F. Martin, Recsys'09 industrial keynote, Proceedings of the third ACM conference on Recommender systems, RecSys '09, p.12, 2009.
DOI : 10.1145/1639714.1639715

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and E. J. , GroupLens, Proceedings of the 1994 ACM conference on Computer supported cooperative work , CSCW '94, pp.175-186, 1994.
DOI : 10.1145/192844.192905

U. Shardanand, « Social information filtering for music recommendation, Massachusetts Institute of Technology, 1994.

W. Hill, L. Stead, and M. Rosenstein, Furnas, « Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI conference on Human factors in computing systems, pp.194-201, 1995.

K. Goldberg, T. Roeder, D. Gupta, E. C. Perkins, and . Eigentaste, A constant time collaborative filtering algorithm, Information Retrieval, vol.4, issue.2, pp.133-151, 2001.
DOI : 10.1023/A:1011419012209

J. B. Schafer, J. A. Konstan, and E. J. , E-Commerce Recommendation Applications, Applications of Data Mining to Electronic Commerce, pp.115-153, 2001.
DOI : 10.1007/978-1-4615-1627-9_6

R. Burke, « Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction, vol.12, issue.4, pp.331-370, 2002.
DOI : 10.1023/A:1021240730564

J. John, « Pandora and the music genome project, Sci. Comput, vol.23, issue.10, pp.40-41, 2006.

F. Ricci, L. Rokach, and E. B. Shapira, Introduction to Recommender Systems Handbook, 2011.
DOI : 10.1007/978-0-387-85820-3_1

R. Mihalcea and A. Csomai, Wikify!, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , CIKM '07, pp.233-242, 2007.
DOI : 10.1145/1321440.1321475

Ò. Celma, Foafing the music: Bridging the semantic gap in music recommendation, The Semantic Web-ISWC 2006, pp.927-934, 2006.

B. Sheth and P. Maes, Evolving agents for personalized information filtering, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications, pp.345-352, 1993.
DOI : 10.1109/CAIA.1993.366590

Y. Blanco-fernandez, J. Pazos-arias, A. Gil-solla, and M. Ramos-cabrer, Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems, IEEE Transactions on Consumer Electronics, vol.54, issue.2, pp.727-735, 2008.
DOI : 10.1109/TCE.2008.4560154

P. Basile, M. De-gemmis, A. L. Gentile, P. Lops, G. Semeraro et al., JIGSAW algorithm for word sense disambiguation, Proceedings of the 4th International Workshop on Semantic Evaluations, pp.398-401, 2007.

M. Baldauf, S. Dustdar, and E. F. Rosenberg, A survey on context-aware systems, International Journal of Ad Hoc and Ubiquitous Computing, vol.2, issue.4, pp.263-277, 2007.
DOI : 10.1504/IJAHUC.2007.014070

M. Khabbaz, L. V. Lakshmanan, and . Toprecs, Top-k algorithms for item-based collaborative filtering, Proceedings of the 14th International Conference on Extending Database Technology, pp.213-224, 2011.

X. Su and T. M. Khoshgoftaar, « A survey of collaborative filtering techniques », Adv, Artif. Intell, issue.4, 2009.

H. Ma, H. Yang, M. R. Lyu, E. I. King, and . Sorec, social recommendation using probabilistic matrix factorization, Proceedings of the 17th ACM conference on Information and knowledge management, pp.931-940, 2008.

L. M. De-campos, J. M. Fernández-luna, J. F. Huete, and M. A. , Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks, International Journal of Approximate Reasoning, vol.51, issue.7, pp.785-799, 2010.
DOI : 10.1016/j.ijar.2010.04.001

T. Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems, vol.22, issue.1, pp.89-115, 2004.
DOI : 10.1145/963770.963774

M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, « Collaborative filtering recommender systems », Found. Trends Hum, Comput. Interact, vol.4, issue.2, pp.81-173, 2011.

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

B. Sarwar, G. Karypis, and J. Konstan, Riedl, « Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, pp.285-295, 2001.

G. Linden, B. Smith, J. York, and . Amazon, 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

J. Herlocker, J. A. Konstan, and E. J. , « An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Information Retrieval, vol.5, issue.4, pp.287-310, 2002.
DOI : 10.1023/A:1020443909834

J. S. Breese and D. Heckerman, Kadie, « Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp.43-52, 1998.

D. Mladenic, Text-learning and related intelligent agents: a survey, IEEE Intelligent Systems, vol.14, issue.4, pp.44-54, 1999.
DOI : 10.1109/5254.784084

C. Ziegler, S. M. Mcnee, and J. A. Konstan, Lausen, « Improving recommendation lists through topic diversification, Proceedings of the 14th international conference on World Wide Web, pp.22-32, 2005.

