摘要
针对马太效应中过度流行性偏见问题,通过定义新的节点权重来初始化项目资源值,达到降低项目流行性的目的;进一步考虑用户可信度因素,结合统计学中的3σ原则,根据数据统计量筛选出系统中存在的异常用户或欺诈用户。在此基础上给出一个新的推荐算法(UTMT)。在数据集MovieLens_100K上对算法进行试验,并与资源分配中的热传导算法作比较,结果表明,构建的UTMT推荐算法预测结果的准确率较之热传导算法有较大的提升。
Aiming at the problem of excessive popularity bias in Matthew effect,a new node weight is defined to initialize the project resource value,so as to achieve the aim of reducing the project popularity.Further considering the user credibility factor and combining with the 3σprinciple of statistics,the abnormal users or fraudulent users existing in the system are filtered out according to the data statistics.On this basis,a new recommendation algorithm(UTMT)is proposed.Test the algorithm on the dataset MovieLens_100k and make a comparison with heat conduction algorithm in resource allocation.The final results show that the constructed recommendation algorithm UTMT has a big improvement in the accuracy rate of predict outcomes than that of the heat conduction algorithm.
作者
迟露阳
CHI Luyang(College of Sciences,Northeastern University,Shenyang 110004,China)
出处
《现代信息科技》
2021年第8期127-129,共3页
Modern Information Technology
关键词
二分图
马太效应
用户可信度
资源分配
推荐算法
bipartite graph
Matthew effect
user credibility
resource allocation
recommendation algorithm
作者简介
迟露阳(1996-),女,蒙古族,内蒙古赤峰人,硕士研究生,研究方向:推荐系统、数据分析。
