融合贝叶斯推理与随机游走的好友推荐

doi:10.3969/j.issn.1000-5641.2018.04.008

华东师范大学学报(自然科学版) ›› 2018, Vol. 2018 ›› Issue (4): 80-89.doi: 10.3969/j.issn.1000-5641.2018.04.008

融合贝叶斯推理与随机游走的好友推荐

杨青¹, 王海洋¹, 卞梦阳¹, 张敬伟², 林煜明², 张会兵², 张海涛²

1. 桂林电子科技大学广西自动检测技术与仪器重点实验室, 广西桂林 541004;
2. 桂林电子科技大学广西可信软件重点实验室, 广西桂林 541004

收稿日期:2017-06-30 出版日期:2018-07-25 发布日期:2018-07-19
通讯作者: 张敬伟,男,博士,副教授,研究方向为海量数据管理.E-mail:gtzjw@hotmail.com E-mail:gtzjw@hotmail.com
作者简介:杨青,女,副教授,研究方向为智能信息处理.E-mail:gtyqing@hotmail.com
基金资助:
国家自然科学基金（61462017，61363005，U1501252，61662013）；广西自然科学基金（2017GXNSFAA198035，2014GXNSFAA118353，2014GXNSFAA118390）；广西自动检测技术与仪器重点实验室基金（YQ15110）；广西高校中青年教师基础能力提升项目（KY2016YB156）

Incorporating Bayesian inference with random walk for friend recommendations

YANG Qing¹, WANG Hai-yang¹, BIAN Meng-yang¹, ZHANG Jing-wei², LIN Yu-ming², ZHANG Hui-bing², ZHANG Hai-tao²

1. Guangxi Key Laboratory of Automatic Detection Technology and Instrument, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi 541004, China

Received:2017-06-30 Online:2018-07-25 Published:2018-07-19

摘要/Abstract

摘要： 随机游走是一种应对推荐应用中用户规模庞大、数据稀疏等问题的有效方法.鉴于社交网络用户间亲密度差异、反向社交影响力等因素对基于随机游走的推荐具有积极影响，提出了一种引入频繁项挖掘来计算用户社交亲密度，进而优化转移概率矩阵，并与局部反向游走相结合的随机游走改进模型.此外，为了有效利用用户属性信息，提出了一种用户潜在好友关系推断的贝叶斯推理模型，并与随机游走改进模型协同应用，进一步提升了好友推荐性能.真实数据集上的对比实验验证了提出算法的有效性.

关键词: 好友推荐, 随机游走, 贝叶斯推理

Abstract: Random walk is an effective strategy for dealing with a large user base as well as data sparsity in recommendation problems. However, the current work on recommendation problems do not take full account of the impact implied by both the intimacy difference between users and the reverse social influence. This paper presents an optimized friend recommendation model based on random walk, which introduces frequent pattern mining to capture user intimacy and to optimize the transition probability matrix,and is combined with local reverse search to implement recommendations. In order to make full use of users' attribute information, a Bayesian inference model is proposed for analyzing users' potential friend relationships and combined with random walk to provide better recommendation services. Experiments on real datasets demonstrated the effectiveness of the proposed method.

Key words: friend recommendations, random walk, Bayesian inference

中图分类号:

TP391

杨青, 王海洋, 卞梦阳, 张敬伟, 林煜明, 张会兵, 张海涛. 融合贝叶斯推理与随机游走的好友推荐[J]. 华东师范大学学报(自然科学版), 2018, 2018(4): 80-89.

YANG Qing, WANG Hai-yang, BIAN Meng-yang, ZHANG Jing-wei, LIN Yu-ming, ZHANG Hui-bing, ZHANG Hai-tao. Incorporating Bayesian inference with random walk for friend recommendations[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(4): 80-89.

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融合贝叶斯推理与随机游走的好友推荐

Incorporating Bayesian inference with random walk for friend recommendations

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