基于社交网络中双重好友及用户偏好的协同过滤推荐  

Collaborative filtering recommendation algorithm based on double friends relationships and users preferences

作　　者：胡致杰 印鉴[2] 

机构地区：[1]广东理工学院信息工程系,广东肇庆526100 [2]中山大学数据科学与计算机学院,广东广州510006

出　　处：《长春工业大学学报》2016年第3期230-235,共6页Journal of Changchun University of Technology

基　　金：国家自然科学基金资助项目(61033010;61472453);广东省自然科学基金资助项目(S2013020012865);广东省科技计划项目(2013B090200006)

摘　　要：针对目前基于社交网络的协同过滤推荐算法只融入直接好友信息且不能有效防御概貌注入攻击等问题,提出一种融合双重好友及用户偏好的协同过滤推荐算法,通过设置合适的熟悉度阈值在社交网络的直接好友、间接好友中选取可信好友用户集作为目标用户K近邻候选集,在共同评分项目数的基础上,采用用户偏好相似度与评分相似度的加权相似度作为寻找近邻用户的标准,完成目标用户项目评分预测。在数据集Flixster上的实验结果表明,融合双重好友关系及用户偏好的推荐算法不仅具有较好的推荐准确率,还具有较强的抗概貌注入攻击能力。Collaborative filtering recommendation based on social network suffers the following problems：direct friends information is considered only,weaker capability of profile inj ection attack resistance.To address the problem,a collaborative filtering recommendation algorithm based on double friends’relationships and users’preferences （CF-DFP）is proposed.The trusty friend set is selected from direct friends and potential friends in social network by proper familiarity threshold and is considered as K nearest neighbor candidates （KNNC）.Meanwhile,weighted similarity combining users’preferences and ratings is used to find KNN in KNNC and predict ratings for users.Related＆amp;nbsp;experiment results on Flixster dataset show that the proposed algorithm can not only improve the accuracy of collaborative filtering,but also resists the profile inj ection attack effectively.

关 键 词：协同过滤 社交网络 双重好友 用户偏好 概貌注入攻击 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]