Manber, U., Patel, A. and Robison, J. (2000). Yahoo!. Communications of the ACM, Vol. 43, No. 8, p. 35.
 Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, Vol. 12, No. 4, pp. 331–370.
 Schafer, J. B., Konstan, J. and Riedi, J. (1999). Recommender systems in e-commerce. In
Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166.
 Vozalis, M. G. and Margaritis, K. G. (2007). Using SVD and demographic data for the enhancement of generalized collaborative filtering. Information Sciences, Vol. 177, No. 15, pp. 3017– 3037.
 Choi, K. and Suh, Y. (2013). A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowledge-Based Systems, Vol. 37, pp. 146–153.
 Schafer, J. B., Frankowski, D., Herlocker, J. and Sen, S. (2007). Collaborative filtering recommender systems. The adaptive web. Springer Berlin Heidelberg, pp. 291-324.
 Lee, W. S. (2001). Collaborative learning for recommender systems. In MACHINE LEARNING- INTERNATIONAL WORKSHOP THEN CONFERENCE-, pp. 314–321.
 Guo, G. (2013, August). Improving the performance of recommender systems by alleviating the data sparsity and cold start problems. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, AAAI Press, pp. 3217-3218.
 Lops, P., Degemmis, M. and Semeraro, G. (2007). Improving social filtering techniques through WordNet-Based user profiles. User Modeling 2007. Springer Berlin Heidelberg, pp. 268-277.
 Lika, B., Kolomvatsos, K. and Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, Vol. 41, No. 4, pp. 2065–2073.
 Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, Vol. 46, pp. 109–132.
 Liu, H., Hu, Z., Mian, A., Tian, H. and Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, Vol. 56, pp. 156–166.
 Sun, D., Luo, Z. and Zhang, F. (2011). A novel approach for collaborative filtering to alleviate the new item cold-start problem. In Communications and Information Technologies (ISCIT), 2011 11th International Symposium on, IEEE, pp. 402–406.
 Ghazanfar, M. A. and Prügel-Bennett, A. (2014). Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Systems with Applications, Vol. 41, No. 7, pp. 3261–3275.
 Su, X. and Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques.
Advances in Artificial Intelligence, p. 4.
 Koenigstein, N. and Koren, Y. (2013). Towards scalable and accurate item-oriented recommendations. In Proceedings of the 7th ACM conference on Recommender systems, pp. 419–422.
 Kumar, A. and Sharma, A. (2012). Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender Systems. In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA): Theory and Applications (FICTA), Springer, Vol. 199, p. 103.
 Herlocker, J. L., Konstan, J. A., Terveen, L. G. and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), Vol. 22, No. 1, pp. 5–53.
 Perugini, S., Gonçalves, M. A. and Fox, E. A. (2004). Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, Vol. 23, No. 2, pp. 107–143.
 Adomavicius, G. and Tuzhilin, A. (2005). 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, Vol. 17, No. 6, pp. 734–749.
 Candillier, L., Meyer, F. and Boullé, M. (2007). Comparing state-of-the-art collaborative filtering systems. In Machine Learning and Data Mining in Pattern Recognition, Springer, pp. 548– 562.
 Park, D. H., Kim, H. K., Choi, I. Y. and Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, Vol. 39, No. 11, pp. 10059–10072
 Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K. and Zhou, T. (2012). Recommender systems. Physics Reports, Vol. 519, No. 1, pp. 1–49.
 Pennock, D. M., Horvitz, E., Lawrence, S. and Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, pp. 473–480.
 Ma, H., Zhou, T. C., Lyu, M. R. and King, I. (2011). Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems (TOIS), Vol. 29, No. 2, p. 9.
 Wang, J., De Vries, A. P. and Reinders, M. J. (2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 501– 508.
 Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186.
 Breese, J. S., Heckerman, D. and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43–52.
 Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pp. 285–295.
 Deshpande, M. and Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), Vol. 22, No. 1, pp. 143–177.
