Skip to main content
SpringerLink
Log in

Forgetting techniques for stream-based matrix factorization in recommender systems

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users’ preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users’ preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.

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

Similar content being viewed by others

Notes

  1. http://www.movielens.org/.

  2. http://www.palcoprincipal.com.

  3. http://last.fm.

References

  1. Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Lucian P, Serge A, Phokion GK (eds) PODS. ACM, pp 1–16

  2. Berry MW, Dumais ST, O’Brien GW (1995) Using linear algebra for intelligent information retrieval. SIAM Review 37(4):573–595

    Article  MathSciNet  MATH  Google Scholar 

  3. Cao F, Ester M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. In: Joydeep G, Diane L, David BS, Jaideep S (eds) SDM. SIAM

  4. Celma O (2010) Music recommendation and discovery in the long tail. Springer, Berlin

    Book  Google Scholar 

  5. Chua FCT, Oentaryo RJ, Lim E-P (2013) Modeling temporal adoptions using dynamic matrix factorization. In: Hui X, George K, Bhavani MT, Diane JC, Xindong W (eds) ICDM, IEEE computer society, pp 91–100

  6. Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of ACM RecSys, RecSys ’10. ACM, pp 39–46

  7. Ding Y, Li X (2005) Time weight collaborative filtering. In: Otthein H, Hans-Jörg S, Norbert F, Abdur C, Wilfried T, (eds) CIKM. ACM, pp 485–492

  8. Domingos P, Hulten G (2001) Catching up with the data: research issues in mining data streams. In: DMKD

  9. Gama João (2012) A survey on learning from data streams: current and future trends. Prog Artif Intell 1(1):45–55

    Article  Google Scholar 

  10. Gama J, Sebastião R, Rodrigues PP (2009) Issues in evaluation of stream learning algorithms. In: KDD

  11. Halchenko YO, Hanke M (2012) Open is not enough. Let’s take the next step: an integrated, community-driven computing platform for neuroscience. Front Neuroinf

  12. Harper F Maxwell, Konstan Joseph A (2016) The movielens datasets: history and context. TiiS 5(4):19

    Google Scholar 

  13. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE international conference on data mining (ICDM 2008), December 15–19, 2008, Pisa, Italy. IEEE Computer Society, pp 263–272

  14. Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. ACM SIGKDD

  15. Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Doheon L, Mario S, Foster JP, Ramakrishnan S (eds) KDD. ACM, pp 97–106

  16. Karimi R, Freudenthaler C, Nanopoulos A, Schmidt-Thieme L (2011) Towards optimal active learning for matrix factorization in recommender systems. In: ICTAI. IEEE, pp 1069–1076

  17. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  18. Koren Y (2009) Collaborative filtering with temporal dynamics. In: KDD

  19. Koychev I (2000) Gradual forgetting for adaptation to concept drift. In: ECAI 2000 workshop on current issues in spatio-temporal reasoning, Berlin, Germany, pp 101–106

  20. Koychev I, Schwab I (2000) Adapting to drifting user’s interests. In: ECML 2000

  21. Li Xue, Barajas Jorge M, Ding Yi (2007) Collaborative filtering on streaming data with interest-drifting. Intell Data Anal 11(1):75–87

    Google Scholar 

  22. Ling G, Yang H, King I, Lyu MR (2012) Online learning for collaborative filtering. In: International joint conference on neural networks. IEEE, pp 1–8

  23. Liu NN, Zhao M, Xiang EW, Yang Q (2010) Online evolutionary collaborative filtering. In: Proceedings of the ACM RecSys

  24. Massa P, Avesani P (2006) Trust-aware bootstrapping of recommender systems. In: ECAI workshop on recommender systems. Citeseer, pp 29–33

  25. Matuszyk P, Spiliopoulou M (2014) Selective forgetting for incremental matrix factorization in recommender systems. In: Discovery science, volume 8777 of LNCS. Springer, pp 204–215

  26. Matuszyk P, Spiliopoulou M (2015) Semi-supervised learning for stream recommender systems. In: Nathalie J, Stan M (eds) Discovery science, volume 9356 of LNCS. Springer, pp 131–145

  27. Matuszyk P, Vinagre J, Spiliopoulou M, Jorge AM, Gama J (2015) Forgetting methods for incremental matrix factorization in recommender systems. In: Proceedings of the ACM SAC, SAC ’15, New York, NY, USA. ACM, pp 947–953

  28. Miranda C, Jorge AM (2008) Incremental collaborative filtering for binary ratings. In: Web intelligence conference proceedings. IEEE Computer Society, pp 389–392

  29. Nasraoui O, Cerwinske J, Rojas C, González F (2007) Performance of recommendation systems in dynamic streaming environments. In: SDM, SIAM

  30. Nasraoui O, Uribe CC, Coronel CR, González FA (2003) TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model. In: Proceedings of the IEEE ICDM 2003, pp 235–242

  31. Papagelis M, Rousidis I, Plexousakis D, Theoharopoulos E (2005) Incremental collaborative filtering for highly-scalable recommendation algorithms. In: Proceedings of the ISMIS 2005

  32. Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of the KDD cup workshop at SIGKDD’07

  33. Sarwar BM, Karypis G, Konstan JA, Riedl JT (2000) Application of dimensionality reduction in recommender system—a case study. In: In ACM WEBKDD workshop

  34. Shaffer JP (1995) Multiple hypothesis testing. Ann Rev Psychol 46(1):561–584

    Article  Google Scholar 

  35. Sun John Z, Parthasarathy Dhruv, Varshney Kush R (2014) Collaborative Kalman filtering for dynamic matrix factorization. IEEE Trans Signal Process 62(14):3499–3509

    Article  MathSciNet  Google Scholar 

  36. Takács G, Pilászy I, Németh B, Tikk D (2009) Scalable collaborative filtering approaches for large recommender systems. J Mach Learn Res 10:623–656

    Google Scholar 

  37. Joáo V, Jorge AM (2012) Forgetting mechanisms for scalable collaborative filtering. J Braz Comput Soc 18(4):271–282

    Article  Google Scholar 

  38. Vinagre J, Jorge AM, Gama J (2014) Fast incremental matrix factorization for recommendation with positive-only feedback. In: UMAP, pp 459–470

Download references

Acknowledgements

We would like to thank to the Institute of Psychology II at the University of Magdeburg for making their computational cluster available for our experiments. This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme, and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project POCI-01-0145-FEDER-006961.

Author information

Authors and Affiliations

  1. Knowledge Management and Discovery Lab, Otto von Guericke University, Magdeburg, Germany

    Pawel Matuszyk & Myra Spiliopoulou

  2. LIAAD - INESC TEC, FCUP - Universidade do Porto, Porto, Portugal

    João Vinagre & Alípio Mário Jorge

  3. LIAAD - INESC TEC, FEP - Universidade do Porto, Porto, Portugal

    João Gama

Authors
  1. Pawel Matuszyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Vinagre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Myra Spiliopoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alípio Mário Jorge
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. João Gama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pawel Matuszyk.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matuszyk, P., Vinagre, J., Spiliopoulou, M. et al. Forgetting techniques for stream-based matrix factorization in recommender systems. Knowl Inf Syst 55, 275–304 (2018). https://doi.org/10.1007/s10115-017-1091-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-017-1091-8

Keywords

Search

Navigation