C. C. Aggarwal, J. L. Wolf, K. Lung-wu, and P. S. Yu, Horting hatches an egg, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.201-212, 1999.
DOI : 10.1145/312129.312230

R. Alba, A graph???theoretic definition of a sociometric clique???, The Journal of Mathematical Sociology, vol.11, issue.1, pp.112-126, 1973.
DOI : 10.1080/0022250X.1973.9989826

G. Amati, C. Carpineto, and G. Romano, An effective threshold-based neighbor selection in collaborative filtering, European Conference on Information Retrieval, pp.712-715, 2007.

L. Baltrunas and F. Ricci, Dynamic item weighting and selection for collaborative filtering, Web mining 2.0 Workshop, ECML-PKDD 2007, 2007.

G. Bonnin, A. Brun, and A. Boyer, Web Intelligence and Intelligent Agents, chap. Skipping-Based Collaborative Recommendations inspired from Statistical Language Modeling. Zeeshan-ul-hassan Usmani, 2010.

J. Booth, G. Casella, and J. Hobert, Clustering using objective functions and stochastic search, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.37, issue.1, 2007.
DOI : 10.1111/j.1467-9868.2007.00629.x

P. S. Bradley and U. M. Fayyad, Refining initial points for k-means clustering, Proceedings of the 15th International Conference on Machine Learning (ICML98), pp.91-99, 1998.

L. Branting, Incremental Detection of Local Community Structure, 2010 International Conference on Advances in Social Networks Analysis and Mining, pp.80-87, 2010.
DOI : 10.1109/ASONAM.2010.53

J. Breese, D. Heckerman, and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proc. of UAI-98, 1998.

A. Brun, G. Bonnin, and A. Boyer, History Dependent Recommender Systems Based on Partial Matching, Proceedings of the 17th International Conference on User Modeling, Adaptation and Personalization, pp.343-348, 2009.
DOI : 10.1007/978-3-642-02247-0_34

URL : https://hal.archives-ouvertes.fr/inria-00430592

A. Brun, A. Hamad, O. Buffet, and A. Boyer, Towards preference relations in recommender systems, Preference Learning, European Conference on Machine Learning and Principle and Practice of Knowledge Discovery in Databases (ECML-PKDD 2010), 2010.
URL : https://hal.archives-ouvertes.fr/inria-00523496

S. Brüninghaus and K. Ashley, Toward adding knowledge to learning algorithms for indexing legal cases, Proceedings of the seventh international conference on Artificial intelligence and law , ICAIL '99, pp.9-17, 1999.
DOI : 10.1145/323706.323709

R. Burke, K. Hammond, and E. Cooper, Knowledge-based navigation of complex information spaces, Proc. of the 13th National Conference on Artificial Intelligence, pp.462-468, 1996.

R. Burke and B. Mobasher, Trust and bias in multi-agent recommender systems, conjunction with the 19th International Joint Conference on Artificial Intelligence, 2005.

J. Callut, K. Franoisse, M. Saerens, and P. Dupont, Semi-supervised classification in graphs using bounded random walks, Proceedings of the 17th Annual Machine Learning Conference of Belgium and the Netherlands, pp.67-68, 2008.

L. Candillier, F. Meyer, and M. Boullé, Comparing State-of-the-Art Collaborative Filtering Systems, Proc. of 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLMD'07, pp.548-562, 2007.
DOI : 10.1007/978-3-540-73499-4_41

S. Castagnos, Modélisation de comportements et apprentissage stochastique non supervisé de stratégies d'interactions sociales au sein de systèmes temps réel de recherche et d'accèsaccès`accèsà l'information, 2008.

S. Castagnos and A. Boyer, A client/server user-based collaborative filtering algorithm: Model and implementation, Proc. of the 17th European Conference on Artificial Intelligence, pp.617-621, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00104863

S. Castagnos and A. Boyer, Personalized Communities in a Distributed Recommender System, Proc. of the European Conference on Information Retrieval, pp.343-355, 2007.
DOI : 10.1007/978-3-540-71496-5_32

URL : https://hal.archives-ouvertes.fr/inria-00171796

S. Castagnos and A. Boyer, Privacy concerns when modeling users in collaborative filtering recommender systems. Book chapter Social and Human Elements of Information Security: Emerging Trends and Countermeasures, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00171806

S. Castagnos, N. Jones, P. Pu, and D. Chakrabarti, Eye-tracking product recommenders' usage, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, 2004.
DOI : 10.1145/1864708.1864717

URL : http://infoscience.epfl.ch/record/150716

J. Chen, R. Zaane, and R. Goebel, Local Community Identification in Social Networks, 2009 International Conference on Advances in Social Network Analysis and Mining, pp.237-242, 2009.
DOI : 10.1109/ASONAM.2009.14

I. Choicestream, Choicestream personalization survey, 2006.

A. Clauset, M. Newmann, and C. Moore, Finding community structure in very large networks, Physical Review E, vol.70, issue.6, 2005.
DOI : 10.1103/PhysRevE.70.066111

M. D. Ekstrand, P. Kannan, J. A. Stemper, J. T. Butler, J. A. Konstan et al., Automatically building research reading lists, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.159-16610, 2010.
DOI : 10.1145/1864708.1864740

L. Ertöz, M. Steinbach, and V. Kumar, Information Retrivial and Clustering , chap. Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach, 2002.

