...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computers >A Novel Personalized Recommendation Algorithm for the Metrology Industry with Massive Sparse Data
【24h】

A Novel Personalized Recommendation Algorithm for the Metrology Industry with Massive Sparse Data

机译：大规模稀疏数据的计量行业新型个性化推荐算法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sparsity of source data sets is one major reason causing the poor recommendation quality. In order to solve this problem in the recommendation system of metrology industry with limited an unordered data, this paper proposes a novel personalized recommendation algorithm incorporating industry information and service category information to alleviate the influence of source data sparsity. First, the user's industry information and service category information are added to existing user-service preference data. Then, the K-means clustering algorithm is used to calculate the different user clusters. And then, the user-service preference matrix and the user-service category preference matrix are constructed separately from the user data in each cluster. And then, the nearest neighbor set of target user is calculated by the measure of cosine similarity. Finally, we use the user-based collaborative filtering algorithm to implement personalized recommendations for each user. Experimental results show that the proposed method can improve the recommendation accuracy rate in the metrology industry with sparse data set. The time to calculate for the nearest neighbor is shortened and the recommended speed is improved by reducing the nearest neighbor search range using clustering.
机译：源数据集的稀疏性是导致推荐质量较差的主要原因之一。为了解决数据量有限的计量行业推荐系统中的这一问题，提出一种结合行业信息和服务类别信息的新型个性化推荐算法，以减轻源数据稀疏性的影响。首先，将用户的行业信息和服务类别信息添加到现有的用户服务偏好数据中。然后，使用K均值聚类算法来计算不同的用户聚类。然后，与每个集群中的用户数据分别构建用户服务偏好矩阵和用户服务类别偏好矩阵。然后，通过余弦相似度的度量来计算目标用户的最近邻居集。最后，我们使用基于用户的协作过滤算法为每个用户实施个性化推荐。实验结果表明，该方法在数据稀疏的情况下可以提高计量行业的推荐准确率。通过使用聚类缩小最近邻居搜索范围，可以缩短最近邻居的计算时间，并提高推荐速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号