基于辅助项目评分的协同过滤推荐算法  

Collaborative Filtering Recommendation Algorithm Based on Assistant Item Rating

作　　者：刘旭东[1] 房培玉[1] 陈德人[2] 

机构地区：[1]烟台职业学院信息工程系,山东烟台264670 [2]浙江大学计算机科学与技术学院,浙江杭州310027

出　　处：《武汉理工大学学报（信息与管理工程版）》2011年第1期111-114,共4页Journal of Wuhan University of Technology：Information & Management Engineering

基　　金：国家科技支撑计划基金资助项目(2008BAH21B03);山东省教育厅职成教育科研"十一五"规划基金资助项目(2010zcj104)

摘　　要：协同过滤技术是目前电子商务推荐系统中主要的技术之一,随着系统规模的日益扩大,其面临数据稀疏性和冷开始等问题。针对上述问题,提出了一种改进的协同过滤推荐算法。新算法是在传统协同过滤推荐算法的基础上增添辅助项目评分支持环节,加入辅助项目的评分信息对产品项目评分数据库进行补充。实验结果表明,改进的算法有效地解决了由于产品评分数据的稀疏而造成的推荐质量下降的问题,提高了推荐系统的推荐质量。对经济实力较弱的企业通过推荐系统推动电子商务的发展具有一定的参考价值。The collaborative filtering is one of the main technologies for the e-commerce recommendation systems.However,the sparsity of rating data and the cold-start were faced with the gradual increase of users and items.An improved collaborative filtering algorithm was employed to solve these problems.The modified algorithm is strengthened by an assistant item rating segment based on the original one.The user-item rating database can be upgraded by supplementing it with assistant items ratings information.It relieves the sparsity of rating data.The experiment results indicate that this algorithm could efficiently resolve these problems,and improve recommendation quality.The thesis is a valuable reference for the industry which economic strength is weaker to boost e-commerce business with the modified personalized recommend system.

关 键 词：推荐系统 协同过滤 冷开始 数据稀疏性 相似性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]