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interestingness

网络  趣味性; 有趣的; 兴趣度; 有趣性; 兴趣性

英英释义

noun

  • the power of attracting or holding one's attention (because it is unusual or exciting etc.)
    1. they said nothing of great interest
    2. primary colors can add interest to a room
    Synonym:interest

双语例句

  • The current personalized recommendation system is based on the correlation ship between users and products, that is, it predicts the interestingness of users to products according to the existing consuming behavior or the evaluations and recommend personally.
    当前个性化推荐系统是基于用户和产品之间的相关关系,即通过用户已有的购买行为或对产品的评价来预测用户对待推荐产品的兴趣度,进而进行个性化商品推荐。
  • Improve students 'interestingness and learning efficiency by introduce examples like the impoundment of reservoir, the most value of resistors, sales pricing etc. that is familiar to students into the teaching and the aesthetic value of inequalities.
    学生熟悉的水库蓄水、电阻最值、销售定价等关于不等式的生活实例融入课堂教学及向同学们介绍相关不等式的美学价值等方式以达到提高数学教学的趣味性,提高学习效率。
  • Those of us who tell ourselves we are curious about the world are actually swimming in evidence that has been filtered again and again in favour of interestingness.
    我们当中那些告诉自己我对世界充满好奇的人,其实是遨游在证据的海洋中,而这些证据早已经过一次又一次过滤,使其有意思。
  • After adding the subjective factors, it further improves the interestingness of the rules and reduces lots of unwanted or rubbish rules.
    增加主观性因素后能进一步提高规则的有趣性,减少一些无用,垃圾规则。
  • Mining Optimized Support and Interestingness Quantitative Association Rules
    挖掘支持度和兴趣度最优的数量关联规则
  • An Overview of Measures of Interestingness
    感兴趣度的研究综述
  • Lastly an algorithm of measuring interestingness of data mining schema based on user expectation and fuzzy logic is presented.
    最后对数据挖掘结果模式的价值进行了深入分析,利用模糊逻辑技术,在现有的兴趣度测量方式的基础上,介绍了一种新的模式兴趣度测量算法。
  • A measure of Association Rule Interestingness
    关联规则兴趣度的度量
  • WWW Personalized Information Retrieval Based on Measures of Interestingness
    基于感兴趣度的WWW个性化信息发现
  • The problem of discovering association rules consists of four elements: data set, the form of the rule, search algorithm, interestingness measure.
    关联规则发现问题可以归纳为四个要素:数据集、规则形式、搜索方法、兴趣度量。它们分别对应机器学习问题中的数据空间、假设空间、算法、评价标准。