M. Anderson, M. Ball, H. Boley, S. Greene, N. Howse et al., A rule-applying collaborative filtering system, Proceedings of COLA, 2003.

Z. Xia and Y. Dong, Xing, « Support vector machines for collaborative filtering, Proceedings of the 44th annual Southeast regional conference, pp.169-174, 2006.

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. , Evaluating collaborative filtering recommender systems, Evaluating collaborative filtering recommender systems, pp.5-53, 2004.
DOI : 10.1145/963770.963772

D. Billsus and M. J. Pazzani, « User modeling for adaptive news access, User Modeling and User-Adapted Interaction, vol.10, issue.2/3, pp.147-180, 2000.
DOI : 10.1023/A:1026501525781

Y. Zhang, J. Callan, and E. T. Minka, Novelty and redundancy detection in adaptive filtering, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '02, pp.81-88, 2002.
DOI : 10.1145/564376.564393

H. Lieberman and . Letizia, An agent that assists web browsing, IJCAI 1, pp.924-929, 1995.

M. Pazzani and D. Billsus, Learning and revising user profiles: The identification of interesting web sites, Machine Learning, vol.27, issue.3, pp.313-331, 1997.
DOI : 10.1023/A:1007369909943

F. A. Asnicar and C. Tasso, « ifWeb: a prototype of user model-based intelligent agent for document filtering and navigation in the world wide web, Proceedings of WorkshopAdaptive Systems and User Modeling on the World Wide Web'at 6th International Conference on User Modeling, pp.3-11, 1997.

A. Moukas, Amalthaea information discovery and filtering using a multiagent evolving ecosystem, Applied Artificial Intelligence, vol.11, issue.5, pp.437-457, 1997.
DOI : 10.1080/088395197118127

L. Chen, K. Sycara, and . Webmate, a personal agent for browsing and searching, Proceedings of the second international conference on Autonomous agents, pp.132-139, 1998.

H. Sorensen, A. O-'riordan, and C. O-'riordan, « Profiling with the informer text filtering agent, J. Univers. Comput. Sci, vol.3, issue.8, pp.988-1006, 1997.

D. Billsus and M. J. Pazzani, A Hybrid User Model for News Story Classification, Proceeding UM '99 Proceedings of the seventh international conference on User modeling Pages 99-108 Springer, 1999.
DOI : 10.1007/978-3-7091-2490-1_10

J. Ahn, P. Brusilovsky, J. Grady, D. He, and S. Y. , Syn, « Open user profiles for adaptive news systems: help or harm?, Proceedings of the 16th international conference on World Wide Web, pp.11-20, 2007.

P. Lops and M. De-gemmis, Semeraro, « Content-based recommender systems: State of the art and trends, Recommender systems handbook, pp.73-105, 2011.

B. Magnini and C. Strapparava, « Improving user modelling with content-based techniques », in User Modeling, pp.74-83, 2001.

M. Degemmis, P. Lops, and G. Semeraro, A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation, User Modeling and User-Adapted Interaction, vol.46, issue.2, pp.217-255, 2007.
DOI : 10.1007/s11257-006-9023-4

G. Semeraro, P. Basile, and M. De-gemmis, Lops, User Profiles for Personalizing Digital Libraries, 2009.

M. Eirinaki and M. , Vazirgiannis, et I. Varlamis, « SEWeP: using site semantics and a taxonomy to enhance the Web personalization process, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.99-108, 2003.

S. E. Middleton, N. R. Shadbolt, and D. C. De-roure, Ontological user profiling in recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.54-88, 2004.
DOI : 10.1145/963770.963773

I. Cantador, A. Bellogín, and E. P. Castells, News@hand: A Semantic Web Approach to Recommending News, Adaptive Hypermedia and Adaptive Web-Based Systems, pp.279-283, 2008.
DOI : 10.1007/978-3-540-70987-9_34

E. Gabrilovich and S. Markovitch, « Overcoming the brittleness bottleneck using Wikipedia: Enhancing text categorization with encyclopedic knowledge, AAAI, pp.1301-1306, 2006.

E. Gabrilovich and S. Markovitch, Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis. », in IJCAI, pp.1606-1611, 2007.

J. Lees-miller, F. Anderson, B. Hoehn, and E. R. Greiner, Does Wikipedia Information Help Netflix Predictions?, 2008 Seventh International Conference on Machine Learning and Applications, pp.337-343, 2008.
DOI : 10.1109/ICMLA.2008.121

G. Semeraro, P. Lops, and P. Basile, de Gemmis, « Knowledge infusion i \nto content-based recommender systems, Proceedings of the third ACM conference on Recommender systems, pp.301-304, 2009.

