Abstract
Information filtering is one of the core technologies in a recommender system for personalized services. Each filtering technology has such shortcomings as new user problems and sparsity. Moreover, a recommender system dependent on items decreases reusability. In order to solve these problems, we developed a personalized recommender framework with hybrid filtering. This framework consists of reusable and flexible modules for recommended items. Further, this framework improves the productivity of programming. As an application of this framework, we implemented a personalized tourist recommender system and analyzed it. Also, we applied the system to Jeju beer recommender system. The results show the performance of the framework proposed in this paper.
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Cho, J., Kang, Ey., Kim, H., Kim, H., Lee, Y., Jeong, S. (2011). Hybrid Filtering-Based Personalized Recommender System for Revitalization of Jeju Water Industry. In: Luo, X., Cao, Y., Yang, B., Liu, J., Ye, F. (eds) New Horizons in Web-Based Learning - ICWL 2010 Workshops. ICWL 2010. Lecture Notes in Computer Science, vol 6537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20539-2_7
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DOI: https://doi.org/10.1007/978-3-642-20539-2_7
Publisher Name: Springer, Berlin, Heidelberg
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