基于相似度和信任度的关联规则微博好友推荐

doi:10.11772/j.issn.1001-9081.2016.08.2262

计算机应用 ›› 2016, Vol. 36 ›› Issue (8): 2262-2267.DOI: 10.11772/j.issn.1001-9081.2016.08.2262

基于相似度和信任度的关联规则微博好友推荐

王涛¹, 覃锡忠¹, 贾振红¹, 牛红梅², 曹传玲²

1. 新疆大学信息科学与工程学院, 乌鲁木齐 830046;
2. 中国移动通信集团新疆有限公司网络监控部, 乌鲁木齐 830063

收稿日期:2016-02-02 修回日期:2016-03-17 出版日期:2016-08-10 发布日期:2016-08-10
通讯作者: 覃锡忠
作者简介:王涛(1992-),男,江苏无锡人,硕士研究生,主要研究方向:数据挖掘、复杂网络;覃锡忠(1963-),男,重庆长寿人,副教授,硕士,主要研究方向:机器学习、数字信号处理;贾振红(1964-),男,河南洛阳人,教授,博士,主要研究方向:光通信技术;牛红梅(1972-),女,山东泰安人,硕士,主要研究方向:数据监视与分析;曹传玲(1980-),女,江苏苏州人,硕士,主要研究方向:网络用户行为分析、网络规划。
基金资助:
中国移动通信集团新疆有限公司研究发展基金项目（XJM2013-2788）。

Association rules recommendation of microblog friend based on similarity and trust

WANG Tao¹, QIN Xizhong¹, JIA Zhenhong¹, NIU Hongmei², CAO Chuanling²

1. School of Information Science and Engineering, Xingjiang University, Urumqi Xinjiang 830046, China;
2. Department of Network Monitoring, China Mobile Group Xinjiang Company Limited, Urumqi Xinjiang 830063, China

Received:2016-02-02 Revised:2016-03-17 Online:2016-08-10 Published:2016-08-10
Supported by:
This work is partially supported by the Research and Development Program of China Mobile Group Xinjiang Co., Ltd. (XJM2013-2788).

摘要/Abstract

摘要： 针对关联规则个性化好友推荐中规则挖掘效率及推荐有效性不高的问题，首先提出基于散列及位图的改进关联规则算法BHA。该算法通过引入散列技术，减少了频繁2项集挖掘所需的时间；利用位图及相关性质，压缩无关候选项，减少了数据集所需的遍历次数。另外，在BHA的基础上，提出基于相似度及信任度的推荐算法STA，利用出、入相似度定义信任度，有效解决了新浪微博未提供显示信任关系的问题，同时弥补了相似度推荐未考虑用户间远近层次关系的缺陷。采集新浪微博用户数据进行实验，在关联规则挖掘效率的对比上，BHA挖掘所需的平均时间仅为改进AprioiriTid算法的47%；在好友推荐的有效性上，推荐算法STA较SNFRBOAR算法在准确率及召回率上分别提升了15.2%和9.8%。实验结果表明，STA能够有效降低规则挖掘所需的平均时间，并使实际好友推荐的有效性得到提升。

关键词: 好友推荐, 关联规则, 出相似度, 入相似度, 信任度

Abstract: Since the efficiency of rule mining and validity of recommendation are not high in personalized friends recommendation based on association rules, an improved association rule algorithm based on bitmap and hashing, namely BHA, was proposed. The mining time of frequent 2-itemsets was decreased by introducing hashing technique in this algorithm, and the irrelevant candidates were compressed to decrease the traversal of data by using bitmap and relevant properties. In addition, on the basis of BHA, a friend recommendation algorithm named STA was proposed based on similarity and trust. The problem of no displayed trust relationship in microblog was resolved effectively through trust defined by similarity of out-degree and in-degree; meanwhile, the defect of the similarity recommendation without considering users' hierarchy distance was remedied. Experiments were conducted on the user data of Sina microblog. In the comparison experiment of digging efficiency, the average minging time of BHA was only 47% of the modified AprioriTid; in the comparison experiment of availability in friend recommendation with SNFRBOAR (Social Network Friends Recommendation algorithm Based On Association Rules), the precision and recall of BHA were increased by 15.2% and 9.8% respectively. The theoretical analysis and simulation results show that STA can effectively decrease average time of mining rules, and improve the validity of friend recommendation.

Key words: friend recommendation, association rule, similarity of out-degree, similarity of in-degree, trust

中图分类号:

TP181

王涛, 覃锡忠, 贾振红, 牛红梅, 曹传玲. 基于相似度和信任度的关联规则微博好友推荐[J]. 计算机应用, 2016, 36(8): 2262-2267.

WANG Tao, QIN Xizhong, JIA Zhenhong, NIU Hongmei, CAO Chuanling. Association rules recommendation of microblog friend based on similarity and trust[J]. Journal of Computer Applications, 2016, 36(8): 2262-2267.

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基于相似度和信任度的关联规则微博好友推荐

Association rules recommendation of microblog friend based on similarity and trust

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