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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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item-based并行协同过滤推荐算法的设计与实现
Keywords: 推荐系统,协同过滤,hadoop,mapreduce
Abstract:
基于协同过滤的推荐已成为推荐系统中广泛采用的推荐技术.由于应用中用户数目和商品条目的日益增长,在计算相似度和计算预测时,单机集中式计算已不能满足推荐系统实时性和可扩展性的要求.针对这一问题,设计并实现了item-based并行协同过滤推荐算法.该算法采用hadoop的mapreduce与hdfs架构,可分为map与reduce两个过程.通过在map和reduce节点上的并行处理可提高算法的执行效率.实验结果表明,该算法可明显减少推荐时间,提高推荐实时性,获得良好的可扩展性.
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