G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.6, pp.734-749, 2005.
DOI : 10.1109/TKDE.2005.99

M. Akhmatnurov and D. I. Ignatov, Context-aware recommender system based on boolean matrix factorisation, pp.99-110, 2015.

F. Alqadah, C. K. Reddy, J. Hu, and H. F. Alqadah, Biclustering neighborhood-based collaborative filtering method for top-n recommender systems, Knowledge and Information Systems, vol.11, issue.8, pp.475-491, 2015.
DOI : 10.1016/j.knosys.2013.04.010

A. Ben-dor, B. Chor, R. Karp, and Z. Yakhini, Discovering local structure in gene expression data, Proceedings of the sixth annual international conference on Computational biology , RECOMB '02, pp.3-4, 2003.
DOI : 10.1145/565196.565203

P. Du-boucher-ryan and D. Bridge, Collaborative Recommending using Formal Concept Analysis, Knowledge-Based Systems, vol.19, issue.5, pp.309-315, 2006.
DOI : 10.1016/j.knosys.2005.11.017

V. Codocedo and A. Napoli, Lattice-based biclustering using partition pattern structures, Proceedings of the Twenty-first European Conference on Artificial Intelligence, pp.213-218, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095865

V. Codocedo-henríquez, ContributionsàContributionsà l'indexation etàet`età la récupération d'information utilisant l'analyse formelle de concepts, 2015.

B. Ganter and S. O. Kuznetsov, Pattern Structures and Their Projections, International Conference on Conceptual Structures, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

D. I. Ignatov, A. Y. Kaminskaya, N. Konstantinova, A. Malyukov, and J. Poelmans, FCA-Based Recommender Models and Data Analysis for Crowdsourcing Platform Witology, International Conference on Conceptual Structures, pp.287-292, 2014.
DOI : 10.1007/978-3-319-08389-6_24

D. I. Ignatov, S. O. Kuznetsov, and J. Poelmans, Concept-Based Biclustering for Internet Advertisement, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.123-130, 2009.
DOI : 10.1109/ICDMW.2012.100

D. I. Ignatov, J. Poelmans, and V. Zaharchuk, Recommender system based on algorithm of bicluster analysis RecBi. arXiv preprint arXiv, pp.1202-2892, 2012.

D. I. Ignatov and B. W. Watson, Towards a unified taxonomy of biclustering methods. arXiv preprint arXiv:1702, p.5376, 2017.

M. Kaytoue, S. O. Kuznetsov, J. Macko, and A. Napoli, Biclustering meets triadic concept analysis, Annals of Mathematics and Artificial Intelligence, vol.14, issue.2???3, pp.55-79, 2014.
DOI : 10.1145/1066157.1066236
URL : https://hal.archives-ouvertes.fr/hal-01101143

S. C. Madeira and A. L. Oliveira, Biclustering algorithms for biological data analysis: a survey, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.1, issue.1, pp.24-45, 2004.
DOI : 10.1109/TCBB.2004.2

J. Wang and J. Han, BIDE: efficient mining of frequent closed sequences, Proceedings. 20th International Conference on Data Engineering, pp.79-90, 2004.
DOI : 10.1109/ICDE.2004.1319986

X. Yan, J. Han, and R. Afshar, CloSpan: Mining: Closed Sequential Patterns in Large Datasets, Proceedings of the 2003 SIAM international conference on data mining, pp.166-177, 2003.
DOI : 10.1137/1.9781611972733.15