Top

Cluster Computing

Published in:

12-09-2017

A new recommendation algorithm combined with spectral clustering and transfer learning

Authors: Xiang Li, Zhijian Wang

Published in: Cluster Computing | Special Issue 1/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Collaborative filtering (CF) recommendation algorithm has been successfully applied into recommender systems for years which can solve the problem of information overload. However, CF suffers from data sparsity and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a collaborative filtering recommendation algorithm combined with spectral clustering and transfer learning (RASCTL). RASCTL firstly uses spectral clustering to cluster the dimensions of users and items in the original rating matrix. In addition, RASCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RASCTL makes rating forecasting and recommendations combined with the sharing group rating matrix and transfer learning. By the simulation experiments on Epinions and MovieLents data sets, the results show that RASCTL is able to obtain comparable or even better recommendation accuracy and generalization ability compared with other seven CF recommendation algorithms.

previous article An efficient personalized trust based hybrid recommendation (TBHR) strategy for e-learning system in cloud computing

next article Accurate detection of user interest data in cloud computing environment

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Ribeiro, M.T., Ziviani, N., Edleno, S.: Multi-objective pareto-efficient approaches for recommender systems. ACM Trans. Intell. Syst. Technol. 5(4), 38–52 (2015). doi:10.1145/2629350

Guha, S., Mishra, N.: Clustering data streams. In: Minos, G., Johannes, G. (eds.) Data Stream Management, pp. 359–366. Springer, Berlin (2016)

Lin, F., Zhou, X., Zeng, W.H.: Online learning for collaborative filtering. Int. J. Comput. Commun. Control 11(2), 248–258 (2016). doi:10.15837/ijccc.2016.2.2144 CrossRef

Pan, W., Ming, Z.: Interaction-rich transfer learning for collaborative filtering with heterogeneous user feedback. IEEE Intell. Syst. 29(6), 48–54 (2014). doi:10.1109/MIS.2014.2 CrossRef

Komkhao, M., Lu, J., Li, Z.: Incremental collaborative filtering based on mahalanobis distance and fuzzy membership for recommender systems. Int. J. Gen Syst 42(1), 41–46 (2013). doi:10.1080/03081079.2012.710437 CrossRefMATH

Lin, S.Y., Lai, C.H., Wu, C.H.: A trustworthy qos-based collaborative filtering approach for web service discovery. J. Syst. Softw. 93, 217–228 (2014). doi:10.1016/j.jss.2014.01.036 CrossRef

Cron, A., Zhang, L., Agarwal, D.: Collaborative filtering for massive multinomial data. J. Appl. Stat. 41(4), 701–715 (2014). doi:10.1080/02664763.2013.847072 MathSciNetCrossRef

Xiao, M.B., Zheng, X.W.: Collaborative filtering algorithm with stepwise prediction. Appl. Res. Comput. 32(11), 3256–3272 (2015). doi:10.3969/j.issn.1001-3695.2015.11.012

Wu, H., Wang, Y.J., Wang, Z.: Two-phase collaborative filtering algorithm based on co-clustering. J. Softw. 21(5), 1042–1054 (2010). doi:10.3724/SP.J.1001.2010.03758 CrossRef

10.

Zhao, H.C., Zhai, L.L., Zhang, S.C.: A collaborative filtering recommendation method based on clustering of gray association degree and factors of tag overlap. Comput. Eng. Sci. 38(1), 171–175 (2016). doi:10.3969/j.issn.1007-130X.2016.01.028

11.

Zhao, H.X., Wang, X.H., Yang, J.P.: Mixed collaborative recommendation algorithm based on factor analysis of user and item. J. Comput. Appl. 31(5), 1382–1386 (2011). doi:10.3724/SP.J.1087.2011.01382 MATH

12.

Huang, Z., Zeng, D., Chen, H.C.: A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intell. Syst. 22(5), 68–78 (2007). doi:10.1109/MIS.2007.80 CrossRef

13.

Yan, H., Qimin, P., Hu, X.: Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering. IEEE Trans. Serv. Comput. 8(5), 782–794 (2015). doi:10.1109/TSC.2014.2381611 CrossRef

14.

Luo, X., Zhou, M.C., Leung, H.: An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering. IEEE Trans. Autom. Sci. Eng. 13(1), 333–343 (2016). doi:10.1109/TASE.2014.2348555 CrossRef

15.

Saya, Y., Yasunari, Y., Chikoto, K.: Music recommendation hybrid system for improving recognition ability using collaborative filtering and impression words. Artif. Life Robot. 18(1), 109–116 (2013). doi:10.1007/s10015-013-0107-z

16.

Li, W.Z.: Fast program for clustering and comparing large sets of protein or nucleotide sequences. In: Karen E.N. (ed.)Encyclopedia of Metagenomics, pp. 173–177. Springer Science+Business Media, New Work (2015). doi:10.1007/978-1-4899-7478-5_221

17.

Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Computer Vision and Pattern Recognition, Boston, pp. 815–823 (2015). doi:10.1109/CVPR.2015.7298682

18.

Bi, C., Wang, H., Bao, R.: SAR image change detection using regularized dictionary learning and fuzzy clustering. In: International Conference on Cloud Computing and Intelligence Systems, Beijing, pp. 327–330 (2015). doi:10.1109/CCIS.2014.7175753

19.

Schulam, P., Wigley, F., Saria, S.: Clustering longitudinal clinical marker trajectories from electronic health data: applications to phenotyping and endotype discovery. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, pp. 2956–2964 (2015)

20.

Yang, Y., Ma, Z.H., Yi, Y.: Multitask spectral clustering by exploring intertask correlation. IEEE Trans. Cybern. 45(5), 1083–1094 (2015). doi:10.1109/TCYB.2014.2344015 CrossRef

21.

Marina, A.O., Zanoni, D., Siome, G.: Manifold learning and spectral clustering for image phylogeny forests. IEEE Trans. Inf. Forensics Secur. 11(1), 5–18 (2016). doi:10.1109/TIFS.2015.2442527 CrossRef

22.

Sayyed, B.F., Mohammad, R.M., Ali, A.A.: Clustering multispectral images using spatial information. IEEE Geosci. Remote Sens. Lett. 12(7), 1521–1525 (2015). doi:10.1109/LGRS.2015.241155 CrossRef

23.

Budianto, T., Henry, J., Hock, S.S.: Spectral caustic rendering of a homogeneous caustic object based on wavelength clustering and eye sensitivity. Vis. Comput. 31(3), 365–370 (2015). doi:10.1007/s00371-014-1037-z CrossRef

24.

25.

Ullah, M.Z., Aono, M., Seddiqui, M.: 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 (2015). doi:10.1145/2700487 CrossRef

26.

Agni, D., Herve, J., Laurent, A.: Image retrieval with reciprocal and shared nearest neighbors. In: 2014 International Conference on Computer Vision Theory and Applications, Lisbon, pp. 321–328 (2014)

27.

Luiz, P., Tomasz, R., Joshua, A.: Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Model. User Adapt. Interact. 23(5), 77–488 (2013)

28.

Liu, J., Wu, C., Xiong, Y.: List-wise probabilistic matrix factorization for recommendation. Inf. Sci. 278, 434–447 (2014). doi:10.1016/j.ins.2014.03.063 CrossRef

29.

Zhou, X.K., Wu, S., Chen, G.: kNN processing with co-space distance in SoLoMo systems. Expert Syst. Appl. 41(16), 6967–6982 (2014). doi:10.1016/j.eswa.2014.06.008 CrossRef

30.

Saleh, A.I., Desoulw, A., Ali, S.H.: Promoting the performance of vertical recommendation systems by applying new classification techniques. Knowl. Based Syst. 75, 192–223 (2015). doi:10.1016/j.knosys.2014.12.002 CrossRef

31.

Huang, J.J., Yuan, X., Zhong, N.: Modeling tag-aware recommendations based on user preferences. Int. J. Inform. Technol. Decis. Making 14(5), 947–970 (2015). doi:10.1142/S0219622015500194 CrossRef

32.

Liu, W., Wu, C., Feng, B.: Conditional preference in recommender systems. Expert Syst. Appl. 42(2), 774–788 (2015). doi:10.1016/j.eswa.2014.08.044

33.

Liu, J., Xiong, Y., Wu, C.: Learning conditional preference networks from inconsistent examples. IEEE Trans. Knowl. Data Eng. 26(2), 376–390 (2014). doi:10.1109/TKDE.2012.231 CrossRef

34.

Le, H.S., Tran, M.T.: General factorization framework for context-aware recommendations. Data Min. Knowl. Disc. 30(2), 342–371 (2016). doi:10.1007/s10618-015-0417- MathSciNetCrossRef

35.

Liu, J.T., Sui, C.H., Deng, D.W.: Representing conditional preference by boosted regression trees for recommendation. Inf. Sci. 327, 1–20 (2016). doi:10.1016/j.ins.2015.08.001 CrossRef

36.

Gantner, Z., Rendle, S., Freudenthaler, C.: MyMediaLite: a free recommender system library. In: Proceedings of the 15th ACM Conference on Recommender Systems, New York, pp. 305–308 (2011). doi:10.1145/2043932.2043989

Title: A new recommendation algorithm combined with spectral clustering and transfer learning
Authors: Xiang Li
Zhijian Wang
Publication date: 12-09-2017
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 1/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1161-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Special Issue 1/2019

Energy and cluster based efficient routing for broadcasting in mobile ad hoc networks

IoT based fall detection and ambient assisted system for the elderly

Revamping data access privacy preservation method against inside attacks in wireless sensor networks

B+-tree construction on massive data with Hadoop

Study of spectral reflectance reconstruction based on regularization matrix R method

Cloud-assisted privacy-preserving profile-matching scheme under multiple keys in mobile social network

Premium Partner