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基于显式和隐式信息的协同过滤算法

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摘要 为解决数据稀疏性和冷启动问题,提出了一种改进的计算用户相似性的协同过滤推荐算法。该算法是首先将用户进行聚类,从而减少算法的计算量,降低数据稀疏性,提高推荐效率。在计算用户相似性时,并加入了聚类用户隐式信息相似性的计算,来提高推荐精度。实验结果表明该算法较传统的协同过滤算法具有更高的推荐质量,既降低了数据的稀疏性,又在一定程度上改善了数据的冷启动问题。
出处 《信息系统工程》 2018年第2期147-148,共2页
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