计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 234-240.doi: 10.11896/jsjkx.200500136
刘忠慧1, 赵琦1, 邹璐1, 闵帆1,2
LIU Zhong-hui1, ZHAO Qi1, ZOU Lu1, MIN Fan1,2
摘要: 形式概念分析作为知识发现的方法,在理论分析和实际应用中已经取得很多成果。随着三维数据的涌现,许多学者开始了对三元形式概念分析的研究。但是,目前该领域的研究和应用较少,尤其还没有被应用到推荐系统。文中介绍了三元概念的构建及其社会化推荐应用。首先设计启发式信息,构造覆盖所有用户的三元概念集合,启发式信息旨在生成外延和内涵均有一定规模的强概念;然后根据拟推荐项目的属性来筛选用户合适的社会关系,并结合项目在概念中的流行度实现推荐预测。文中分别在真实数据集和抽样数据集中进行了3个实验。实验1对比了启发式方法和∨oc运算构造的三元概念数量及其运行时间,其中∨oc运算构造的概念数量少、耗时长且对推荐的提升效果不明显;实验2对比了推荐效果的精确度、召回率和F1值,揭示了增加条件可以有效提升推荐效果;实验3的结果表明,基于三元概念的推荐算法的推荐效果优于KNN及GRHC。
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