检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:李艳萍[1] 于丽梅[1] 刘国丽[1] 鲁鹏[1] LI Yah-ping, YU Li-mei, LIU Guo-li, LU Peng (Langfang Campus, Hebei University of Technology, Langfang 065000, China)
出 处:《电脑知识与技术》2015年第3期261-263,共3页Computer Knowledge and Technology
基 金:河北省高等学校科学技术研究重点项目(ZD20131070);廊坊市科技支撑计划项目(2014011036)
摘 要:随着互联网的高速发展,网络信息的规模越来越大,为了更高效的为用户提供信息服务,运用协同过滤算法的个性化推荐技术正广泛的应用于各类网络信息服务系统。然而协同过滤算法存在着冷启动、数据稀疏等问题,影响了系统的推荐精度。为了改善这一问题,本文在对传统的协同过滤算法分析研究的基础上,提出了一种改进方案,将基于项目的协同过滤与基于用户的协同过滤技术联合运用,通过相关反馈和查询扩展的信息扩展方法,在一定程度上缓解了用户一项目评分矩阵数据稀疏性问题,与传统算法相比提高了推荐准确度。With the rapid development of the Internet, there are more and more network information. In order to provide a more ef- ficient information services, collaborative filtering based on personalized recommendation technology is widely used in various types of network service system. However, there are some problems with collaborative filtering algorithms, such as cold start, data sparse, these affect the accuracy of the information which systems is recommended. In this paper we studied the principles of col- laborative filtering algorithm, proposed an method to improve the accuracy. It uses item-based collaborative filtering with user- based collaborative filtering, expands the information by relevance feedback and query methods, and it raise the recommendation accuracy than traditional algorithms.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.28