2Ioannis K, Vassilios S. On social networks and collaborative recommendation [C] //Proc of the 32nd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2009: 195-202.
3Ferman A M, James H E, Peter van B, et al. Content-based filtering and personalization using structured metadata [C] // Proc of the 2nd ACM/IEEE-CS Joint Conf on Digital Libraries. New York: ACM, 2002: 393-393.
4Jonathan L H, Joseph A K, Al B, et al. An algorithmic framework for performing collaborative filtering [C] //Proc of the 22nd Annual Int ACM SIGIR Conf Research and Development in Information Retrieval. New York: ACM, 1999: 230-237.
5John S B, David H, CARL K. Empirical analysis of predictive algorithms for collaborative filtering [C] //Proc of the 14th Conf on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann, 1998: 43-52.
6Dmitry r. David M P. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains [C] //Proc of the 16th Annual Conf Neural Information Processing Systems. Cambridge: MIT Press 2002: 1441-1448.
7Marko B, Yoav S. Fab: Content-based, collaborative recommendation [J]. Communications of the ACM, 1997, 40(3): 66-72.
8Nick C, Martin S. Random walks on the click graph [C] // Proc of the 30th Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2007: 239-246.
9Hilmi Y, Mukkai S K. A random walk method for alleviating the sparsity problem in collaborative filtering [C] //Proc of the 2008 ACM Conf on Recommender Systems. New York: ACM, 2008: 131-138.
10Yang S, Lu Z, Giles C L. Automatic tag recommendation algorithms for social recommender systems [J]. ACM Transactions on the Web, 2011, 5(1): 1-31.
