|
[1] F. Aiolli. A preliminary study on a recommender system for the million songs dataset challenge. In Proceedings of the 2013 International Institute of Refriger- ation (IIR), pages 73–83. Citeseer, 2013. [2] P. Bedi, H. Kaur, and S. Marwaha. Trust based recommender system for seman- tic web. In Proceedings of the 2007 International Joint Conference on Artificial Intelligence (IJCAI), volume 7, pages 2677–2682, 2007. [3] M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of infor- mation propagation in the flickr social network. In Proceedings of the 18th Interna- tional Conference on World Wide Web, pages 721–730. ACM, 2009. [4] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, et al. The youtube video recommendation system. In Proceedings of the 4th ACM Conference on Recommender Systems, pages 293–296. ACM, 2010. [5] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61–70, 1992. [6] D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proceedings of the 13th International Conference on World Wide Web, pages 491–501. ACM, 2004. [7] L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In Proceedings of the 20th International Conference Companion on World Wide Web, pages 57–58. ACM, 2011. [8] L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining, pages 557– 566. ACM, 2013. [9] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang. Social contex- tual recommendation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pages 45–54. ACM, 2012. [10] G. Linden, B. Smith, and J. York. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76–80, 2003. [11] H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 203–210. ACM, 2009. [12] P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM Conference on Recommender Systems, pages 17–24. ACM, 2007. [13] I. Pila ́szy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the 3rd ACM Conference on Recom- mender Systems, pages 93–100. ACM, 2009. [14] S. Rendle. Factorization machines. In Processing of the 10th IEEE International Conference on Data Mining (ICDM), pages 995–1000. IEEE, 2010. [15] S. Rendle. Factorization machines with libfm. Proceedings of the 2012 ACM Trans- actions on Intelligent Systems and Technology (TIST), 3(3):57, 2012. [16] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, pages 285–295. ACM, 2001. [17] J. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recom- mender systems. The adaptive web, pages 291–324, 2007. [18] Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. Tfmap: optimizing map for top-n context-aware recommendation. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval, pages 155–164. ACM, 2012. [19] J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. arXiv preprint arXiv:1206.4603, 2012.
|