Skip to main content
SpringerLink
Log in

A group interest-based collaborative filtering algorithm for multimedia information

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a collaborative filtering algorithm based on user group interest. A novel co-clustering method (BalClust) and various weighted non-negative matrix factorization algorithms are used in the proposed method. The BalClust method is used to divide the raw rating matrix into clusters, which are smaller than the original matrix. Then, the balance factor is introduced to consider the user weight and the item-based CF (collaborative filtering). To predict the rating of the unknown items in the cluster, the non-negative matrix factorization algorithm was used. The proposed method achieves higher predicting accuracy and efficiency on low dimensional and homogeneous sub-matrices, and the method also reduces the computational complexity by combining the user and item-based CF. Based on the proposed method, this paper proposed an incremental learning method to ensure data accuracy and timeliness to overcome the problem brought by data updates. The experimental results show the proposed methods outperformed traditional CF algorithms, and the completion time is reduced.

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

Similar content being viewed by others

References

  1. Abrate F, Bona B, Indri M (2013) Multirobot Localization in Highly Symmetrical Environments [J]. J Intell Robot Syst 71(4):403–421

    Article  Google Scholar 

  2. Agarwal D, Merugu S (2007) Predictive discrete latent factor models for large scale dyadic data [C]. In: Berkhin P, ed. Proc. of the SIGKDD. New York: ACM Press, 26–35

  3. Akinshina A, Jambon-Puillet E, Warren PB (2013) Self-consistent field theory for the interactions between keratin intermediate filaments [J]. BMC Biophys 6(9):6–12

    Google Scholar 

  4. Banerjee A, Dhillon I, Ghosh J, Merugu S, Modha DS (2007) A generalized maximum entropy approach to Bregman co-clustering and matrix approximation [J]. J Mach Learn Res 8(8):1919–1986

    MathSciNet  MATH  Google Scholar 

  5. Bobadilla J, Ortega F, Hernando A (2013) A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors’ algorithm [J]. Knowl-Based Syst 51(6):27–34

    Article  Google Scholar 

  6. Brooks CH, Montanez N (2006) Improved annotation of the blogosphere via auto-tagging and hierarchical clustering [C]. Proceedings of the 15th International Conference on World Wide Web. Edinburgh, UK, 625–632

  7. Brzozowski MJ, Romero DM (2011) Who should I fellow? Recommending people in directed social networks [C]. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Barcelona, Spain 458–461

  8. Cheng YZ, Church GM (2000) Biclustering of expression data [C]. In: Bourne PE, ed. Proc. of the 8th Int’l Conf. on Intelligent Systems for Molecular Biology. La Jolla: AAAI Press, 93–103

  9. Cheng G, Wang F, Zhang CS (2009) Collaborative filtering using orthogonal nonnegative matrix tri-factorization [J]. Inf Process Manag 45(3):368–379

    Article  Google Scholar 

  10. Chirici G, Scotti R, Montaghi A (2013) Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery [J]. Int J Appl Earth Obs Geoinf 25:87–97

    Article  Google Scholar 

  11. Dhillon SI (2001) Co-Clustering documents and words using bipartite spectral graph partitioning [C]. In: Lee D, ed. Proc. of the 7th ACM SIGKDD. New York: ACM Press, 269–274

  12. Dhillon IS, Mallela S, Modha DS (2003) Information-Theoretic co-clustering [C]. In: Getoor L, ed. Proc. of the 9th ACM SIGKDD. New York: ACM Press, 89–98

  13. Dinuzzo F (2013) Learning output kernels for multi-task problems [J]. Neurocomputing 118(2):119–126

    Article  Google Scholar 

  14. George T, Merugu S (2005) A scalable collaborative filtering framework based on co-clustering [C]. In: Raghavan V, ed. Proc. of the 5th IEEE Int’l Conf. on Data Mining. Washington: IEEE Computer Society Press, 625–628