 Ma, H., King, I. and Lyu, M. R. (2007). Effective missing data prediction for collaborative filtering. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 39–46.
 Li, Y., Lu, L. and Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple- interests and multiple-content recommendation in E-Commerce. Expert Systems with Applications, Vol. 28, No. 1, pp. 67–77.
 Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y. and Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 114–121.
 Hofmann, T. and Puzicha, J. (1999). Latent class models for collaborative filtering. In
International Joint Conference on Artificial Intelligence, Vol. 16, pp. 688–693.
 Vucetic, S. and Obradovic, Z. (2000). A regression-based approach for scaling-up personalized recommender systems in e-commerce. WEBKDD’00.
 Gong, S. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software, Vol. 5, No. 7, pp. 745–752.
 Kelleher, J. and Bridge, D. (2004). An accurate and scalable collaborative recommender.
Artificial Intelligence Review, Vol. 21, No. 3-4, pp. 193–213.
 Ungar, L. H. and Foster, D. P. (1998). Clustering methods for collaborative filtering. In AAAI Workshop on Recommendation Systems.
 Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M. (1999). Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR workshop on recommender systems, Vol. 60.
 Melville, P. and Sindhwani, V. (2010). Recommender systems. Encyclopedia of Machine Learning, Vol. 1, pp. 829–838.
 Blanco-Fernández, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M., García-Duque, J., Fernández-Vilas, A., Díaz-Redondo, R. P. and Bermejo-Muñoz, J. (2008). A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowledge-Based Systems, Vol. 21, No. 4, pp. 305-320.
 Chen, T. and He, L. (2009). Collaborative filtering based on demographic attribute vector. In
Future Computer and Communication, 2009. FCC’09. International Conference on, pp. 225–229.
 Yapriady, B. and Uitdenbogerd, A. L. (2005). Combining demographic data with collaborative filtering for automatic music recommendation. In Knowledge-Based Intelligent Information and Engineering Systems, pp. 201–207.
 Carrer-Neto, W., Hernández-Alcaraz, M. L., Valencia-García, R. and García-Sánchez, F. (2012). Social knowledge-based recommender system. Application to the movies domain. Expert Systems with Applications, Vol. 39, No. 12, pp. 10990–11000.
 Antoniou, G. and Harmelen, F. V. (2004). A semantic Web primer. MIT Press.
 Yang, R., Hu, W. and Qu, Y. (2013). Using Semantic Technology to Improve Recommender Systems Based on Slope One. In Semantic Web and Web Science, Springer, pp. 11–23.
 Shambour, Q. and Lu, J. (2011). A hybrid multi-criteria semantic-enhanced collaborative filtering approach for personalized recommendations. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on, Vol. 1, pp. 71–78.
 Peis, E., Morales-del-Castillo, J. M. and Delgado-López, J. A. (2008). Semantic Recommender Systems. Analysis of the state of the topic. Hipertext.net. Retrieved April 16, 2013, from http://www.upf.edu/hipertextnet/en/numero-6/recomendacion.html.
 Mobasher, B., Jin, X. and Zhou, Y. (2004). Semantically enhanced collaborative filtering on the web. In Web Mining: From Web to Semantic We, Springer, pp. 57–76.
 El-Dosuky, M. A., Rashad, M. Z., Hamza, T. T. and El-Bassiouny, A. H. (2012). Food Recommendation Using Ontology and Heuristics. In Advanced Machine Learning Technologies and Applications, Springer, pp. 423–429.
 Daramola, O., Adigun, M. and Ayo, C. (2009). Building an Ontology-based Framework for Tourism Recommendation Services. In Information and Communication Technologies in Tourism 2009, Springer, pp. 135–147.
 Blanco-Fernández, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., Barragáns- Martinéz, B., López-Nores, M., García-Duque, J., Fernández-Vilas, A. and Díaz-Redondo, R. P. (2004). AVATAR: An advanced multi-agent recommender system of personalized TV contents by semantic reasoning. In Web Information Systems–WISE 2004, Springer, pp. 415–421.
