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.
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References
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):345–366
Xiong G, Zhu FH, Dong XS (2016) Semantics-aware content-based recommender systems: design and architecture guidelines. Neurocomputing 254(SI):79–85
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 72:37–48
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):1202–1222
Yang B, Lei Y, Liu JM (2017) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633–1647
Aghdam MH, Analoui M, Kabiri P (2017) Collaborative filtering using non-negative matrix factorisation. J Inf Sci 43(4):567–579
Gordillo A, Barra E, Quemada J (2017) A hybrid recommendation model for learning object repositories. IEEE Lat Am Trans 15(3):462–473
Kassak O, Kompan M, Bielikova M (2016) Personalized hybrid recommendation for group of users: Top-N multimedia recommender. Inf Process Manag 52(3):459–477
Liu YN, Wang YW, Feng LZ (2016) Term frequency combined hybrid feature selection method for spam filtering. Pattern Anal Appl 19(2):369–383
Xiao MB, Zheng XW (2015) Collaborative filtering algorithm with stepwise prediction. Appl Res Comput 32(11):3256–3272
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):1031–1059
Hamidreza K, Kourosh K (2017) A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst Appl 83:30–39
Zhao HX, Wang XH, Yang JP (2011) Mixed collaborative recommendation algorithm based on factor analysis of user and item. J Comput Appl 31(5):1382–1386
Yu J, Yang XK, Gao F (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 99:1–11
Yu J, Rui Y, Tang YY (2014) High-order distance based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442
Yu J, Tao DC, Wang M (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779
Yu J, Hong RC, Wang M (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512–3519
Meng XF, Ci X (2013) Big data management: concepts, techniques and challenges. J Comput Res Dev 50(1):146–169
Zhao S, Cao Q, Chen J (2016) A multi-atl method for transfer learning across multiple domains with arbitrarily different distribution. Knowl Based Syst 94:60–69
Saha B, Gupta S, Dinh P (2016) Multiple task transfer learning with small sample sizes. Knowl Inf Syst 46(2):315–342
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):782–794
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):333–343
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):109–116
Yang Y, Ma Z, Yang Y (2015) Multitask spectral clustering by exploring intertask correlation. IEEE Trans Cybern 45(5):1083–1094
Marina AO, Zanoni D, Siome G (2016) Manifold learning and spectral clustering for image phylogeny forests. IEEE Trans Inf Forensics Secur 11(1):5–18
Sayyed BF, Mohammad RM, Ali AA (2015) Clustering multispectral images using spatial–spectral information. IEEE Geosci Remote Sens Lett 12(7):1521–1525
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):365–370
Siamak M, Carlos A, Raghvendra M (2015) Multiclass semisupervised learning based upon kernel spectral clustering. IEEE Trans Neural Netw Learn Syst 26(4):720–733
Juan BB (2016) Existence of travelling wave solutions for a Fisher–Kolmogorov system with biomedical applications. Commun Nonlinear Sci Numer Simul 36(1):14–20
Dixit VS, Mehta H, Bedi P (2014) A proposed framework for group-based multi-criteria recommendations. Appl Artif Intell 28(10):917–956
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 617–624
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):1–22
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 321–328
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):477–488
Liu J, Wu C, Xiong Y (2014) List-wise probabilistic matrix factorization for recommendation. Inf Sci 278:434–447
Zhou XK, Wu S, Chen G (2014) kNN processing with co-space distance in solomo systems. Expert Syst Appl 41(16):6967–6982
Saleh AI, Desoulw A, Ali SH (2015) Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl Based Syst 75:192–223
Huang JJ, Yuan X, Zhong N (2015) Modeling tag-aware recommendations based on user preferences. Int J Inf Technol Decis Mak 14(5):947–970
Liu W, Wu C, Feng B (2015) Conditional preference in recommender systems. Expert Syst Appl 42(2):774–788
Liu J, Xiong Y, Wu C (2014) Learning conditional preference networks from inconsistent examples. IEEE Trans Knowl Data Eng 26(2):376–390
Liu J, Yao Z, Xiong Y (2013) Learning conditional preference network from noisy samples using hypothesis testing. Knowl Based Syst 40(1):7–16
Le HS, Tran MT (2016) General factorization framework for context-aware recommendations. Data Min Knowl Discov 30(2):342–371
Liu JT, Sui CH, Deng DW (2016) Representing conditional preference by boosted regression trees for recommendation. Inf Sci 327:1–20
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 305–308
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.
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Li, X., Wang, Z., Hu, R. et al. Recommendation algorithm based on improved spectral clustering and transfer learning. Pattern Anal Applic 22, 633–647 (2019). https://doi.org/10.1007/s10044-017-0671-2
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DOI: https://doi.org/10.1007/s10044-017-0671-2