References
- Abouzakhar, N. and Bello Abdulazeez, M. (2009). A fingerprint matching model using unsupervised learning approach, 3rd International Conference on Cybercrime Forensics Education & Training, Canterbury, UK.
- Al-Shamri, M.Y.H. (2014). Power coefficient as a similarity measure for memory-based collaborative recommender systems, Expert Systems with Applications 41(13): 5680-5688.
- Ardissono, L., Gena, C., Torasso, P., Bellifemine, F., Difino, A. and Negro, B. (2004). User modeling and recommendation techniques for personalized electronic program guides, in L. Ardissono, A. Kobsa and M.T. Maybury (Eds.), Personalized Digital Television, Springer, Dordrecht, pp. 3-26.
- Bansal, N., Blum, A. and Chawla, S. (2004). Correlation clustering, Machine Learning 56(1-3): 89-113.
- Barragáns-Martínez, A.B., Costa-Montenegro, E., Burguillo, J.C., Rey-López, M., Mikic-Fonte, F.A. and Peleteiro, A. (2010a). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition, Information Sciences 180(22): 4290-4311.
- Barragáns-Martínez, A.B., Rey-López, M., Costa Montenegro, E., Mikic-Fonte, F.A., Burguillo, J.C. and Peleteiro, A. (2010b). Exploiting social tagging in a web 2.0 recommender system, IEEE Internet Computing 14(6): 23-30.
- Basu, C., Hirsh, H.and Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation, 15th National/10th Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, Madison, WI, USA, pp. 714-720.
- Burke, R. (2002). Hybrid recommender systems: Survey and experiments, User Modeling and User-adapted Interaction 12(4): 331-370.
- Cannon, R.L., Dave, J.V. and Bezdek, J. (1986). Efficient implementation of the fuzzy c-means clustering algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 8(2): 248-255.
- Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J.L. and García-Crespo, ´A. (2012). ReSySTER: A hybrid recommender system for scrum team roles based on fuzzy and rough sets, International Journal of Applied Mathematics and Computer Science 22(4): 801-816, DOI: 10.2478/v10006-012-0059-9.
- Di Noia, T., Mirizzi, R., Ostuni, V. C., Romito, D. and Zanker, M. (2012). Linked open data to support content-based recommender systems, Proceedings of the 8th International Conference on Semantic Systems, Graz, Austria, pp. 1-8.
- Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3(3): 32-57.
- Endert, H., Küster, T., Hirsch, B. and Albayrak, S. (2007). Mapping BPMN to agents: An analysis, Agents, Web-Services, and Ontologies Integrated Methodologies, Durham, UK, pp. 43-58.
- Ghazanfar, M.A. and Prugel-Bennett, A. (2010). A scalable, accurate hybrid recommender system, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD’10, Phuket, Thailand, pp. 94-98.
- Herlocker, J.L., Konstan, J.A., Borchers, A. and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, pp. 230-237.
- Herlocker, J.L., Konstan, J.A., Terveen, L.G. and Riedl, J.T. (2004). Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems 22(1): 5-53.
- Kurapati, K., Gutta, S., Schaffer, D., Martino, J. and Zimmerman, J. (2001). A multi-agent TV recommender, Proceedings of the UM 2001 Workshop on Personalization in Future TV, Sonthofen, Germany.
- Lau, R.Y. (2007). Towards a web services and intelligent agents-based negotiation system for B2B ecommerce, Electronic Commerce Research and Applications 6(3): 260-273.
- Li, H., Cai, F. and Liao, Z. (2012). Content-based filtering recommendation algorithm using HMM, Proceedings of the 4th International Conference on Computational and Information Sciences (ICCIS), Chongqing, China, pp. 275-277.
- Melville, P., Mooney, R.J. and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations, 18th National Conference on Artificial Intelligence, Edmonton, Alberta, Canada, pp. 187-192.
- Melville, P. and Sindhwani, V. (2010). Recommender systems, Encyclopedia of Machine Learning, Springer, New York, NY, pp. 829-838.
- Moreno, M.N., Segrera, S., López, V.F., Muñoz, M.D. and Sánchez, A.L. (2011). Mining semantic data for solving first-rater and cold-start problems in recommender systems, Proceedings of the 15th Symposium on International Database Engineering & Applications, IDEAS ’11, Lisbon, Portugal, pp. 256-257.
