闫寒,,
曹高峰,
刘昕鸿
中南大学软件学院 ??长沙 ??410075
基金项目:国家自然科学基金(61572526)，中南大学研究生创新项目(502211708)
详细信息
作者简介:赵明：男，1957年生，博士后，教授，博士生导师，研究方向为群智感知、无线传感器网络等
闫寒：女，1991年生，硕士生，研究方向为无线网络和数据挖掘等
曹高峰：男，1991年生，硕士生，研究方向为推荐系统和数据挖掘等
刘昕鸿：男，1995年生，硕士生，研究方向为机器学习和数据挖掘等
通讯作者:闫寒 yanhan@csu.edu.cn
中图分类号:TP391.3计量
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被引次数:0
出版历程
收稿日期:2018-02-02
修回日期:2018-10-23
网络出版日期:2018-10-29
刊出日期:2019-01-01
Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity
Ming ZHAO,Han YAN,,
Gaofeng CAO,
Xinhong LIU
School of Software, Central South University, Changsha 410075, China
Funds:The National Natural Science Foundation of China (61572526), The Central South University Graduate Student Innovation Project (502211708)
摘要
摘要:推荐系统可以方便地帮助人们做出决策，然而，目前很少有研究考虑到剔除不相关噪声用户的影响，保留少量核心用户做推荐。该文提出基于信任关系和兴趣相似度的核心用户抽取的新方法。首先计算所有用户对之间的信任度和兴趣相似度并且排序，然后根据用户在最近邻列表中出现的频率和位置权重两种策略选择候选核心用户集合，最后利用用户的推荐能力筛选出最终的核心用户并且做推荐。实验表明利用核心用户做推荐的有效性，并且证明了利用20%的核心用户做推荐，可以达到超过90%的准确性，而且利用核心用户做推荐能很好地抵御托攻击对推荐系统造成的负面影响。
关键词:推荐系统/
核心用户/
鲁棒性/
相似度/
信任度
Abstract:Recommendation systems can help people make decisions conveniently. However, few studies consider the effect of removing irrelevant noise users and retaining a small number of core users to make recommendations. A new method of core user extraction is proposed based on trust relationship and interest similarity. First, all users trust and interest similarity between pairs are calculated and sorted, then according to the frequency and position weight users travel in the nearest neighbor in the list of two kinds of strategies for the selection of candidate core collection of users. Finally, according to the user’s ability the core users are sieved out. Experimental results show that the core user recommendation effectiveness, and verify that the core of user 20% can reach more than recommended accuracy of 90%, and through the use of core user recommendation the negative effects caused by the attacks on the recommendation system can be resisted.
Key words:Recommendation system/
Core users/
Robustness/
Similarity/
Trust
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