. In: 2015 Eighth International Conference on Contemporary Computing (IC3 2015)

  17. Lee T, Park Y, Park Y (2008) A time-based approach to effective recommender systems using implicit feedback. Expert Syst Appl 34:3055–3062

    Article  Google Scholar 

  18. Li D, Cao P, Guo Y, Lei M (2013) Time weight update model based on the memory principle in collaborative filtering. J Comput 8(11):2763–2767

    Google Scholar 

  19. Ma Z, Yang Y, Wang F, Li C, Li L (2014) The SOM based improved K-means clustering collaborative filtering algorithm in TV recommendation system. In: 2014 Second International Conference on Advanced Cloud and Big Data (CBD’14), pp 288–295

  20. Melville P, Mooney R, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002)

  21. Nie U, Liu Y, Yu X (2014) Weighted aspect-based collaborative filtering. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’14), pp 1071–1074

  22. Niemann K, Wolpers M (2013) A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’ 13), pp 955–963

  23. Pirasteh P, Hwang D, Jung J (2015) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl Based Syst 83:51–57

    Article  Google Scholar 

  24. Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work (CSCW’94), pp 175–186

  25. Richards D, Morgan T, Wilson R, Schwebach V, Young G (1993) Good times, bad times, and the diversionary use of force: a tale of some not-so-free agents. J Confl Resolut 37(3):504–535

    Article  Google Scholar 

  26. Renaud-Deputter S, Xiong T, Wang S (2013) Combining collaborative filtering and clustering for implicit recommender system. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA’13), pp 748–755

  27. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW10), pp 285–295

  28. Wang J, Yin J (2013) Combining user-based and item-based collaborative filtering techniques to improve recommendation diversity. In: The IEEE 6th International Conference on Biomedical Engineering and Informatics (BMEI’13), pp 661–665

  29. Wixted J, Ebbesen E (1997) Genuine power curves in forgetting: a quantitative analysis of individual subject forgetting functions. Mem Cognit 25(5):731–739

    Article  Google Scholar 

  30. Wu D, Yuan Z, Yu K, Pan H (2012) Temporal social tagging based collaborative filtering recommender for digital library. In: International Conference on Asian Digital Libraries (ICADL’2012), pp 199–208

  31. Zhang Z, Liu Y, Jin Z, Zhang R (2018) A dynamic trust based two-layer neighbor selection scheme towards online recommender systems. Neurocomputing 285:94–103

    Article  Google Scholar 

  32. Zhao Z, Wang C, Wan Y, Huang Z, Lai J (2015) Pipeline item-based collaborative filtering based on MapReduce. In: The IEEE Fifth International Conference on Big Data and cloud computing (BDCloud’15), pp 9–14

  33. Zhou W, Zhang G, Zhao X, Li M, Hu X, Xue Y (2015) A collaborative filtering algorithm based on biclustering. In: Proceedings of the 2015 International Conference on Machine Learning and Cybernetics (ICMLC 2015), pp 533–538

Download references

Funding

This study was supported by Ministry of Science and Technology, Taiwan (Grant numbers MOST 108-2221-E-008-063-MY3, MOST 108-2221-E-032-036-).

Author information

Authors and Affiliations

  1. Department of Information Management, National Central University, Taoyüan City, 32001, Taiwan

    Yi-Cheng Chen

  2. Department of Innovative Information and Technology, Tamkang University, Yilan County, 26247, Taiwan

    Lin Hui

  3. Computer Science and Information Engineering, National Central University, Taoyüan City, 32001, Taiwan

    Tipajin Thaipisutikul

Authors
  1. Yi-Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tipajin Thaipisutikul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Hui.

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, YC., Hui, L. & Thaipisutikul, T. A collaborative filtering recommendation system with dynamic time decay. J Supercomput 77, 244–262 (2021). https://doi.org/10.1007/s11227-020-03266-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03266-2

Keywords

Search

Navigation