[1]熊才权,陈 曦.基于FP-Growth的社交好友推荐方法研究[J].湖北工业大学学报,2020,(1):33-37.
　XIONG Caiquan,CHENG Xi.Research on Friend Recommendation Method at Social Network Based on FP-Growth[J].,2020,(1):33-37.
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基于FP-Growth的社交好友推荐方法研究()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2020年第1期
页码:
33-37
栏目:
湖北工业大学学报
出版日期:
2020-02-28

文章信息/Info

Title:
Research on Friend Recommendation Method at Social Network Based on FP-Growth
文章编号:
1003-4684(2020)01-0033-05
作者:
熊才权;  陈   曦
湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
XIONG Caiquan; CHENG Xi
School of Computer Science, Hubei Univ. of Tech., 430068, China
关键词:
社交网络;  关注关系;  频繁模式;  FP-Growth
Keywords:
social network;  concern relationship;  data item;  FP-Growth
分类号:
TP18
文献标志码:
A
摘要:
针对基于关系的好友推荐中偏离共同兴趣以及推荐好友数量不足的问题，将数据挖掘中FP-Growth关联规则算法应用于社交网络好友推荐中，对用户间的相互关注关系进行深度挖掘，将不同用户同时被关注的事件作为一个项集，挖掘其频繁模式，再根据设定支持度，推荐用户感兴趣Top-N组合好友。63641条实验结果表明，算法具有良好的性能，可实现较高的召回率与准确率。
Abstract:
In view of the problem of deviation from common interests and insufficient number of recommended friends in relationship-based friend recommendation, FP-Growth association rule algorithm in data mining is applied to social network friend recommendation to conduct deep mining of mutual concern among users. Taking events that different users are concerned about at the same time as an item set, their frequent patterns are mined. Top-N group friends whom users are interested in are then recommended according to the set support degree. Experimental results show that the algorithm has good performance and can achieve high recall and accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2019-09-30
［基金项目］ 国家重点研发计划项目（2017YFC1405403）； 国家自然科学基金(61075059)； 湖北工业大学绿色工业科技引领计划项目（CPYF2017008）
［第一作者］ 熊才权(1966-)， 男， 湖北鄂州人，湖北工业大学教授，研究方向为模型识别与智能系统
［通信作者］ 陈   曦(1994-)， 男， 福建宁德人，湖北工业大学硕士研究生，研究方向为模型识别与智能系统
更新日期/Last Update: 2020-04-11