M. Balabanovi?, Y. Shoham, and . Fab, Fab: content-based, collaborative recommendation, Communications of the ACM, vol.40, issue.3, pp.66-72, 1997.
DOI : 10.1145/245108.245124

T. Joachims, D. Freitag, T. Mitchell, and . Webwatcher, A tour guide for the world wide web, IJCAI, issue.1, pp.770-777, 1997.

M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes et al., « Combining content-based and collaborative filters in an online newspaper, Proceedings of ACM SIGIR workshop on recommender systems, 1999.

A. M. Ahmad-wasfi, Collecting user access patterns for building user profiles and collaborative filtering, Proceedings of the 4th international conference on Intelligent user interfaces , IUI '99, pp.57-64, 1998.
DOI : 10.1145/291080.291091

B. Smyth and P. Cotter, « A personalised TV listings service for the digital TV age », Knowl.-Based Syst, p.5359, 2000.

J. Salter and N. Antonopoulos, CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering, IEEE Intelligent Systems, vol.21, issue.1, pp.35-41, 2006.
DOI : 10.1109/MIS.2006.4

T. Qiu, G. Chen, Z. Zhang, and E. T. Zhou, An item-oriented recommendation algorithm on cold-start problem, EPL (Europhysics Letters), vol.95, issue.5, p.58003, 2011.
DOI : 10.1209/0295-5075/95/58003

A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. Mcnee et al., Riedl, « Getting to know you: learning new user preferences in recommender systems, Proceedings of the 7th international conference on Intelligent user interfaces, pp.127-134, 2002.

P. Massa and B. Bhattacharjee, Using Trust in Recommender Systems: An Experimental Analysis, Trust Management, pp.221-235, 2004.
DOI : 10.1007/978-3-540-24747-0_17

X. N. Lam, T. Vu, T. D. Le, and A. D. Duong, Addressing cold-start problem in recommendation systems, Proceedings of the 2nd international conference on Ubiquitous information management and communication , ICUIMC '08, pp.208-211, 2008.
DOI : 10.1145/1352793.1352837

S. Park and W. Chu, Pairwise preference regression for cold-start recommendation, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.21-28, 2009.
DOI : 10.1145/1639714.1639720

G. Shaw, Y. Xu, and E. S. Geva, Using Association Rules to Solve the Cold-Start Problem in Recommender Systems, Advances in Knowledge Discovery and Data Mining, pp.340-347, 2010.
DOI : 10.1007/978-3-642-13657-3_37

K. Zhou and S. Yang, Zha, « Functional matrix factorizations for cold-start recommendation, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp.315-324, 2011.

N. Golbandi, Y. Koren, and E. R. Lempel, Adaptive bootstrapping of recommender systems using decision trees, Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pp.595-604, 2011.
DOI : 10.1145/1935826.1935910

Z. Zhang, C. Liu, Y. Zhang, and E. T. Zhou, Solving the cold-start problem in recommender systems with social tags, EPL (Europhysics Letters), vol.92, issue.2, p.28002, 2010.
DOI : 10.1209/0295-5075/92/28002

H. Gao, J. Tang, and E. H. Liu, Addressing the cold-start problem in location recommendation using geo-social correlations, Data Mining and Knowledge Discovery, vol.2009, issue.1, pp.1-25, 2014.
DOI : 10.1007/s10618-014-0343-4

B. Lika and K. Kolomvatsos, Facing the cold start problem in recommender systems, Expert Systems with Applications, vol.41, issue.4, pp.2065-2073, 2014.
DOI : 10.1016/j.eswa.2013.09.005

G. Shani and A. Gunawardana, Evaluating Recommendation Systems, Recommender systems handbook, pp.257-297, 2011.
DOI : 10.1007/978-0-387-85820-3_8

P. Zigoris and Y. Zhang, Bayesian adaptive user profiling with explicit & implicit feedback, Proceedings of the 15th ACM international conference on Information and knowledge management , CIKM '06, pp.397-404, 2006.
DOI : 10.1145/1183614.1183672

J. Beel, S. Langer, M. Genzmehr, B. Gipp, and C. Breitinger, Nürnberger, « Research paper recommender system evaluation: a quantitative literature survey, Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, pp.15-22, 2013.