- Papagelis, M. and Plexousakis, D. (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents, Engineering Applications of Artificial Intelligence 18(7): 781-789.
- Pera, M.S. and Ng, Y.-K. (2013). A group recommender for movies based on content similarity and popularity, Information Processing & Management 49(3): 673-687.
- Ramappa, M.H. and Krishnamurthy, S. (2013). A comparative study of different feature extraction and classification methods for recognition of handwritten Kannada numerals, International Journal of Database Theory & Application 6(4): 71-90.
- Rey-López, M., Díaz-Redondo, R.P., Fernádez-Vilas, A. and Pazos-Arias, J.J. (2010). T-learning 2.0: A personalised hybrid approach based on ontologies and folksonomies, in F. Xhafa et al. (Eds.), Computational Intelligence for Technology Enhanced Learning, Berlin/Heidelberg, Springer, pp. 125-142.
- Rosaci, D. and Sarnè, G. (2013). Multi-agent technology and ontologies to support personalization in B2C e-commerce, Electronic Commerce Research and Applications 13(1): 13-23, DOI: 10.1016/j.elerap.2013.07.003.
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2002a). Incremental singular value decomposition algorithms for highly scalable recommender systems, Proceedings of the 5th International Conference on Computer and Information Technology, Dhaka, Bangladesh.
- Sarwar, B.M., Karypis, G., Konstan, J. and Riedl, J. (2002b). Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering, Proceedings of the 5th International Conference on Computer and Information Technology, Dhaka, Bangladesh.
- Shani, G.,Meisles, A., Gleyzer, Y., Rokach, L. and Ben-Shimon, D. (2007). A stereotypes-based hybrid recommender system for media items, Workshop on Intelligent Techniques for Web Personalization, Vancouver, Canada, pp. 76-83.
- Sollenborn, M. and Funk, P. (2002). Category-based filtering and user stereotype cases to reduce the latency problem in recommender systems, in S. Craw and A. Preece (Eds.), Advances in Case-Based Reasoning, Springer, Berlin/Heidelberg, pp. 395-405.
- Tulyakov, S., Jaeger, S., Govindaraju, V. and Doermann, D. (2008). Review of classifier combination methods, in S. Marinai and H. Fujisawa (Eds.), Machine Learning in Document Analysis and Recognition, Springer, Berlin/Heidelberg, pp. 361-386.
- Veloso, B., Sousa, L. and Malheiro, B. (2013). Personalised advertising supported by agents, in S. Omatu et al. (Eds.), Distributed Computing and Artificial Intelligence, Springer, Cham, pp. 473-481. Vemulapalli, S., Luo, X., Pitrelli, J.F. and Zitouni, I. (2009).
- Classifier combination techniques applied to coreference resolution, in U. Germann et al. (Eds.), Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student ResearchWorkshop and Doctoral Consortium, Association for Computational Linguistics, Stroudsburg, PA, pp. 1-6.
- Vozalis, M.G. and Margaritis, K.G. (2005). Applying SVD on item-based filtering, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, Wrocław, Poland, pp. 464-469.
- Winkler, R., Klawonn, F. and Kruse, R. (2011). Fuzzy c-means in high dimensional spaces, International Journal of Fuzzy System Applications 1(1): 1-16.
- Winkler, R., Klawonn, F. and Kruse, R. (2012). Problems of fuzzy c-means clustering and similar algorithms with high dimensional data sets, in W. Gaul et al. (Eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Springer, Berlin/Heidelberg, pp. 79-87.
- Wu, M.-L., Chang, C.-H. and Liu, R.-Z. (2014). Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices, Expert Systems with Applications 41(6): 2754-2761.
- Yanxiang, L., Deke, G., Fei, C. and Honghui, C. (2013). User-based clustering with top-n recommendation on cold-start problem, Proceedings of the 3rd International Conference on Intelligent System Design and Engineering Applications (ISDEA), Hong Kong, China, pp. 1585-1589.
- Zhang, L., Tao, Q. and Teng, P. (2014). An improved collaborative filtering algorithm based on user interest, Journal of Software 9(4): 999-1006.
- Zhang, Y. and Jiao, J. R. (2007). An associative classification-based recommendation system for personalization in B2c e-commerce applications, Expert Systems with Applications 33(2): 357-367.