Recommending Scientific Literatures in a Collaborative Tagging Environment Tag-text-cosine-TKKTTC-TKK): Almost the same with TTD-TKK except that cosine-based similarity is used instead of dot-product-based similarity 4 Conclusions and Acknowledgment As the experiment shows, our tag-based algorithm is better than the baseline algo- rithm. The extension of user model with literature keywords and dot-product-based hilarity computation also help to achieve better results. The prototype is now avail- able under PKUSpace"[8] This work is partially supported by NSCF Grant(60573166) as well as Network Key lab Grant of Guang Dong Province References 1. Burke, R: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331-370(2002) 2. Balabanovic, M., Shoham, Y. Fab: Content-Based, Collaborative Recommendation. Com- munications of the ACM 40(3), 66-72(1997) 3. Golder, S.A., Huberman, B.A. Usage Patterns of Collaborative Tagging systems. Journal of Information Science 32(2), 198-208(2006) 4. Torres, R. McNee, S.M., Abel, M., Konstan, J.A., Riedl, J. Enhancing Digital Libraries ith TechLens+ In: Proc. of the 2004 Joint ACM/IEEE Conference on Digital Libraries p.228-2362004) 5. McNee, S.M., Albert, I, Cosley, D, Gopalkrishnan, P, Lam, S.K., Rashid, A M, Konstan, JA, Riedl, J. On the Recommending of Citations for Research Papers. In: CSCW 2002 Proceedings of the 2002 6. Sarwa, B M, Karypis, G, Konstan, J, Riedl, J: Analysis of Recommendation Algorithms for E-commerce [R]. In: ACM Conference on Electronic Commerce, pp. 158-167(2000) 7. Pan, H.Y., Lin, H F, Zhao, J. Collaborative Filtering Algorithm Based on Matrix Partition and Interest Variance. Journal of the China Society for Scientific and Technical Informa tion25(1),49-54(2006) 8. Zhang, M, Yang, D Q, Deng, Z H, Feng, Y, Wang, wQ, Zhao, P.X., Wu, S, Wang SA Tang, s.W.: PKUSpace: A Collaborative Platform for Scientific Researching. In: Liu, w, Shi, Y, Li, Q.(eds )ICWL 2004. LNCS, vol 3143, pp. 120-127. Springer, Heidelberg (2004) http://fusion.grids.cn:8080/pkuspace/hoMe.jspRecommending Scientific Literatures in a Collaborative Tagging Environment 481 Tag-text-cosine-TKK (TTC-TKK): Almost the same with TTD-TKK except that cosine-based similarity is used instead of dot-product-based similarity. 4 Conclusions and Acknowledgments As the experiment shows, our tag-based algorithm is better than the baseline algo￾rithm. The extension of user model with literature keywords and dot-product-based similarity computation also help to achieve better results. The prototype is now avail￾able under PKUSpace4 [8]. This work is partially supported by NSCF Grant (60573166) as well as Network Key Lab Grant of Guang Dong Province. References 1. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002) 2. Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Com￾munications of the ACM 40(3), 66–72 (1997) 3. Golder, S.A., Huberman, B.A.: Usage Patterns of Collaborative Tagging systems. Journal of Information Science 32(2), 198–208 (2006) 4. Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing Digital Libraries with TechLens+. In: Proc. of the 2004 Joint ACM/IEEE Conference on Digital Libraries, pp. 228–236 (2004) 5. McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the Recommending of Citations for Research Papers. In: CSCW 2002: Proceedings of the 2002 6. Sarwa, B.M., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-commerce [R]. In: ACM Conference on Electronic Commerce, pp. 158–167 (2000) 7. Pan, H.Y., Lin, H.F., Zhao, J.: Collaborative Filtering Algorithm Based on Matrix Partition and Interest Variance. Journal of the China Society for Scientific and Technical Informa￾tion 25(1), 49–54 (2006) 8. Zhang, M., Yang, D.Q., Deng, Z.H., Feng, Y., Wang, W.Q., Zhao, P.X., Wu, S., Wang, S.A., Tang, S.W.: PKUSpace: A Collaborative Platform for Scientific Researching. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 120–127. Springer, Heidelberg (2004) 4 http://fusion.grids.cn:8080/PKUSpace/home.jsp