D. Bamber, The area above the ordinal dominance graph and the area below the receiver operating characteristic graph, Journal of Mathematical Psychology, vol.12, issue.4, pp.387-415, 1975.
DOI : 10.1016/0022-2496(75)90001-2

M. Montaner, B. López, J. L. De, and R. , Developing trust in recommender agents, Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 1, AAMAS '02, pp.304-305, 2002.
DOI : 10.1145/544741.544811

J. A. Konstan and J. , Recommender systems: from algorithms to user experience, User Modeling and User-Adapted Interaction, vol.21, issue.2, pp.101-123, 2012.
DOI : 10.1007/s11257-011-9112-x

N. Tintarev and J. Masthoff, A Survey of Explanations in Recommender Systems, 2007 IEEE 23rd International Conference on Data Engineering Workshop, pp.801-810, 2007.
DOI : 10.1109/ICDEW.2007.4401070

N. Tintarev and J. Masthoff, Effective explanations of recommendations, Proceedings of the 2007 ACM conference on Recommender systems , RecSys '07, pp.153-156, 2007.
DOI : 10.1145/1297231.1297259

J. Beel, S. Langer, and M. Genzmehr, Nürnberger, « Introducing Docear's research paper recommender system, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp.459-460, 2013.

T. Bogers and A. , Van den Bosch, « Recommending scientific articles using citeulike, Proceedings of the 2008 ACM conference on Recommender systems, pp.287-290, 2008.

M. Mönnich and M. Spiering, Adding Value to the Library Catalog by Implementing a Recommendation System, D-Lib Magazine, vol.14, issue.5/6, 2008.
DOI : 10.1045/may2008-monnich

J. Bollen, Van de Sompel, « An architecture for the aggregation and analysis of scholarly usage data, Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, pp.298-307, 2006.

K. Stock, A. Robertson, F. Reitsma, T. Stojanovic, M. Bishr et al., Ortmann, « eScience for Sea Science: A Semantic Scientific Knowledge Infrastructure for Marine Scientists », in e-Science, 2009. e-Science'09, Fifth IEEE International Conference on, pp.110-117, 2009.

E. Korczynski and . Others, « Finding Science with Science: Evaluating a Domain and Scientific Ontology User Interface for the Discovery of Scientific Resources, Trans. GIS, vol.17, issue.4, pp.612-639, 2013.

L. Rokach, P. Mitra, S. Kataria, W. Huang, and E. L. Giles, « A supervised learning method for context-aware citation recommendation in a large corpus, Invit. Speak. Anal. Perform. Top-K Retr. Algorithms, 1978.

Q. He, J. Pei, D. Kifer, P. Mitra, L. Giles et al., t see the forest for the trees?: a citation recommendation system « Learning multiple graphs for document recommendations Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach, Context-aware citation recommendation Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries Proceedings of the 17th international conference on World Wide Web Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence et L. Rokach, « Recommending citations: translating papers into references Proceedings of the 21st ACM international conference on Information and knowledge management, pp.421-430, 2000.

J. Gaillard, M. El-bèze, E. Altman, and E. Ethis, « Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems, Conference on Empirical Methods in Natural Language Processing, pp.1943-1947, 2013.

J. H. Kietzmann, B. S. Silvestre, I. P. Mccarthy, and L. F. Pitt, Unpacking the social media phenomenon: towards a research agenda, Journal of Public Affairs, vol.24, issue.5, pp.109-119, 2012.
DOI : 10.1002/pa.1412

K. Plangger, The power of popularity: how the size of a virtual community adds to firm value, Journal of Public Affairs, vol.56, issue.October, pp.145-153, 2012.
DOI : 10.1002/pa.1416

R. Chakrabarti and P. Berthon, Gift giving and social emotions: experience as content, Journal of Public Affairs, vol.20, issue.1, pp.154-161, 2012.
DOI : 10.1002/pa.1417

P. Longart, What drives word???of???mouth in restaurants?, International Journal of Contemporary Hospitality Management, vol.22, issue.1, pp.121-128, 2010.
DOI : 10.1108/09596111011013516

M. Khammash, G. H. Griffiths, «. Arrivederci, C. Com, and B. Bing, ???Arrivederci CIAO.com, Buongiorno Bing.com??????Electronic word-of-mouth (eWOM), antecedences and consequences, International Journal of Information Management, vol.31, issue.1, pp.82-87, 2011.
DOI : 10.1016/j.ijinfomgt.2010.10.005

J. H. Kietzmann and I. Angell, Generation-C: creative consumers in a world of intellectual property rights, International Journal of Technology Marketing, vol.9, issue.1, pp.86-98, 2014.
DOI : 10.1504/IJTMKT.2014.058085

A. M. Kaplan and M. Haenlein, Users of the world, unite! The challenges and opportunities of Social Media, Business Horizons, vol.53, issue.1, pp.59-68, 2010.
DOI : 10.1016/j.bushor.2009.09.003

J. Arndt and A. R. , Foundation, Word of mouth advertising: a review of the literature Advertising Research Foundation, 1967.

J. F. Engel, R. D. Blackwell, and R. J. , Kegerreis, « How information is used to adopt an innovation, J. Advert. Res, vol.9, issue.4, pp.3-8, 1969.

