摘要
针对传统协同过滤算法难以解决数据稀疏性、冷启动及用户兴趣各异的问题,提出了基于加权二部图的个性化推荐方法,解决个性化设计方案推荐问题。采用加权二部图,基于用户特征和方案特征的评分,对用户和方案分类,减轻数据稀疏性,形成用户-方案规则库;采用加权网络的协同过滤算法,计算新用户特征与用户-方案规则库中用户特征的改进相似度,通过Top-N方法筛选高相似的方案集进行推荐,解决冷启动和用户兴趣各异的问题。最后与传统协同过滤算法、加权二部图个性化推荐进行比较,证明该方法的有效性和实用性。
As the most widely used recommendation algorithm,the traditional collaborative filtering can hardly solve the problems of data sparsity,cold start and different user interests.Aiming at these three problems,a personalized recommendation method based on weighted bipartite graphs was proposed to solve the problem of recommendation of personalized design scheme.A weighted bipartite graph was used to classify users and schemes based on user characteristics and scoring features to reduce the data sparsity and form a user-scheme rule base.A collaborative filtering algorithm based on weighted networks was used to calculate the improved similarity of user features between new user characteristics and user-scheme rules in the library,and recommended by the Top-N method to screen high similar solution sets to solve the problems of cold starting and different user interests.Finally,compared with the traditional collaborative filtering algorithm and weighted bipartite graph,the validity and practicability of the method were proved.
作者
杨珍
耿秀丽
YANG Zhen;GENG Xiuli(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《上海理工大学学报》
CAS
CSCD
北大核心
2019年第2期174-182,共9页
Journal of University of Shanghai For Science and Technology
基金
国家自然科学基金资助项目(71301104,71271138)
高等学校博士学科点专项科研基金资助项目(20133120120002)
上海市教委科研创新项目(14YZ088)
上海高校一流学科建设计划(S1201YLXK)
沪江基金资助项目(A14006)
关键词
加权二部图
加权网络
协同过滤算法
改进相似度
Top-N方法
weighted bipartite graph
weighted network
collaborative filtering algorithm
improvedsimilarity
Top-N method
作者简介
第一作者:杨珍(1994-),女,硕士研究生.研究方向:管理科学与工程等.E-mail:jenny_0513@163.com;通信作者:耿秀丽(1984-),女,副教授.研究方向:数据挖掘、个性化推荐等.E-mail:xiuliforever@163.com.
