摘要
目前已有各种推荐算法来解决互联网的信息过载问题,其中物质扩散算法和热传导算法是近年来颇受关注的两种推荐算法。物质扩散算法虽然具有较高的准确率,但推荐结果集中在少数热门物品,缺乏多样性,而热传导算法虽然具有较好的多样性,但准确率又明显偏低。为了解决这对矛盾,提出了一个混合算法,新算法在相似性计算模型上融合了两种传统算法的优点,增加了一个调节参数来抑制系统对热门物品的过度推荐。实验结果表明,在一定的参数条件下,新算法在准确率和多样性两指标上能够超越传统算法,并且该算法在平衡准确率和多样性这一对矛盾时表现得比传统算法更好。
Up to now,researchers developed many recommendation algorithms to solve information overloading.Mass diffusion and heat conduction are two popular algorithms which receive widely attention in recent years.However,there are still some defects for the two algorithms.For example,though the mass diffusion algorithm is of high accuracy,the diversity of the recommendation results is not good.Heat conduction algorithm is just the opposite,which is high in diversity,but low in accuracy.In order to solve the contradiction,this paper proposed a hybrid algorithm based on mass diffusion and heat conduction.The new algorithm not only mixed the advantages of two traditional algorithms,but also added an adjusting parameter to restrain the excessive recommendation of popular items.The experimental results show that the new algorithm does better in both indicators when proper parameter is set.Meanwhile,the system can reach a better balance between precision and diversity.
作者
周海平
沈士根
黄龙军
周洪波
Zhou Haiping;Shen Shigen;Huang Longjun;Zhou Hongbo(Dept.of Computer Science&Engineering,Shaoxing University,Shaoxing Zhejiang 312000,China;College of Mathematics&Information Science,Guiyang University,Guiyang 550005,China)
出处
《计算机应用研究》
CSCD
北大核心
2018年第11期3224-3227,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(11247286)
贵州省自然科学基金资助项目(LH[2014]7210
LH[2015]7294)
关键词
推荐算法
准确率
多样性
物质扩散
热传导
recommendation algorithm
precision
diversity
mass diffusion
heat conduction
