Abstract
With the sharp increment of information on the Internet, many technologies have been proposed to solve the problem of information explosion in people’s life. Collaborative Filtering (CF) recommendation system is one of the most popular and efficient ways of solutions, especially item based CF systems. While traditional item based CF recommendation algorithms either ignore the diversity of different users’ rating behavior or do not deal with it efficiently. In this paper, we present a novel similarity function using the average rating for each user instead of the overall average rating for all users. In order to find the optimal similarity function, we use genetic algorithm (GA) to optimize the weight vectors associated to the similarity function. A series of comparison experiments are conducted to demonstrate the effectiveness in terms of the quality of prediction of the proposed method.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (NSFC) projects No. 61202296 and No. 61300044, the National High-Technology Research and Development Program (“863” program) of China under Grant No. 2013AA01A212, the Natural Science Foundation of Guangdong Province project No. S2012030006242 and the Key Areas of Guangdong-HongKong Breakthrough project No. 2012A090200008.
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Xiao, J., Luo, M., Chen, JM., Li, JJ. (2015). An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_48
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DOI: https://doi.org/10.1007/978-3-319-22053-6_48
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