G. W. Flake, S. Lawrence, and C. Giles, Efficient identification of Web communities, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.150-160, 2000.
DOI : 10.1145/347090.347121

G. W. Flake, R. Tarjan, and K. Tsioutsiouliklis, Graph Clustering and Minimum Cut Trees, Internet Mathematics, vol.1, issue.4, pp.385-408, 2004.
DOI : 10.1080/15427951.2004.10129093

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.6592

S. Fortunato, Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.75-174, 2009.
DOI : 10.1016/j.physrep.2009.11.002

L. Freeman, A Set of Measures of Centrality Based on Betweenness, Sociometry, vol.40, issue.1, pp.35-41, 1977.
DOI : 10.2307/3033543

M. Girvan and M. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, vol.99, issue.12, pp.7821-7826, 2002.
DOI : 10.1073/pnas.122653799

D. Goldberg, D. Nichols, B. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, pp.61-70, 1992.
DOI : 10.1145/138859.138867

M. Grcar, B. Fortuna, and D. Mladenic, knn versus svm in the collaborative filtering framework, Proceedings of the WebKDD'05 conference, 2005.

S. Gregory, An Algorithm to Find Overlapping Community Structure in Networks, Proc. of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp.91-102, 2007.
DOI : 10.1007/978-3-540-74976-9_12

S. Guha, R. Rastogi, and K. Shim, Cure: An efficient clustering algorithm for large databases, Proc. of the 1998 ACM-SIGMOD International Conference on Management of Data (SIGMOD'98, pp.73-84, 1998.

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.5-53, 2004.
DOI : 10.1145/963770.963772

J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '99, pp.230-237, 1999.
DOI : 10.1145/312624.312682

J. Herlocker, J. Konstan, and J. Riedl, An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Journal of Information Retrieval, 2002.

H. Ino, M. Kudo, and A. Nakamura, Partitioning of Web graphs by community topology, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.661-669, 2005.
DOI : 10.1145/1060745.1060841

R. Jarvis and E. Patrick, Clustering Using a Similarity Measure Based on Shared Near Neighbors, IEEE Transactions on Computers, vol.22, issue.11, p.22, 1973.
DOI : 10.1109/T-C.1973.223640

X. Jiang, W. Song, and W. Feng, Optimizing Collaborative Filtering by Interpolating the Individual and Group Behaviors, Proceedings of the Eighth Asia Pacific Web Conference (APWeb06), pp.568-578, 2006.
DOI : 10.1007/11610113_50

K. Jung, D. Park, and J. Lee, Hybrid collaborative filtering and contentbased filtering for improved recommender system, Proc. of the 2004 International Conference on Computational Science, pp.295-302, 2004.

G. Karypis, Recommendation Algorithms, Proceedings of the tenth international conference on Information and knowledge management , CIKM'01, pp.247-254, 2001.
DOI : 10.1145/502585.502627

N. Lathia, S. Halles, and L. Capra, Trust-Based Collaborative Filtering, IFIP International Federation for Information Processing, vol.263, pp.119-134, 2008.
DOI : 10.1007/978-0-387-09428-1_8

G. Linden, . Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering, IEEE Internet Computing, vol.7, issue.1, pp.76-80, 2003.
DOI : 10.1109/MIC.2003.1167344

F. Luo, J. Wang, and E. Promislow, Exploring Local Community Structures in Large Networks, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06), pp.233-239, 2006.
DOI : 10.1109/WI.2006.72

A. Martinez, P. Mittrapiyanuruk, and A. Kak, On combining graphpartitioning with non-parametric clustering for image segmentation, Computer Vision and Image Understanding, issue.95, pp.72-85, 2004.

C. Miranda and A. Jorge, Incremental Collaborative Filtering for Binary Ratings, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp.389-392, 2008.
DOI : 10.1109/WIIAT.2008.263

N. Mishra, R. Schreiber, I. Stanton, and R. Tarjan, Clustering Social Networks, Proceedings of the WAW conference, pp.56-67, 2007.
DOI : 10.1007/978-3-540-77004-6_5

B. Mobasher, R. Burke, and J. Sandvig, Model-based collaborative filtering as a defense against profile injection attacks, Conference of the American Association for Artificial Intelligence (AAAI2006), 2006.

B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, Improving the effectiveness of collaborative filtering on anonymous web usage data, Proceedings of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP01), 2001.

D. Oard and J. Kim, Implicit feedback for recommender systems, Proceedings of the AAAI Workshop on Recommender Systems, pp.81-83, 1998.

O. Connor, M. Herlocker, and J. , Clustering items for collaborative filtering, Proc. of the SIGIR Conference (SIGIR99), 1999.

S. Papadopoulos, Y. Kompatsiaris, and A. Vakali, Leveraging collective intelligence through community detection in tag networks, Proceedings of CKCaR'09 Workshop on Collective Knowledge Capturing and Representation, 2009.