 Davoodi, E., Kianmehr, K. and Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied Intelligence, Vol. 39, No. 1, pp. 1–13.
 Middleton, S. E., Alani, H. and De Roure, D. C. (2002). Exploiting synergy between ontologies and recommender systems. arXiv Preprint Cs/0204012.
 Oldakowski, R. and Bizer, C. (2005). SemMF: A framework for calculating semantic similarity of objects represented as RDF graphs. In Poster at the 4th International Semantic Web Conference (ISWC 2005).
 Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, Vol. 5, No. 1, pp. 4–7.
 Abbar, S., Bouzeghoub, M. and Lopez, S. (2009). Context-aware recommender systems: A service-oriented approach. In VLDB PersDB workshop, pp. 1–6.
 Lee, D., Park, S. E., Kahng, M., Lee, S. and Lee, S. (2010). Exploiting contextual information from event logs for personalized recommendation. In Computer and Information Science 2010, Springer, pp. 121–139.
 Lee, J. S. and Lee, J. C. (2007). Context awareness by case-based reasoning in a music recommendation system. In Ubiquitous Computing Systems, Springer, pp. 45–58.
 Lee, J. S. and Lee, J. C. (2006). Music for my mood: A music recommendation system based on context reasoning. In Smart sensing and context, Springer, pp. 190–203.
 Balabanović, M. and Shoham, Y. (1997). Fab: content-based, collaborative recommendation.
Communications of the ACM, Vol. 40, No. 3, pp. 66–72.
 Smyth, B. and Cotter, P. (2000). A personalised TV listings service for the digital TV age.
Knowledge-Based Systems, Vol. 13, No. 2, pp. 53–59.
 Melville, P., Mooney, R. J. and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the National Conference on Artificial Intelligence, pp. 187–192.
 Su, X., Greiner, R., Khoshgoftaar, T. M. and Zhu, X. (2007). Hybrid collaborative filtering algorithms using a mixture of experts. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 645–649.
 Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B. and Riedl, J. (1998). Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of the 1998 ACM conference on Computer supported cooperative work, pp. 345–354.
 Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, Vol. 13, No. 5-6, pp. 393–408.
 Schwab, I., Kobsa, A. and Koychev, I. (2001). Learning user interests through positive examples using content analysis and collaborative filtering. Internal Memo, GMD, St. Augustin, Germany.
 Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic- Fonte, F. A. and Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, Vol. 180, No. 22, pp. 4290–4311.
 Basu, C., Hirsh, H. and Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the national conference on artificial intelligence, pp. 714–720.
 De Campos, L. M., Fernández-Luna, J. M., Huete, J. F. and Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, Vol. 51, No. 7, pp. 785–799.
 Condliff, M. K., Lewis, D. D., Madigan, D. and Posse, C. (1999). Bayesian mixed-effects models for recommender systems. In Proc. ACM SIGIR, Vol. 99.
 Song, R. P., Wang, B., Huang, G. M., Liu, Q. D., Hu, R. J. and Zhang, R. S. (2014). A hybrid recommender algorithm based on an improved similarity method. Applied Mechanics and Materials, Vol. 475, pp. 978–982.
 Xia, W., He, L., Gu, J. and He, K. (2009). Effective Collaborative Filtering Approaches Based on Missing Data Imputation. In INC, IMS and IDC, 2009. NCM’09. Fifth International Joint Conference on, pp. 534–537.
 Vozalis, M. and Margaritis, K. G. (2004). Enhancing collaborative filtering with demographic data: The case of item-based filtering. In 4th International Conference on Intelligent Systems Design and Applications, pp. 361–366.
 Burke, R. (1999). Integrating knowledge-based and collaborative-filtering recommender systems. In Proceedings of the Workshop on AI and Electronic Commerce, pp. 69–72.
 Tran, T. and Cohen, R. (2000). Hybrid recommender systems for electronic commerce. In Proc. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS- 00-04, AAAI Press.
