1. Adler, B.T., Alfaro, L.D.: A Content-Driven Reputation System for the Wikipedia. Technical Report ucsc-crl-06-18. School of Engineering, University of California, Santa Cruz (2006)

   Google Scholar 

2. Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation, pp. 714–720. AAAI Press (1998)

   Google Scholar 

3. Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M., García-Duque, J., Fernández-Vilas, A., Díaz-Redondo, R.P., Bermejo-Muñoz, J.: A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowl-Based Syst. 21, 305–320 (2008)

   Article  Google Scholar 

4. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc., Madison (1998)

   Google Scholar 

5. Christo, D., Darina, D., Lora, A.: Using topic maps for e-learning. In: Proceedings of International Conference on Computers and Advanced Technology in Education including the IASTED International Symposium on Web-Based Education, Rhodes, Greece, pp. 26–31 (2003)

   Google Scholar 

6. Dong, Y., Li, M.: HyO-XTM: a set of hyper-graph operations on XML Topic Map toward knowledge management. Future Gener. Comp. Sy. 20, 81–100 (2004)

   Article  Google Scholar 

7. Golbeck, J., Rothstein, M.: Linking social networks on the web with FOAF: a semantic web case study. In: AAAI 2008, pp. 1138–1143. AAAI Press (2008)

   Google Scholar 

8. Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412. ACM, New York (2004)

   Chapter  Google Scholar 

9. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22, 116–142 (2004)

   Article  Google Scholar 

10. Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: KDD 2009, pp. 397–406. ACM, New York (2009)

    Google Scholar 

11. Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International Conference on World Wide Web, pp. 640–651. ACM, Budapest (2003)

    Google Scholar 

12. Kini, A., Choobineh, J.: Trust in electronic commerce: definition and theoretical considerations. In: Proceedings of the Thirty-First Hawaii International Conference on System Sciences, vol. 4, pp. 51–61 (1998)

    Google Scholar 

13. Knud, S.: Topic Maps-An Enabling Technology for Knowledge Management. In: International Workshop on Database and Expert Systems Applications, p. 472 (2001)

    Google Scholar 

14. Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: An Experimental Analysis, pp. 221–235 (2004)

    Google Scholar 

15. Massa, P., Avesani, P.: Trust-aware recommender systems. In: RecSys 2007, pp. 17–24. ACM, New York (2007)

    Google Scholar 

16. Massa, P.:http://www.trustlet.org/wiki/Extended_Epinions_dataset.

17. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth National Conference on Artificial Intelligence (AAAI 2002)/Fourteenth Innovative Applications of Artificial Intelligence Conference, pp. 187–192 (2002)

    Google Scholar 

18. Miranda, T., Claypool, M., Gokhale, A., Murnikov, P., Netes, D., Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper (1999)

    Google Scholar 

19. Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E.: Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 553–561. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

20. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)

    Google Scholar 

21. Schein, A.I., Popescul, A., Popescul, R., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold-Start Recommendations, pp. 253–260. ACM Press (2002)

    Google Scholar 

22. G. M. O. The Topic Maps: XML Topic Maps (XTM) 1.0. (2001)

    Google Scholar 

23. Tso, K., Schmidt-Thieme, L.: Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data. Data Science and Classification, 271–278 (2006)

    Google Scholar 

24. Vercellis, C.: Business Intelligence: Data Mining and Optimization for Decision Making, p. 277. John Wiley & Sons, Ltd., Chichester (2009)

    MATH  Google Scholar 

25. Vydiswaran, V.G.V., Zhai, C., Roth, D.: Content-driven trust propagation framework. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 974–982. ACM, San Diego (2011)

    Google Scholar 

26. Zhang, Z.K., Zhou, T., Zhang, Y.C.: Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A 389, 179–186 (2010)

    Article  Google Scholar 

27. Ziegler, C., Lausen, G.: Spreading activation models for trust propagation. In: Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 83–97 (2004)

    Google Scholar 

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