E. M. Steffes and L. E. Burgee, Social ties and online word of mouth, Internet Research, vol.19, issue.1, pp.42-59, 2009.
DOI : 10.1108/10662240910927812

F. A. Buttle, Word of mouth: understanding and managing referral marketing, Journal of Strategic Marketing, vol.6, issue.3, pp.241-254, 1998.
DOI : 10.1080/096525498346658

M. Huang, F. Cai, A. S. Tsang, and E. N. Zhou, Making your online voice loud: the critical role of WOM information, European Journal of Marketing, vol.45, issue.7/8, pp.1277-1297, 2011.
DOI : 10.1108/03090561111137714

D. L. Williams, V. L. Crittenden, T. Keo, and E. P. Mccarty, The use of social media: an exploratory study of usage among digital natives, Journal of Public Affairs, vol.19, issue.1, pp.127-136, 2012.
DOI : 10.1002/pa.1414

M. Bulearca, S. Bulearca, and . Twitter, a viable marketing tool for SMEs?, Glob. Bus. Manag. Res. Int. J, vol.2, issue.4, pp.296-309, 2010.

J. H. Kietzmann, K. Hermkens, I. P. Mccarthy, and B. S. Silvestre, Social media? Get serious! Understanding the functional building blocks of social media, Business Horizons, vol.54, issue.3, pp.241-251, 2011.
DOI : 10.1016/j.bushor.2011.01.005

J. Grimmelmann, « Facebook and the social dynamics of privacy, Iowa Law Rev, vol.95, issue.4, 2009.

F. Ricci, L. Rokach, and E. B. Shapira, Introduction to Recommender Systems Handbook, 2011.
DOI : 10.1007/978-0-387-85820-3_1

J. Golbeck, Generating Predictive Movie Recommendations from Trust in Social Networks, 2006.
DOI : 10.1007/11755593_8

D. Goldberg, D. Nichols, B. M. Oki, and E. 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

R. R. Sinha and K. Swearingen, Comparing Recommendations Made by Online Systems and Friends. », in DELOS workshop: personalisation and recommender systems in digital libraries, 2001.

H. Ma, H. Yang, M. R. Lyu, E. I. King, and . Sorec, social recommendation using probabilistic matrix factorization, Proceedings of the 17th ACM conference on Information and knowledge management, pp.931-940, 2008.

X. Yang, H. Steck, Y. Guo, and E. Y. Liu, On top-k recommendation using social networks, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.67-74, 2012.
DOI : 10.1145/2365952.2365969

H. Ma, I. King, and M. R. Lyu, Learning to recommend with social trust ensemble, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pp.203-210, 2009.
DOI : 10.1145/1571941.1571978

M. Jamali and M. Ester, A matrix factorization technique with trust propagation for recommendation in social networks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.135-142, 2010.
DOI : 10.1145/1864708.1864736

X. Yang, H. Steck, and E. Y. Liu, Circle-based recommendation in online social networks, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.1267-1275, 2012.
DOI : 10.1145/2339530.2339728

R. Pan, G. Xu, and E. P. Dolog, « Improving Recommendations in Tag-based Systems with Tag Neighbor Expansion, the Proceedings of the 3rd International Conference on Computer Science and its Applications (CSA-11), pp.12-15, 2011.

L. Yu, R. Pan, and Z. Li, Adaptive social similarities for recommender systems, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.257-260, 2011.
DOI : 10.1145/2043932.2043978

S. Yang, B. Long, A. Smola, N. Sadagopan, and Z. Zheng, Zha, « Like like alike: joint friendship and interest propagation in social networks, Proceedings of the 20th international conference on World wide web, pp.537-546, 2011.

J. Noel, S. Sanner, K. Tran, P. Christen, L. Xie et al., Della Penna, « New objective functions for social collaborative filtering, Proceedings of the 21st international conference on World Wide Web, pp.859-868, 2012.

C. Ziegler and G. Lausen, Analyzing Correlation between Trust and User Similarity in Online Communities, Trust management, pp.251-265, 2004.
DOI : 10.1007/978-3-540-24747-0_19

C. Ziegler and J. Golbeck, Investigating interactions of trust and interest similarity, Decision Support Systems, vol.43, issue.2, pp.460-475, 2007.
DOI : 10.1016/j.dss.2006.11.003

P. Massa and P. Avesani, « Controversial users demand local trust metrics: An experimental study on epinions. com community, Proceedings of the National Conference on artificial Intelligence, p.121, 2005.

