Advertisement

Recommendation algorithm based on improved spectral clustering and transfer learning

  • Xiang Li
  • Zhijian Wang
  • Ronglin Hu
  • Quanyin Zhu
  • Liuyang Wang
Theoretical Advances
  • 313 Downloads

Abstract

Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a recommendation algorithm based on improved spectral clustering and transfer learning (RAISCTL) to improve the forecasting accuracy and generalization ability of recommender system. RAISCTL firstly improves the spectral clustering by using the eigenvalue differences and orthogonal eigenvectors and realizes the automatic determination of cluster numbers. In addition, the improved spectral clustering algorithm is used to cluster the two dimensions of the users and items of the original rating matrix. Then, RAISCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RAISCTL makes rating forecasting and recommendations based on the sharing group rating matrix and transfer learning. The simulation results show that RAISCTL can effectively improve the recommendation accuracy and generalization ability compared with other 8 conventional CF approaches.

Keywords

Spectral clustering Recommendation algorithm Recommender systems Collaborative filtering Transfer learning 

Notes

Acknowledgements

This work is supported by University Science Research Project of Jiangsu Province (15KJB520004), Science and Technology Projects of Huaian (HAC201601), Science and Technology Project of Jiangsu Province (BE2015127), Jiangsu Government Scholarship for Overseas Studies, Jiangsu QingLan Project and Top-notch Academic Programs Project of Jiangsu Higher Education Institutions.

