考虑数据稀疏性的图书推荐协同过滤算法仿真  

Simulation of Collaborative Filtering Algorithm for BookRecommendation Considering Data Sparsity

作　　者：贾丽坤[1] 赵亚丽 黄晓英 肖丹[1] JIA Li-kun;ZHAO Ya-li;HUANG Xiao-ying;XIAO Dan(Hebei University of Architecture,Zhangjiakou Hebei 075000,China;Department of Electronic Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]河北建筑工程学院,河北张家口075000 [2]清华大学电子工程系,北京100084

出　　处：《计算机仿真》2024年第4期470-474,共5页Computer Simulation

基　　金：河北省大中学生科技创新能力培育专项(202151001010544);河北省教育厅高校基本科研业务费项目(2022QNJS12)。

摘　　要：图书推荐算法易忽略数据稀疏性问题,导致推荐结果与用户感兴趣内容之间存在较大的偏差。在考虑数据稀疏性的基础上提出一种图书推荐协同过滤算法,对数据预处理,通过对用户和用户之间综合信任度分析,利用分布估计算法对用户兴趣建模;构建用户兴趣簇类集,划分用户兴趣,从中选择出与检索对象最接近的邻居;计算邻近项目得分,按照从大到小的顺序排列,排名靠前的资源项即为图书推荐结果。实验结果表明,所提方法在推荐500本图书时,用时在12s内,且降低了平均绝对误差和均方根误差,实现了最精准的图书推荐。At present,book recommendation algorithms tend to ignore the problem of data sparsity,leading to a large error between recommendation results and content.As a result,a collaborative filtering algorithm for book recommendation was proposed based on data sparsity.Firstly,the data was preprocessed.Based on the comprehensive trust analysis between users,the user interest was modeled by the distribution estimation algorithm.Secondly,a set of user interest clusters was constructed to divide user interests.Moreover,the neighbor closest to the retrieved object was selected.Furthermore,the scores of adjacent items were calculated and ranked in descending order.The top items were the book recommendation results.Experimental results show that the proposed algorithm takes less than 12s to recommend 500 books.Meanwhile,this algorithm reduces the average absolute error and root mean square error,thus achieving the most accurate book recommendation.

关 键 词：数据稀疏性 图书推荐 协同过滤算法 用户兴趣模型 综合信任度 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]