P. Massa and B. Bhattacharjee, Using Trust in Recommender Systems: An Experimental Analysis, Trust Management, pp.221-235, 2004.
DOI : 10.1007/978-3-540-24747-0_17

P. Massa and P. Avesani, Trust-aware recommender systems, Proceedings of the 2007 ACM conference on Recommender systems , RecSys '07, pp.17-24, 2007.
DOI : 10.1145/1297231.1297235

P. Massa and P. Avesani, Trust-Aware Collaborative Filtering for Recommender Systems, On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, pp.492-508, 2004.
DOI : 10.1007/978-3-540-30468-5_31

T. Dubois, J. Golbeck, J. Kleint, and E. A. Srinivasan, « Improving recommendation accuracy by clustering social networks with trust, pp.1-8, 2009.

F. E. Walter, S. Battiston, and E. F. Schweitzer, A model of a trust-based recommendation system on a social network, Autonomous Agents and Multi-Agent Systems, vol.293, issue.1, pp.57-74, 2008.
DOI : 10.1007/s10458-007-9021-x

K. Sarda, P. Gupta, D. Mukherjee, S. Padhy, and E. H. Saran, « A distributed trust-based recommendation system on social networks, Proceedings of the 2nd IEEE Workshop on Hot Topics in Web Systems and Technologies, pp.1-6, 2008.

X. Yang, Y. Guo, E. Y. Liu, and . Bayesian, Bayesian-Inference-Based Recommendation in Online Social Networks, Online Social Networks, pp.642-651, 2013.
DOI : 10.1109/TPDS.2012.192

M. Jamali and M. Ester, Using a trust network to improve top-N recommendation, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.181-188, 2009.
DOI : 10.1145/1639714.1639745

M. A. Tayebi, M. Jamali, M. Ester, U. Glässer, E. R. Frank et al., a recommendation model for suspect investigation, Proceedings of the fifth ACM conference on Recommender systems, pp.173-180, 2011.

B. Schilit, N. Adams, and E. R. Want, « Context-aware computing applications, Mobile Computing Systems and Applications, 1994. WMCSA 1994. First Workshop on, pp.85-90, 1994.

A. K. Dey and G. D. Abowd, A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications, Human-Computer Interaction, vol.37, issue.3, pp.97-166, 2001.
DOI : 10.1109/98.626982

P. Prekop, M. Burnett, and . Activities, Activities, context and ubiquitous computing, Computer Communications, vol.26, issue.11, pp.1168-1176, 2003.
DOI : 10.1016/S0140-3664(02)00251-7

R. M. Gustavsen, « Condor?an application framework for mobility-based context-aware applications, Proceedings of the Workshop on Concepts and Models for Ubiquitous Computing, 2002.

P. Brézillon, Context and Explanation in e-Collaborative Work, Handb. Res. Methods Tech. Stud. Virtual Communities Paradig. Phenom, vol.1, p.285, 2010.
DOI : 10.4018/978-1-60960-040-2.ch016

P. T. Eugster and B. Garbinato, Holzer, « Middleware Support for Context-Aware Applications », Middlew, Netw. Eccentric Mob. Appl, vol.1, p.305, 2009.

P. Bellavista, A. Corradi, M. Fanelli, E. L. Foschini, and «. A. , A survey of context data distribution for mobile ubiquitous systems, ACM Computing Surveys, vol.44, issue.4, 2013.
DOI : 10.1145/2333112.2333119

A. Rakotonirainy, S. W. Loke, and E. P. Obst, Social Awareness Concepts to Support Social Computing, 2009 International Conference on Computational Science and Engineering, pp.223-228, 2009.
DOI : 10.1109/CSE.2009.314

M. Perttunen and J. Riekki, Lassila, « Context representation and reasoning in pervasive computing: a review, Int. J. Multimed. Ubiquitous Eng, vol.4, issue.4, 2009.

G. Chen and D. Kotz, « A survey of context-aware mobile computing research, 2000.

M. Baldauf, S. Dustdar, and E. F. Rosenberg, A survey on context-aware systems, International Journal of Ad Hoc and Ubiquitous Computing, vol.2, issue.4, pp.263-277, 2007.
DOI : 10.1504/IJAHUC.2007.014070

C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas et al., A survey of context modelling and reasoning techniques, Pervasive and Mobile Computing, vol.6, issue.2, pp.161-180, 2010.
DOI : 10.1016/j.pmcj.2009.06.002

P. Gray and D. Salber, Modelling and Using Sensed Context Information in the Design of Interactive Applications, Eng. Hum.-Comput. Interact, pp.317-335, 2001.
DOI : 10.1007/3-540-45348-2_26

A. K. Dey, Understanding and Using Context, Understanding and using context, pp.4-7, 2001.
DOI : 10.1007/s007790170019

S. Dobson and J. Ye, Using fibrations for situation identification, Pervasive 2006 workshop proceedings, pp.645-651, 2006.

G. Riva and G. Riva, Ambient Intelligence: the evolution of technology, communication and cognition towards the future of human-computer interaction, 2005.

