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- W2360454550 abstract "As the traditional collaborative filtering recommendation algorithm(CFRA) having many problems,such as slow recommended speed and low recommend quality,an improved CFRA(ICFRA) is proposed based on the shuffled frog leaping fuzzy clustering algorithm.With the fuzzy C-means(FCM) clustering method,the user data is preprocessed,and the user data clustering center is presented,effectively reducing the workload of recommendation.Several clusters having the best similarity are selected to form the user neighbor set,and the shuffled frog leaping algorithm is used based on its fast global optimization ability to get the nearest neighbor set,which improves the accuracy of recommendation.Finally,recommendation results are generated by calculating the score.The simulation results show that,compared to the traditional collaborative filtering algorithm,this algorithm has better performance in the recommended speed and accuracy." @default.
- W2360454550 created "2016-06-24" @default.
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- W2360454550 date "2013-01-01" @default.
- W2360454550 modified "2023-09-24" @default.
- W2360454550 title "Collaborative Filtering Recommendation in E-commerce Based on Shuffled Frog Leaping Fuzzy Clustering Algorithm" @default.
- W2360454550 hasPublicationYear "2013" @default.
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