基于聚类的推荐算法综述  

Review of clustering-based recommendation algorithms

作　　者：殷锋[1] 曹旭 漆翔宇 YIN Feng;CAO Xu;QI Xiang-yu(School of Computer Science and Engineering,Southwest Minzu University,Chengdu 610041,China)

机构地区：[1]西南民族大学计算机科学与工程学院,四川成都610041

出　　处：《西南民族大学学报（自然科学版）》2021年第3期303-309,共7页Journal of Southwest Minzu University(Natural Science Edition)

基　　金：国家社会科学基金项目-重大招标项目(19ZDA284);四川省科技项目(2020JDR0141);四川省科技计划项目(2020JDRC0040)。

摘　　要：推荐系统作为解决"信息过载"问题的有效解决手段,其发展已在多方面取得了长足进步,该系统也已经应用于软件开发的众多层面.研究首先针对目前在该领域所存在的主要研究方向、发展脉络、存在问题等进行梳理,给出了推荐系统的主要分支;然后对已经面世的研究文献进行分析归纳,给出推荐算法中目前最为活跃的研究方向为"聚类"算法;最后针对基于聚类的推荐算法进行讨论并描述其结构,并给出该领域未来的研究趋势.As an effective way to solve the problem of "information overload", recommender system has made great progress in many aspects, and it has been applied in many aspects of software development. Firstly, the main research direction, development context and existing problems in this field are sorted out, and the main branches of recommendation system are given;Then, the published research literature was analyzed and summarized, and it is pointed out that the most active research direction of recommendation algorithm is "clustering" recommendation algorithm;Finally, the recommendation algorithm based on clustering is discussed, and the structure of clustering recommendation algorithm and future research trend in this field is given.

关 键 词：信息过载 推荐算法 聚类 算法综述 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]