References

  1. 1.
    Lai HP, Visani M, Boucher A (2012) An experimental comparison of clustering methods for content-based indexing of large image databases. Pattern Anal Appl 15(4):345–366MathSciNetCrossRefGoogle Scholar
  2. 2.
    Xiong G, Zhu FH, Dong XS (2016) Semantics-aware content-based recommender systems: design and architecture guidelines. Neurocomputing 254(SI):79–85Google Scholar
  3. 3.
    Tarus JK, Niu ZD, Yousif A (2017) A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener Comput Syst Int J Esci 72:37–48CrossRefGoogle Scholar
  4. 4.
    Colombo-Mendoza LO, Valencia-Garcia R, Rodriguez-Gonzalez A (2015) RecomMetz: a context-aware knowledge-based mobile recommender system for movie showtimes. Expert Syst Appl 42(3):1202–1222CrossRefGoogle Scholar
  5. 5.
    Yang B, Lei Y, Liu JM (2017) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633–1647CrossRefGoogle Scholar
  6. 6.
    Aghdam MH, Analoui M, Kabiri P (2017) Collaborative filtering using non-negative matrix factorisation. J Inf Sci 43(4):567–579CrossRefGoogle Scholar
  7. 7.
    Gordillo A, Barra E, Quemada J (2017) A hybrid recommendation model for learning object repositories. IEEE Lat Am Trans 15(3):462–473CrossRefGoogle Scholar
  8. 8.
    Kassak O, Kompan M, Bielikova M (2016) Personalized hybrid recommendation for group of users: Top-N multimedia recommender. Inf Process Manag 52(3):459–477CrossRefGoogle Scholar
  9. 9.
    Liu YN, Wang YW, Feng LZ (2016) Term frequency combined hybrid feature selection method for spam filtering. Pattern Anal Appl 19(2):369–383MathSciNetCrossRefGoogle Scholar
  10. 10.
    Xiao MB, Zheng XW (2015) Collaborative filtering algorithm with stepwise prediction. Appl Res Comput 32(11):3256–3272Google Scholar
  11. 11.
    Alexandridis G, Siolas G, Stafylopatis A (2017) Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models. Data Min Knowl Discov 31(4):1031–1059MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hamidreza K, Kourosh K (2017) A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst Appl 83:30–39CrossRefGoogle Scholar
  13. 13.
    Zhao HX, Wang XH, Yang JP (2011) Mixed collaborative recommendation algorithm based on factor analysis of user and item. J Comput Appl 31(5):1382–1386MATHGoogle Scholar
  14. 14.
    Yu J, Yang XK, Gao F (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 99:1–11Google Scholar
  15. 15.
    Yu J, Rui Y, Tang YY (2014) High-order distance based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442CrossRefGoogle Scholar
  16. 16.
    Yu J, Tao DC, Wang M (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779CrossRefGoogle Scholar
  17. 17.
    Yu J, Hong RC, Wang M (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512–3519CrossRefMATHGoogle Scholar
  18. 18.
    Meng XF, Ci X (2013) Big data management: concepts, techniques and challenges. J Comput Res Dev 50(1):146–169Google Scholar
  19. 19.
    Zhao S, Cao Q, Chen J (2016) A multi-atl method for transfer learning across multiple domains with arbitrarily different distribution. Knowl Based Syst 94:60–69CrossRefGoogle Scholar
  20. 20.
    Saha B, Gupta S, Dinh P (2016) Multiple task transfer learning with small sample sizes. Knowl Inf Syst 46(2):315–342CrossRefGoogle Scholar
  21. 21.
    Yan H, Qimin P, Hu X (2015) Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering. IEEE Trans Serv Comput 8(5):782–794CrossRefGoogle Scholar
  22. 22.
    Luo X, Zhou MC, Leung H et al (2016) An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering. IEEE Trans Autom Sci Eng 13(1):333–343CrossRefGoogle Scholar
  23. 23.
    Saya Y, Yasunari Y, Chikoto K (2013) Music recommendation hybrid system for improving recognition ability using collaborative filtering and impression words. Artif Life Robot 18(1):109–116Google Scholar
  24. 24.
    Yang Y, Ma Z, Yang Y (2015) Multitask spectral clustering by exploring intertask correlation. IEEE Trans Cybern 45(5):1083–1094CrossRefGoogle Scholar
  25. 25.
    Marina AO, Zanoni D, Siome G (2016) Manifold learning and spectral clustering for image phylogeny forests. IEEE Trans Inf Forensics Secur 11(1):5–18CrossRefGoogle Scholar
  26. 26.
    Sayyed BF, Mohammad RM, Ali AA (2015) Clustering multispectral images using spatial–spectral information. IEEE Geosci Remote Sens Lett 12(7):1521–1525CrossRefGoogle Scholar
  27. 27.
    Budianto T, Henry J, Hock SS (2015) Spectral caustic rendering of a homogeneous caustic object based on wavelength clustering and eye sensitivity. Vis Comput 31(3):365–370CrossRefGoogle Scholar
  28. 28.
    Siamak M, Carlos A, Raghvendra M (2015) Multiclass semisupervised learning based upon kernel spectral clustering. IEEE Trans Neural Netw Learn Syst 26(4):720–733MathSciNetCrossRefGoogle Scholar
  29. 29.
    Juan BB (2016) Existence of travelling wave solutions for a Fisher–Kolmogorov system with biomedical applications. Commun Nonlinear Sci Numer Simul 36(1):14–20MathSciNetGoogle Scholar
  30. 30.
    Dixit VS, Mehta H, Bedi P (2014) A proposed framework for group-based multi-criteria recommendations. Appl Artif Intell 28(10):917–956CrossRefGoogle Scholar
  31. 31.
    Li B, Yang Q, Xue XY (2009) Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th international conference on machine learning (ICML 2009), Montreal, Canada, 14–18 June, pp 617–624Google Scholar
  32. 32.
    Ullah MZ, Aono M, Seddiqui M (2015) Estimating a ranked list of human genetic diseases by associating phenotype-gene with gene-disease bipartite graphs. ACM Trans Intell Syst Technol 6(4):1–22CrossRefGoogle Scholar
  33. 33.
    Agni D, Herve J, Laurent A (2014) Image retrieval with reciprocal and shared nearest neighbors. In: 2014 international conference on computer vision theory and applications (VISAPP) 2, pp 321–328Google Scholar
  34. 34.
    Luiz P, Tomasz R, Joshua A (2013) Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Model User Adapt Interact 23(5):477–488Google Scholar
  35. 35.
    Liu J, Wu C, Xiong Y (2014) List-wise probabilistic matrix factorization for recommendation. Inf Sci 278:434–447CrossRefGoogle Scholar
  36. 36.
    Zhou XK, Wu S, Chen G (2014) kNN processing with co-space distance in solomo systems. Expert Syst Appl 41(16):6967–6982CrossRefGoogle Scholar
  37. 37.
    Saleh AI, Desoulw A, Ali SH (2015) Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl Based Syst 75:192–223CrossRefGoogle Scholar
  38. 38.
    Huang JJ, Yuan X, Zhong N (2015) Modeling tag-aware recommendations based on user preferences. Int J Inf Technol Decis Mak 14(5):947–970CrossRefGoogle Scholar
  39. 39.
    Liu W, Wu C, Feng B (2015) Conditional preference in recommender systems. Expert Syst Appl 42(2):774–788CrossRefGoogle Scholar
  40. 40.
    Liu J, Xiong Y, Wu C (2014) Learning conditional preference networks from inconsistent examples. IEEE Trans Knowl Data Eng 26(2):376–390CrossRefGoogle Scholar
  41. 41.
    Liu J, Yao Z, Xiong Y (2013) Learning conditional preference network from noisy samples using hypothesis testing. Knowl Based Syst 40(1):7–16CrossRefGoogle Scholar
  42. 42.
    Le HS, Tran MT (2016) General factorization framework for context-aware recommendations. Data Min Knowl Discov 30(2):342–371MathSciNetCrossRefGoogle Scholar
  43. 43.
    Liu JT, Sui CH, Deng DW (2016) Representing conditional preference by boosted regression trees for recommendation. Inf Sci 327:1–20CrossRefGoogle Scholar
  44. 44.
    Gantner Z, Rendle S, Freudenthaler C (2011) Mymedialite: a free recommender system library. In: Proceedings of the 15th ACM conference on recommender systems, RecSys’11, ACM, New York, NY, USA, pp 305–308Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Faculty of Computer and SoftwareHuaiyin Institute of TechnologyHuaianChina
  2. 2.College of Computer and Information Technology EngineeringHohai UniversityNanjingChina

Personalised recommendations