M. Pazzani and D. Billsus, The Adaptive Web, chap. Content-Based Recommendation Systems, pp.325-341, 2007.

P. Perny and J. Zucker, Collaborative filtering methods based on fuzzy preference relations, Proceedings of EUROFUSE-SIC'99, 1999.

A. Pother, Graph partitioning algorithms with applications to scientific computing, Tech. rep, 1997.

Y. Qi, F. Balem, C. Faloutsos, and J. Klen-seertharman, Protein complex identification by supervised graph local clustering, Bioinformatics, vol.24, issue.13, pp.250-268, 2008.
DOI : 10.1093/bioinformatics/btn164

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718642

F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, Defining and identifying communities in networks, Proceedings of the National Academy of Sciences, vol.101, issue.9, 2004.
DOI : 10.1073/pnas.0400054101

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC365677

J. Redpath, D. Glass, S. Mcclean, and L. Chen, Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings, Proceedings of the European Conference On Information Retrieval, pp.394-406, 2010.
DOI : 10.1007/978-3-642-12275-0_35

M. Rosvall, C. Bergstrom, B. M. Sarwar, G. Karypis, J. Konstan et al., Maps of random walks on complex networks reveal community structure Item-based collaborative filtering recommendation algorithms . In: World Wide Web: Graph clustering, National Academy of Sciences of the United States of America Computer Science Review, vol.105, issue.4, pp.1118-1123, 2001.

J. Schafer, J. Konstan, and J. Riedl, E-commerce recommender applications, Data Mining and Knowledge Discovery, vol.5, issue.1/2, pp.115-152, 2001.
DOI : 10.1023/A:1009804230409

V. Schickel-zuber and B. Faltings, Using hierarchical clustering for learning the ontologies used in recommendation systems, Proc. of the KDD'07, 2007.

J. Scott, Social Network Analysis: A handbook, Sage, 2000.
DOI : 10.4135/9781446294413

U. Shardanand and P. Maes, Social information filtering, Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '95, pp.210-217, 1995.
DOI : 10.1145/223904.223931

Q. Song and N. Kasabov, Foundations of cognitive science, chap. ECM - A Novel On-line, Evolving Clustering Method and Its Applications, pp.631-682, 2001.

N. Srinivasa and S. Medasani, Active fuzzy clustering for collaborative filtering, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), pp.1697-1702, 2004.
DOI : 10.1109/FUZZY.2004.1375436

L. Terán and A. Meier, A Fuzzy Recommender System for eElections, Proc. of the International Conference on Electronic Government and the Information Systems Perspective (EGOVIS'10, pp.62-76, 2010.
DOI : 10.1007/978-3-642-15172-9_6

J. Tian, D. Chen, and Y. Fu, A New Local Algorithm for Detecting Communities in Networks, 2009 First International Workshop on Education Technology and Computer Science, pp.721-724, 2009.
DOI : 10.1109/ETCS.2009.421

K. Tsuda and W. S. Noble, Learning kernels from biological networks by maximizing entropy, Bioinformatics, vol.20, issue.Suppl 1, pp.326-333, 2004.
DOI : 10.1093/bioinformatics/bth906

L. Ungar and D. Foster, Clustering methods for collaborative filtering, AAAI Workshop on Recommendation Systems, 1998.

P. Viappiani, B. Faltings, and P. Pu, Preference-based search using example-critiquing with suggestions, Journal of artificial intelligence Research, vol.27, pp.465-503, 2006.

J. Wang, A. De-vries, and M. Reinders, Unifying user-based and itembased collaborative filtering approaches by similarity fusion, Proc. of the ACM SIGIR, Special Interest Group on Information Retrieval, 2006.
DOI : 10.1145/1148170.1148257

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.692

Y. Wang, D. Chakrabarti, I. Wang, and C. Faloutsos, Epidemic spreading in real networks: an eigenvalue viewpoint, 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings., 2003.
DOI : 10.1109/RELDIS.2003.1238052

P. Wanjantuk and J. Keane, Finding related documents via communities in the citation graph, IEEE International Symposium on Communications and Information Technology, 2004. ISCIT 2004., pp.445-450, 2004.
DOI : 10.1109/ISCIT.2004.1412885

G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng et al., Scalable collaborative filtering using cluster-based smoothing, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '05, 2005.
DOI : 10.1145/1076034.1076056

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.7794

B. Yang, W. Cheung, and J. Liu, Community Mining from Signed Social Networks, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.10, pp.1333-1348, 2007.
DOI : 10.1109/TKDE.2007.1061

J. Yin, X. Fan, Y. Chen, and J. Ren, High-Dimensional Shared Nearest Neighbor Clustering Algorithm, Proc. of the Fuzzy Systems and Knowledge Discovery (FSKD05) conference, pp.494-502, 2005.
DOI : 10.1007/11540007_60

D. Zhou, J. Huang, and B. Schölkopf, Learning from labeled and unlabeled data on a directed graph, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.1036-1043, 2005.
DOI : 10.1145/1102351.1102482

C. Ziegler, Towards Decentralized Recommender Systems, 2005.