基于主题模型的个性化图书推荐算法  

Personalized book recommendation algorithm based on topic model

作　　者：郑祥云[1] 陈志刚[1] 黄瑞[1] 李博[1] 

机构地区：[1]中南大学软件学院,长沙410075

出　　处：《计算机应用》2015年第9期2569-2573,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61379057;61309001;61272149;61103202);中南大学中央高校基本科研业务费专项资金资助项目(2015zzts228)

摘　　要：针对传统推荐算法精准度不高的问题,在潜在狄利克雷分布(LDA)主题挖掘模型的基础上提出了一种新的适用于图书推荐(BR)的数据挖掘模型——BR_LDA模型。通过对目标借阅者的历史借阅数据与其他图书数据进行内容相似度分析,得到与目标借阅者历史借阅图书内容相似度较高的其他图书。通过对目标借阅者的历史借阅数据及其他借阅者的历史借阅数据进行相似性分析,得到最近邻借阅者的历史借阅数据。通过求解图书被推荐的概率,最终得到目标借阅者潜在感兴趣的图书。特别地,当推荐数量为4 000时,BR_LDA模型比基于多特征方法和关联规则方法精准度分别提高了6.2%、4.5%;当推荐数量为500时,BR_LDA模型比协同过滤的近邻方法和矩阵分解方法分别提高了2.1%、0.5%。实验表明本模型能够更准确地向目标借阅者推荐历史感兴趣类别的新图书及潜在感兴趣的新类别的图书。Concerning the problem of high time complexity of traditional recommendation algorithms, a new recommendation model based on Latent Dirichlet Allocation （LDA） model was proposed. It was a data mining model applied to Book Recommendation （BR） in library management systems, named Book RecommendationLatent Dirichlet Allocation （ BR_ LDA） model. Through the content similarity analysis of historical borrowing data of the target borrowers with other books, other books which had high content similarities with historical borrowing books of the target borrowers were gotten. Through the similarity analyses performed on the target borrowers＇ historical borrowing data and historical data from other borrowers, historical borrowing data of the nearest neighbors were gotten. Books which the target borrowers were interested in could be finally gotten by calculating the probabilities of the recommended books. In particular, when the number of recommended books is 4000, the precision of BRLDA model is 6.2% higher than multi-feature method and 4.5% higher than association rule method; when the recommended list has 500 items, the precision of BR_LDA model is 2.1% higher than collaborative filtering based on the nearest neighbors and 0.5% higher than collaborative filtering based on matrix decomposition. The experimental results show that this model can efficiently mine data of books, reasonably recommend new books which belong to historical interested categories and new books in potential interested categories to the target borrowers.

关 键 词：图书推荐 图书管理系统 数据挖掘 推荐算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]