Abstract
In collaborative filtering (CF) recommender systems, a user’s favorites usually can be captured while he rating or tagging a set of items in system, then a personalized recommendation can be given based on this user’s favorites. As the CF system growing, the user information it hosts may increase fast and updates frequently, which makes accurate and fast recommending in such systems become more difficult. In this article, a particle swarm optimization based recommending framework is introduced, which enhances the ability of traditional CF system to adapt dynamic updated user information in practice with steady and efficient performance. The experiments show the proposed framework is suitable for dynamic recommendation in CF system.
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Acknowledgments
This work is supported by the National Basic Research 973 Program of China under grant No. 2007CB310801, the National Natural Science Foundation of China under grant No. 60873083, 60803025, 60970017, 60903034, 61003073 and 60703018, the Natural Science Foundation of Hubei Province for Distinguished Young Scholars under grant No. 2008CDB351, the Young and Middle-aged Elitists’ Scientific and Technological Innovation Team Project of the Institutions of Higher Education in Hubei Province under grant No. T200902 and the Natural Science Foundation of Hubei Province under grant No. 2010CDB05601.
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Yao, J., Li, B. (2011). Dynamic Recommendation in Collaborative Filtering Systems: A PSO Based Framework. In: Park, J., Jin, H., Liao, X., Zheng, R. (eds) Proceedings of the International Conference on Human-centric Computing 2011 and Embedded and Multimedia Computing 2011. Lecture Notes in Electrical Engineering, vol 102. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2105-0_4
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DOI: https://doi.org/10.1007/978-94-007-2105-0_4
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