COLLABORATIVE RECOMMENDATION SYSTEMS BASED ON SEMI-SUPERVISED FUZZY CLUSTERING METHOD AND APPLING IN CO-AUTHOR NETWORKS

Bui The Hong

Bui The Hong Hung Yen University of Technology and Education

Keywords: collaborative recommendation, classification, fuzzy semi-supervised clustering

Abstract

The collaborative recommendation problem among researchers is currently being emphasized. Most of the existing reseaches deal with collaborative recommendation problems based on collaborative and non-collaborative binary classification. However, due to the sparseness of the co-authors network, the data set used for training is often subject to imbalance leading to low classification efficiency. This paper proposes a collaboration recommendation system based on a fuzzy semi-supervised clustering to overcome the disadvantages of binary clustering for sparse and unbalanced data. Experimental results for the proposed collaborative recommendation system were empirically tested on a practical data set, suggesting that in most cases a more effective fuzzy semi-observer clustering collaboration recommendations system would be more effective compared with the binary classification system.

References

Lopes G. R., Moro M. M., Wives L. K. and De Oliveira J. P. M., Collaboration recommendation on academic social networks. International Conference on Conceptual Modeling, 2010.

Hasan M. Al, Chaoji V., Salem S. and Zaki M., Link prediction using supervised learning. SDM06: workshop on link analysis, counter-terrorism and security, 2006.

Chen B., Li F., Chen S., Hu R.and Chen L., Link prediction based on non-negative matrix factorization. PloS one, p. e0182968, 2017, vol. 12, no. 8.

Y. Guisheng, Y. Wansi and D. Yuxin, “A new link prediction algorithm: node link strength algorithm,” in Computer Applications and Communications (SCAC), 2014 IEEE Symposium, 2014,pp. 5-9.

Gupta S., Pandey S.and. Shukla K. K, Comparison analysis of link prediction algorithms in social network. International Journal of Computer Applications, 2015, vol. 111, no. 16.

Chuan P. M., Ali M., Khang T. D., Huong L. T. and Dey N. Link prediction in co-authorship networks based on hybrid content similarity metric, Applied Intelligence, 2018, 48(8), 2470-2486.

J. S. Breese, D. Heckerman and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 1998.

C. Basu, H. Hirsh and W. Cohen, “Recommendation as classification: Using social and contentbased information in recommendation,” in Aaai/iaai, 1998, pp. 714-720.

T. Bogers and A. Van den Bosch, “Recommending scientific articles using citeulike,” in In Proceedings of the 2008 ACM conference on Recommender systems, 2008.

R. Burke, “Hybrid recommender systems: Survey and experiments,” User modeling and useradapted interaction, 2002, vol. 12, no. 4, pp. 331-370.

R. D. Burke (2007) “Hybrid web recommender systems,” in P. Brusilovsky, A. Kobsa, & W.Nejdl, editors, The Adaptive Web, Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, Springer, 2007, pp. 377-408.

C. Wang and D. M. Blei, “Collaborative topic modeling for recommending scientific articles,”In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, August, pp. 448-456, ACM.

J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum, NewYork, 1981.

E. Yasunori, H. Yukihiro, Y. Makito and M. Sadaaki, “On semi-supervised fuzzy c-means clustering,” in Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on, IEEE,2009, pp. 1119-1124.

T. Murata and S. Moriyasu, “Link prediction of social networks based on weighted proximity measures,” in the IEEE/WIC/ACM international conference on In Web Intelligence, 2007.

H. R. De Sá and R. B. Prudêncio, “Supervised link prediction in weighted networks,” in Neural Networks (IJCNN), The 2011 International Joint Conference on, IEEE, 2011, pp. 2281-2288.

I. Günes, S. Gündüz-Öüdücü and Z. Çataltepe, “Link prediction using time series of neighborhood-based node similarity scores,” Data Mining and Knowledge Discovery, 2016, vol. 30, no. 1, pp. 147-180.

F. Xia, Z. Chen, W. Wang, J. Li and L. T. Yang, “Mvcwalker: Random walk-based most valuable collaborators recommendation exploiting academic factors,” IEEE Transactions on Emerging Topics in Computing, 2014, vol. 2, no. 3, pp. 364-375.