A. Olaru, « A context-aware multi-agent system for AmI environments, 2010.

A. Ranganathan and R. H. Campbell, An infrastructure for context-awareness based on first order logic, Personal and Ubiquitous Computing, vol.7, issue.6, pp.353-364, 2003.
DOI : 10.1007/s00779-003-0251-x

J. Crowley, J. Coutaz, and G. Rey, Reignier, « Perceptual components for context aware computing, Ubiquitous Comput, pp.117-134, 2002.

J. Ye, S. Dobson, and E. S. Mckeever, Situation identification techniques in pervasive computing: A review, Pervasive and Mobile Computing, vol.8, issue.1, 2011.
DOI : 10.1016/j.pmcj.2011.01.004

G. Shafer, A mathematical theory of evidence, 1976.

J. Barwise and J. Perry, Situations and Attitudes, The Journal of Philosophy, vol.78, issue.11, 1983.
DOI : 10.2307/2026578

A. Zimmermann, M. Specht, and E. A. Lorenz, Personalization and Context Management, User Modeling and User-Adapted Interaction, vol.13, issue.2, pp.275-302, 2005.
DOI : 10.1007/s11257-005-1092-2

S. Sosnovsky and D. Dicheva, Ontological technologies for user modelling, Ontological technologies for user modelling, pp.32-71, 2010.
DOI : 10.1504/IJMSO.2010.032649

H. Prendinger and J. Mori, Ishizuka, « Recognizing, modeling, and responding to users' affective states », in User Modeling, pp.60-69, 2005.

J. Brézillon and P. Brézillon, « Context modeling: Context as a dressing of a focus », Model. Using Context, p, pp.136-149, 2007.

P. Brusilovsky and E. Millán, User Models for Adaptive Hypermedia and??Adaptive??Educational Systems, pp.3-53, 2007.
DOI : 10.1007/978-3-540-72079-9_1

X. Liu, K. Aberer, and . Soco, a social network aided context-aware recommender system, Proceedings of the 22nd international conference on World Wide Web, pp.781-802, 2013.

G. Adomavicius, Tuzhilin, « Context-aware recommender systems, Recommender systems handbook, pp.217-253, 2011.

S. Rendle, Z. Gantner, and C. Freudenthaler, Schmidt-Thieme, « Fast context-aware recommendations with factorization machines, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp.635-644, 2011.

L. Baltrunas, B. Ludwig, and E. F. Ricci, Matrix factorization techniques for context aware recommendation, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.301-304, 2011.
DOI : 10.1145/2043932.2043988

G. Adomavicius, R. Sankaranarayanan, and S. Sen, Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems, vol.23, issue.1, pp.103-145, 2005.
DOI : 10.1145/1055709.1055714

B. Xu, J. Bu, C. Chen, and E. D. Cai, An exploration of improving collaborative recommender systems via user-item subgroups, Proceedings of the 21st international conference on World Wide Web, WWW '12, pp.21-30, 2012.
DOI : 10.1145/2187836.2187840

A. Karatzoglou, X. Amatriain, L. Baltrunas, and E. N. Oliver, Multiverse recommendation, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.79-86, 2010.
DOI : 10.1145/1864708.1864727

L. R. Tucker, Some mathematical notes on three-mode factor analysis, Psychometrika, vol.64, issue.3, pp.279-311, 1966.
DOI : 10.1007/BF02289464

N. N. Liu, B. Cao, M. Zhao, and E. Q. Yang, Adapting neighborhood and matrix factorization models for context aware recommendation, Proceedings of the Workshop on Context-Aware Movie Recommendation, CAMRa '10, pp.7-13, 2010.
DOI : 10.1145/1869652.1869653

N. N. Liu, L. He, and M. Zhao, Social temporal collaborative ranking for context aware movie recommendation, ACM Transactions on Intelligent Systems and Technology, vol.4, issue.1, p.15, 2013.
DOI : 10.1145/2414425.2414440

B. M. Marlin and R. S. , Collaborative prediction and ranking with non-random missing data, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.5-12, 2009.
DOI : 10.1145/1639714.1639717

L. Denti, I. Barbopuolos, I. Nilsson, L. Holmberg, M. Thulin et al., « Sweden's Largest Facebook Study », Lisa Andén, and Emelie Davidsson, 2012.

T. Berners-lee, « Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by its Inventor, 1999.

J. Grimmelmann, « Facebook and the social dynamics of privacy, Iowa Law Rev, vol.95, issue.4, 2009.

M. D. Back, J. M. Stopfer, S. Vazire, S. Gaddis, S. C. Schmukle et al., Facebook Profiles Reflect Actual Personality, Not Self-Idealization, Psychological Science, vol.87, issue.3, pp.372-374, 2010.
DOI : 10.1016/j.jrp.2006.02.001

Y. Bachrach, M. Kosinski, T. Graepel, and P. Kohli, Stillwell, « Personality and Patterns of Facebook Usage, Proceedings of the 4th Annual ACM Web Science Conference

A. Java, X. Song, T. Finin, E. B. Tseng, . Why-we et al., Understanding Microblogging Usage and Communities, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp.56-65, 2007.

