References
- [1] Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, Knowledge and Data Engineering, IEEE Transactions on,17, 6, 2005, 734-749.
- [2] Bellogín A., Cantador I., Díez F., An empirical comparison of social, collaborative filtering, and hybrid recommenders, ACM Transactions on Intelligent Systems and Technology (TIST), 4, 1, 2013, 14.
- [3] Biancalana C., Gasparetti F., Micarelli A., An approach to social recommendation for context-aware mobile services, ACM Transactions on Intelligent Systems and Technology (TIST), 4, 1, 2013, 10.
- [4] Blondel V D., Guillaume J L., Lambiotte R., Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, 2008, 10008.
- [5] Breese J.S., Heckerman D., Kadie C., Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence Morgan Kaufmann Publishers Inc., 1998: 43-52.
- [6] Balabanović M., Shoham Y., Fab: content-based, collaborative recommendation, Communications of the Association of Computing Machinery, 40, 3, 1997, 66-72.
- [7] Chen X.Y., Zhang C., Lin Z.Q., Xiao B., Ma H, A Collaborative Filtering Method using Topological-Potential Based Community Discovery Strategy, Institute of Electrical and Electronics Engineers, Conference on Information Security and Artificial Intelligence, 2010. 229-223.
- [8] Guo L., Ma J., Chen Z., Learning to recommend with social relation ensemble. Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012, 2599-2602.
- [9] Girardi R., Marinho L.B., A domain model of Web recommender systems based on usage mining and collaborative filtering, Requirements Engineering,12, 1, 2007, 23-40.
- [10] Getoor L., Sahami M., Using probabilistic relational models for collaborative filtering, Workshop on Web Usage Analysis and User Profiling, 1999.
- [11] Good N., Schafer J.B., Konstan J.A., Combining collaborative filtering with personal agents for better recommendations, Innovative Applications of Artificial Intelligence Conferences, 1999, 439-446.
- [12] Melville P., Mooney R.J., Nagarajan R., Content-boosted collaborative filtering for improved recommendations, American Association for Artificial Intelligence, 2002, 187-192.
- [13] Jiang M., Cui P., Liu R., Social contextual recommendation, Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012, 45-54.
- [14] Newman M.E.J., Fast algorithm for detecting community structure in networks, in: Physical review E, 69, 6, 2004, 066133.
- [15] Pavlov D., Pennock D M., A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains, Neural Information Processing Systems Foundation, 2002, 2, 1441-1448.
- [16] Sarwar B.M., Konstan J.A., Borchers A., Herlocker J., Miller B., Riedl J., Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System, Proceedings of Computer Supported Cooperative Work’98, (Seattle, WA, USA), Nov. 1998, 345-354.
- [17] Shih Y.Y. and Liu D.R., Hybrid recommendation approaches: collaborative filtering via valuable content information. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on. IEEE, 2005, 217b-217b.
- [18] Sun G.F., Wu L., Liu Q, Zhu C., Chen E.H., Recommendations Based on Collaborative Filtering by Exploiting Sequential Behaviours, Ruan Jian Xue Bao/Journal of Software, 24,11, 2013, 2721-2733.
- [19] Su X.P., Song Y.R., Lou J.G., Jiang Y.L., Leveraging Overlapping Communities Detection Improve Personalized Recommendation in Folksonomy Networks, Journal of Chinese Computer Systems, 34, 9, 2013, 2036-2041.
- [20] Tang J., Zhang Y., Sun J.M., Rao J.H., Yu W.J., Chen Y.R., and Fong A.C.M., Quantitative Study of Individual Emotional States in Social Networks, T, Affective Computing, 3, 2, 2012, 132-144.
- [21] Yang D.Q., Zhang D.Q., Yu Z.Y., Yu Z.W., Fine-grained preference aware location search leveraging crowd sourced digital footprints from LBSNs, in 13th International Conference on Ubiquitous Computing’13, (Zurich, Switzerland), Sept. 2013, 479-488.
- [22] Yoshii K., Goto M., Komatani K., An efficient hybrid music recommender system using an increment ally trainable probabilistic generative model, IEEE Transactions on Audio Speech and Language Processing, 16, 2, 2008, 435-447
- [23] Zhu L., Ge W., Research on Personalized Recommendation Algorithm Based on Social Network, International Conference on Computer and Electrical Engineering 4th ASME Press, 2011.
- [24] Zhao Q.Q., K. Lu, Wang B., SPCF: A Memory Based Collaborative Filtering Algortihm via Propagation, Chinese Journal of Computers, 36, 3, 2013, 671–676.
- [25] Zhang Y., Zhang B., Gao K.N., Guo P.W., Sun D.M., Autonomy Oriented Personalized Tag Recommendation, Journal of Electronic, 40, 12, 2012, 2353-2359.