Abstract
A recommender system is often perceived as an enigmatic entity that seems to guess our thoughts, and predict our interests. It is defined as a system capable of providing information to users according to their needs. It is enable them to explore data more effectively. There are several recommendation approaches and this domain remains to date an active research area that aims improving the quality of recommended contents. The main goal of this paper is to provide not only a global view of major recommender systems but also comparisons according to different specifications. We categorize and discuss their main features, advantages, limits and usages.
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References
Cliquet, G.: Innovation method in the Web 2.0 era. Dissertation, Arts et Métiers ParisTech (2010)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Bobadilla, J.E.S.U.S., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)
Miller, B.N., et al.: MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces. ACM (2003)
Billsus, D., et al.: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5), 34–38 (2002)
Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: InfoScale, vol. 152 (2006)
McNally, K., et al.: A case study of collaboration and reputation in social web search. ACM Trans. Intell. Syst. Technol. (TIST) 3(1), 4 (2011)
Bobadilla, J., et al.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Interac. 12(4), 331–370 (2002)
Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adap. Interac. 25(2), 99–154 (2015)
Ben Ticha, S.: Hybrid personalized recommendation. Dissertation, Université de Lorraine (2015)
Goldberg, D., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Wei, C.-P., Shaw, M.J., Easley, R.F.: Recommendation systems in electronic commerce. In: E-Service: New Directions in Theory and Practice, p. 168 (2002)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12
Lemdani, R.: Hybrid adaptation system in recommendation systems. Dissertation, Paris Saclay (2016)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)
Sharma, M., Mann, S.: A survey of recommender systems: approaches and limitations. Int. J. Innov. Eng. Technol. 2(2), 8–14 (2013)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Louëdec, J.: Bandit strategies for recommender systems. Dissertation, University Paul Sabatier-Toulouse III (2016)
Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1–2), 115–153 (2001)
Quba, R.C.A.: On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness. Dissertation, Université Claude Bernard-Lyon I (2015)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Lousame, F.P., Sánchez, E.: A taxonomy of collaborative-based recommender systems. In: Castellano, G., Jain, L.C., Fanelli, A.M. (eds.) Web Personalization in Intelligent Environments, pp. 81–117. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02794-9_5
Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley, Reading (2010)
Aggarwal, C.C.: Recommender Systems. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-29659-3
Zhang, F., et al.: Fast algorithms to evaluate collaborative filtering recommender systems. Knowl.-Based Syst. 96, 96–103 (2016)
Dias, C.E., Guigue, V., Gallinari, P.: Recommendation and analysis of feelings in a latent textual space. In: CORIA-CIFED (2016)
Hammou, B.A., Lahcen, A.A.: FRAIPA: a fast recommendation approach with improved prediction accuracy. Expert Syst. Appl. 87, 90–97 (2017)
Dias, C.-E., Guigue, V., Gallinari, P.: Recommendation and analysis of feelings in a latent textual space, Sorbonne University, UPMC Paris univ 06, UMR 7606, LIP6, F-75005 (2016)
Hammou, B.A., Lahcen, A.A., Aboutajdine, D.: A new recommendation algorithm for reducing dimensionality and improving accuracy. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 43–47 (2009)
Safoury, L., Salah, A.: Exploiting user demographic attributes for solving cold-start problem in recommender system. Lect. Notes Softw. Eng. 1(3), 303 (2013)
Wang, Y., Chan, S.C.-F., Ngai, G.: Applicability of demographic recommender system to tourist attractions: a case study on trip advisor. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 03. IEEE Computer Society (2012)
Sun, M., Li, C., Zha, H.: Inferring private demographics of new users in recommender systems. In: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems. ACM (2017)
Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_11
Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: Proceedings of the 10th International Conference on Electronic Commerce. ACM (2008)
Bridge, D., et al.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)
De Pessemier, T., Vanhecke, K., Martens, L.: A scalable, high-performance algorithm for hybrid job recommendations. In: Proceedings of the Recommender Systems Challenge. ACM (2016)
Strub, F., Gaudel, R., Mary, J.: Hybrid recommender system based on autoencoders. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM (2016)
Braunhofer, M., Codina, V., Ricci, F.: Switching hybrid for cold-starting context-aware recommender systems. In: Proceedings of the 8th ACM Conference on Recommender systems. ACM (2014)
Kouki, P., et al.: User preferences for hybrid explanations. In: Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM (2017)
Esparza, S.G., O’Mahony, M.P., Smyth, B.: Effective product recommendation using the real-time web. In: Bramer, M., Petridis, M., Hopgood, A. (eds.) Research and Development in Intelligent Systems XXVII, pp. 5–18. Springer, London (2011). https://doi.org/10.1007/978-0-85729-130-1_1
Meng, X., et al.: Exploiting emotion on reviews for recommender systems. AAAI (2018)
Zhang, S., et al.: Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2006)
Raghavan, S., Gunasekar, S., Ghosh, J.: Review quality aware collaborative filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems. ACM (2012)
Hariri, N., et al.: Context-aware recommendation based on review mining. In: Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2011) (2011)
Li, Y., et al.: Contextual recommendation based on text mining. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics (2010)
Musat, C-C., Liang, Y., Faltings, B.: Recommendation using textual opinions. In: IJCAI International Joint Conference on Artificial Intelligence, No. EPFL-CONF-197487 (2013)
Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1/2), 81–93 (1938)
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Srifi, M., Ait Hammou, B., Ait Lahcen, A., Mouline, S. (2018). A Concise Survey on Content Recommendations. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_31
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DOI: https://doi.org/10.1007/978-3-319-96292-4_31
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