A. Beccera, S. Jimenez, and A. Gelbukh, « towards user profile based interfaces for exploration on large collections of items» in Proceddings of Rec Sys workshop, 2013.

X. Su and T. M. Khoshgoftaar, « A survey of collaborative filtering techniques », Adv, Artif. Intell, p.4, 2009.

X. Yang, Y. Guo, and Y. Liu, A survey of collaborative filtering based social recommender systems, Computer Communications, vol.41, pp.1-10, 2014.
DOI : 10.1016/j.comcom.2013.06.009

J. Tang, H. Gao, H. Liu, and A. Sarma, eTrust, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, 2012.
DOI : 10.1145/2339530.2339574

G. Tzeng and J. Huang, Multiple attribute decision making: methods and applications, 2011.

C. Yeh, A Problem-based Selection of Multi-attribute Decision-making Methods, International Transactions in Operational Research, vol.9, issue.2, pp.169-181, 2002.
DOI : 10.1111/1475-3995.00348

C. Zopounidis and M. Doumpos, Multicriteria classification and sorting methods: A literature review, European Journal of Operational Research, vol.138, issue.2, pp.229-246, 2002.
DOI : 10.1016/S0377-2217(01)00243-0

M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, « Collaborative filtering recommender systems », Found. Trends Hum, Comput. Interact, vol.4, issue.2, pp.81-173, 2011.

T. Dubois, J. Golbeck, J. Kleint, and E. A. Srinivasan, « Improving recommendation accuracy by clustering social networks with trust, pp.1-8, 2009.

R. Pan, G. Xu, and E. P. Dolog, « Improving Recommendations in Tag-based Systems with Tag Neighbor Expansion, the Proceedings of the 3rd International Conference on Computer Science and its Applications (CSA-11), pp.12-15, 2011.

X. Yang, H. Steck, and E. Y. Liu, Circle-based recommendation in online social networks, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.1267-1275, 2012.
DOI : 10.1145/2339530.2339728

J. Noel, S. Sanner, K. Tran, P. Christen, L. Xie et al., Della Penna, « New objective functions for social collaborative filtering, Proceedings of the 21st international conference on World Wide Web, pp.859-868, 2012.

B. Adrian, L. Sauermann, T. Roth-berghofer, and «. Contag, A semantic tag recommendation system, Proc. -Semant, pp.297-304, 2007.

G. Adomavicius, Tuzhilin, « Context-aware recommender systems, Recommender systems handbook, pp.217-253, 2011.

X. Yang, Y. Guo, Y. Liu, and E. H. Steck, A survey of collaborative filtering based social recommender systems, Computer Communications, vol.41, p.110, 2014.
DOI : 10.1016/j.comcom.2013.06.009

R. Burke, « Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction, vol.12, issue.4, pp.331-370, 2002.
DOI : 10.1023/A:1021240730564

B. Xu, J. Bu, C. Chen, and E. D. Cai, An exploration of improving collaborative recommender systems via user-item subgroups, Proceedings of the 21st international conference on World Wide Web, WWW '12, pp.21-30, 2012.
DOI : 10.1145/2187836.2187840

M. Chamsi-abu-quba, S. Rupert, and . Hassas, Search by Role: A New Paradigm to Search by Similar Users using Social Information, the Proceedings of IDIAS 2012
URL : https://hal.archives-ouvertes.fr/hal-01353169

R. Chamsi, S. Hassas, U. Fayyad, and H. Chamsi, From a " Cold " to a " Warm " Start in Recommender systems, the Proceeding of Enabling Technologies: Infrastructure for Collaborative Enterprises WETICE, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01301058

R. Chamsi, S. Hassas, U. Fayyad, and M. Alshomary, iSoNTRE: the Social Network Transformer into Recommendation Engine, the Proceeding of AICCSA, 2014.

R. Chamsi, S. Hassas, U. Fayyad, H. Chamsi, and C. Gertosio, iSoNTRE : transformateur intelligent de réseaux sociaux en environnement de moteur de recommendation, the book « Les moteurs et des systèmes recommandation, 2014.

S. Chamsi, U. Hassas, H. Fayyad, C. Chamsi, and . Gertosio, iSoNTRE : The Intelligent Social Network Transformer into Recommendation Engine framework " in the book, Recommander systems », ISTE and Wily, 2014.