  15. Hai-ling X, Xiao W, Xiao-dong L et al (2009) Comparison Study of Internet Recommendation System [J]. J Softw 20(2):350–362

    Article  Google Scholar 

  16. Badaro G, Hajj H, El-Hajj W, Nachman L (2013) A hybrid approach with collaborative filtering for recommender systems. 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, pp 349–354. https://doi.org/10.1109/IWCMC.2013.6583584

  17. Hindle A, Ernst NA, Godfrey MW (2013) Automated topic naming Supporting cross-project analysis of software maintenance activities [J]. Empir Softw Eng 18(6):1125–1155

    Article  Google Scholar 

  18. Kaklauskas A, Zavadskas EK, Seniut M (2013) Recommender System to Analyze Student's Academic Performance [J]. Expert Syst Appl 40(15):6150–6165

    Article  Google Scholar 

  19. Kim H-L, Breslin JG, Decker S, Kim H-G (2011) Mining and representing user interests: The case of tagging practices [J]. IEEE Trans Syst Man Cybern Syst Hum 11(4):683–692

    Article  Google Scholar 

  20. Liu J, Zhang J, Gao Y (2012) Enhancing Spectral Unmixing by Local Neighborhood Weights [J]. IEEE J Sel Top Appl Earth Obs Remote Sens 5(5):1545–1552

    Article  Google Scholar 

  21. Long B, Zhang ZF, Yu PS (2005) Co-Clustering by block value decomposition [C]. In: Grossman R, ed. Proc. of the SIGKDD 2005. New York: ACM Press, 635–640

  22. Pan J-Y, Zhang J-S (2011) Relationship Matrix Nonnegative Decomposition for Clustering [J]. Math Probl Eng 3:1–15

    Article  MathSciNet  Google Scholar 

  23. Shafiei MM, Milios EE (2006) Latent Dirichlet co-clustering [C]. In: Liu JM (ed) Proc. of the 6th Int’l Conf. on Data Mining. IEEE Computer Society Press, Washington, pp 542–551

    Google Scholar 

  24. Shan HH, Banerjee A (2008) Bayesian co-clustering [C]. In: Altman R, ed. Proc. of the ICDM. Washington: IEEE Computer Society Press, 2008. 530–539

  25. Stadnyki KR (1992) Modeling user’ interesting in information filters [J]. Commun ACM 35(12):49–50

    Article  Google Scholar 

  26. Wei C, Khoury R, Fong S (2013) Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm [J]. Inf Syst Front 15(4):533–551

    Article  Google Scholar 

  27. Wu H, Zhang D, Wang YJ, Cheng X (2008) Incremental probabilistic latent semantic analysis for automatic question recommendation [C]. In: Pu P, ed. Proc. of the Recommender System 2008. New York: ACM Press, 99–106

  28. Xiao Y-H, Zhu Z-F, Zhao Y (2013) Class-Driven Non-Negative Matrix Factorization for Image Representation [J]. J Comput Sci Technol 28(5):751–761

    Article  MathSciNet  MATH  Google Scholar 

  29. Xiaogang L, Ge Y, Da-ling W et al (2008) Latent Concept Extraction and Text Clustering Based on Information Theory[J]. Journal of Software 19(9):2276–2284

    Article  Google Scholar 

  30. Zheng Z, Liu J, Wang P et al (2014) Time-Weighted Uncertain Nearest Neighbor Collaborative Filtering Algorithm[J]. Comput Sci 12(8):7–12

    Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers and the editors for the valuable feedback on earlier versions of this paper. This paper is supported by the National Statistical Science Research Project of China, under grant number 2015LY43.

Author information

Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Tao Wang

  2. School of Science, Hubei University for Nationalities, Enshi, Sichuan, China

    Tao Wang

Authors
  1. Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, T. A group interest-based collaborative filtering algorithm for multimedia information. Multimed Tools Appl 77, 4401–4415 (2018). https://doi.org/10.1007/s11042-017-5516-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5516-x

Keywords

Search

Navigation