It can be seen from Table 2 that the proposed gsb algorithm outperforms other algorithms significantly on both datasets, which demonstrates the effectiveness our approach. According to t-test, GSB is significantly better than the second best algorithm GTB, with p<. 001 on all evaluation metrics for both datasets. Despite its simplicity, tag generalization has led to quite sizable improvements on both the subject-based and topic-based algorithms, confirming our hypothesis about the reliability of matrix UT and IT. The fusion method focuses on occurrences of tags rather than their weighing values, thus are not able to benefit from tag generalization. The subject-based algorithms, with or without tag generalization, have shown superiority over the topic-based algorithms, demonstrating the advantages of extracting subjects for recommendation. In general, the tag-enhanced algorithms outperform the traditional CF approaches strikingly, especially on the sparser Large dataset. It implies that it is of great potential to improve item recommendation quality leveraging tagging information, especially when the dataset is sparse. The prediction accuracy of the implemented algorithms on the Small dataset is much higher compared with that on the Large dataset, which may due to the fact that the Small dataset is much denser than the large dataset and more data are available for training/profiling 5 Conclusions and future research This paper discusses a subject-centered model of collaborative tagging, in which subjects are assumed to play the key role in determining users' tagging behavior. We propose the concept of Consistent Nonnegative Matrix Factorization and use it to discover the hidden subjects and inner relations within this model from tagging data. We also propose a preprocessing technique called tag generalization to remove noises stemming from meaningless tags. An evaluation study using two real-world collaborative tagging datasets from Delicious has demonstrated the effectiveness of our approach. For future work, we plan to design more sophisticated techniques for tag generalization, and apply it to matrix IT as well. We will also investigate how to optimize various control parameters of our approach(e. g, c in Equation 1, k, the target number of subjects to be clustered) automatically Acknowledgements The authors wish to acknowledge research support from the NNSFC (60621001, 60875049, 70890084), MOST (2006AA010106), and CAs(2F07C01, 2F08N03) References Halpin, H, Robu, V, and Shepherd, H. "The complex dynamics of collaborative tagging, " in Proceedings of t he 16th i nternational conference on W orld w ide Web, ACM, Banff, Alberta Lambiotte, R, and Ausloos, M. "Collaborative Tagging as a Tripartite Network, in: Lecture Notes in Computer Science In ICCS 2006, 2006, pp. 1114-1117 Lee, DD, and Seung, H.S. "Learning the parts of objects by non-negative matrix factorization, "Nature (401:6755)1999pp788-791 Peng, J, and Zeng, D. Topic-based Web Page Recommendation Using Tags, "in: Proceedings of The 2nd International Workshop on Social Computing, IEEE, Dallas, Texas, 2009 Tso-Sutter, K H.L., Marinho, L B, and Schmidt-Thieme, L. Tag-aware recommender systems by fusion f collaborative filtering algorithms, in: Proceedings of t he A CM s ymposium on A pplied computing, ACM, Fortaleza, Ceara, Brazil, 2008 Xu, W, Liu, X, and Gong, Y "Document clustering based on non-negative matrix factorization, Proceedings oft he 26t h annu al i nternational ACM SI GIR c onference onR esearch and levelopment in informaion retrieval, ACM, Toronto, Canada, 2003 Zhao, S, Du, N, Nauerz, A, Zhang, X, Yuan, Q, and Fu,R "Improved recommendation based on collaborative tagging behaviors, "in: Proceedings of the 13t h i nternational conference on Intelligent user interfaces, ACM, Gran Canaria, Spain, 2008 19th Workshop on Information Technologies and SystemsIt can be seen from Table 2 that the proposed GSB algorithm outperforms other algorithms significantly on both datasets, which demonstrates the effectiveness our approach. According to t-test, GSB is significantly better than the second best algorithm GTB, with p<0.001 on all evaluation metrics for both datasets. Despite its simplicity, tag generalization has led to quite sizable improvements on both the subject-based and topic-based algorithms, confirming our hypothesis about the reliability of matrix UT and IT. The fusion method focuses on occurrences of tags rather than their weighing values, thus are not able to benefit from tag generalization. The subject-based algorithms, with or without tag generalization, have shown superiority over the topic-based algorithms, demonstrating the advantages of extracting subjects for recommendation. In general, the tag-enhanced algorithms outperform the traditional CF approaches strikingly, especially on the sparser Large dataset. It implies that it is of great potential to improve item recommendation quality leveraging tagging information, especially when the dataset is sparse. The prediction accuracy of the implemented algorithms on the Small dataset is much higher compared with that on the Large dataset, which may due to the fact that the Small dataset is much denser than the Large dataset and more data are available for training/profiling. 5. Conclusions and Future Research This paper discusses a subject-centered model of collaborative tagging, in which subjects are assumed to play the key role in determining users’ tagging behavior. We propose the concept of Consistent Nonnegative Matrix Factorization and use it to discover the hidden subjects and inner relations within this model from tagging data. We also propose a preprocessing technique called tag generalization to remove noises stemming from meaningless tags. An evaluation study using two real-world collaborative tagging datasets from Delicious has demonstrated the effectiveness of our approach. For future work, we plan to design more sophisticated techniques for tag generalization, and apply it to matrix IT as well. We will also investigate how to optimize various control parameters of our approach (e.g., c in Equation 1, k, the target number of subjects to be clustered) automatically. Acknowledgements The authors wish to acknowledge research support from the NNSFC (60621001, 60875049, 70890084), MOST (2006AA010106), and CAS (2F07C01, 2F08N03). References Halpin, H., Robu, V., and Shepherd, H. "The complex dynamics of collaborative tagging," in: Proceedings of t he 16th i nternational conference on W orld W ide W eb, ACM, Banff, Alberta, Canada, 2007. Lambiotte, R., and Ausloos, M. "Collaborative Tagging as a Tripartite Network," in: Lecture Notes in Computer Science In ICCS 2006, 2006, pp. 1114-1117. Lee, D.D., and Seung, H.S. "Learning the parts of objects by non-negative matrix factorization," Nature (401:6755) 1999, pp 788-791. Peng, J., and Zeng, D. "Topic-based Web Page Recommendation Using Tags," in: Proceedings of The 2nd International Workshop on Social Computing, IEEE, Dallas, Texas, 2009. Tso-Sutter, K.H.L., Marinho, L.B., and Schmidt-Thieme, L. "Tag-aware recommender systems by fusion of collaborative filtering algorithms," in: Proceedings of t he A CM s ymposium on A pplied computing, ACM, Fortaleza, Ceara, Brazil, 2008. Xu, W., Liu, X., and Gong, Y. "Document clustering based on non-negative matrix factorization," in: Proceedings of t he 26t h annu al i nternational ACM SI GIR c onference on R esearch and development in informaion retrieval, ACM, Toronto, Canada, 2003. Zhao, S., Du, N., Nauerz, A., Zhang, X., Yuan, Q., and Fu, R. "Improved recommendation based on collaborative tagging behaviors," in: Proceedings of the 13t h i nternational conference on Intelligent user interfaces, ACM, Gran Canaria, Spain, 2008. 78 19th Workshop on Information Technologies and Systems