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Erschienen in:
2011 | OriginalPaper | Buchkapitel
4. Dynamic Recommendation in Collaborative Filtering Systems: A PSO Based Framework
verfasst von : Jing Yao, Bing Li
Erschienen in: Proceedings of the International Conference on Human-centric Computing 2011 and Embedded and Multimedia Computing 2011
Verlag: Springer Netherlands
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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.