 Towle, B. and Quinn, C. (2000). Knowledge based recommender systems using explicit user models. In Proceedings of the AAAI Workshop on Knowledge-Based Electronic Markets, pp. 74–77.
 Ceylan, U. and Birturk, A. (2011). Combining Feature Weighting and Semantic Similarity Measure for a Hybrid Movie Recommender System. In The 5th SNA-KDD Workshop’11.
 Blanco-Fernández, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M., García-Duque, J., Fernández-Vilas, A. and Díaz-Redondo, R. P. (2008). Exploiting synergies between
semantic reasoning and personalization strategies in intelligent recommender systems: A case study.
Journal of Systems and Software, Vol. 81, No. 12, pp. 2371–2385.
 Sieg, A., Mobasher, B. and Burke, R. (2010). Improving the effectiveness of collaborative recommendation with ontology-based user profiles. In proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 39–46.
 Hu, B. and Zhou, Y. (2008). Content Semantic Similarity Boosted Collaborative Filtering. In Computational Intelligence and Security, 2008. CIS’08. International Conference on, Vol. 2, pp. 7– 11.
 Jin, X. and Mobasher, B. (2003). Using semantic similarity to enhance item-based collaborative filtering. In Proceedings of The 2nd IASTED International Conference on Information and Knowledge Sharing, pp. 1–6.
 Adomavicius, G. and Tuzhilin, A. (2011). Context-aware recommender systems. In
Recommender Systems Handbook, Springer, pp. 217–253.
 Hayes, C. and Cunningham, P. (2004). Context boosting collaborative recommendations.
Knowledge-Based Systems, Vol. 17, No. 2, pp. 131–138.
 Chen, A. (2005). Context-aware collaborative filtering system: predicting the user’s preferences in ubiquitous computing. In CHI’05 extended abstracts on Human factors in computing systems, pp. 1110–1111.
 Zheng, Y., Burke, R. and Mobasher, B. (2012). Differential context relaxation for context- aware travel recommendation. In E-Commerce and Web Technologies, Springer, pp. 88–99.
 Tan, X. and Pan, P. (2012). A Contextual Item-Based Collaborative Filtering Technology.
Intelligent Information Management, Vol. 4, No. 3, pp. 85–88.
 Karatzoglou, A., Amatriain, X., Baltrunas, L. and Oliver, N. (2010). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, pp. 79–86.
 Oku, K., Nakajima, S., Miyazaki, J. and Uemura, S. (2006). Context-Aware SVM for Context-Dependent Information Recommendation. In 7th International Conference on Mobile Data Management, 2006. MDM 2006, pp. 109–109.
 Chuan, Y., Jieping, X. and Xiaoyong, D. (2006, August). Recommendation algorithm combining the user-based classified regression and the item-based filtering. In Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet, ACM, pp. 574–578.
 Moghaddam, S. G. and Selamat, A. (2011). A scalable collaborative recommender algorithm based on user density-based clustering. In 2011 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), pp. 246–249.
 Wang, Q., Yuan, X. and Sun, M. (2010). Collaborative filtering recommendation algorithm based on hybrid user model. In Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, Vol. 4, pp. 1985–1990.
 Zhang, D., Cao, J., Zhou, J., Guo, M. and Raychoudhury, V. (2009). An efficient collaborative filtering approach using smoothing and fusing. In Parallel Processing, 2009. ICPP’09. International Conference on, pp. 558–565.
 Sun, L., Hao, G., Li, J. and Lv, J. (2014). Cluster-Based Smoothing and Linear-Function Fusion for Collaborative Filtering. In Foundations of Intelligent Systems, Springer, pp. 681–692.
 Ji, H., Li, J., Ren, C. and He, M. (2013). Hybrid collaborative filtering model for improved recommendation. In Service Operations and Logistics, and Informatics (SOLI), 2013 IEEE International Conference on, pp. 142–145.
 Das, A. S., Datar, M., Garg, A. and Rajaram, S. (2007, May). Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, ACM, pp. 271–280.