基于社会网络协同过滤的社会化电子商务推荐研究

doi:10.3969／j.issn.1000-0801.2014.09.011

Abstract

Abstract:

With the development of the social network relation, whether the recommendation is successful, is not only depending on the characteristics of goods, but also influenced by social network relationship. Many users more trust from their friends'recommendation, rather than the machine recommended by single factor calculated results. Therefore, an E-commerce recommending system based on social network collaborative filtering was proposed. In the system, the crucial factors of social network relation intensity, interest preference intensity and production popularity with reputation intensity were set.And each first-level factor was composed of some second-level factors.Experimental results verify that social network relationships will affect users shopping behaviors and so on. In addition, the recommendation method based on social network is superior to other approaches and has better application effect.

Key words: social network relation intensity, interest preference intensity, reputation intensity, AHP, collaborative, filtering, E-commerce

Chunhua Ju,Fuguang Bao,Chonghuan Xu. Researcb on Social Network Collaborative Filtering Based E-commerce Recommending[J]. Telecommunications Science, 2014, 30(9): 82-86.

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References 16

1	中国互联网络信息中心. 第33次中国互联网络发展状况统计报告. , 2014
2	Ni Y , Xie L , Liu Q Z . Minimizing the expected complete influence time of asocial network. Information Sciences 2010, 180(1): 2514～2527
3	Klein A , Bhagat P . E-commerce: evidence on a new virtual commerce platform. Journal of Business Research,2010, 4(1): 107～124
4	Liang T P , Ho Y T , Li Y W , et al. What drives social commerce: the role of social support and relationship quality. International Journal of Electronic Commerce,2011, 16(1): 69～90
5	赵华 , 林政 , 方艾 , 等. 一种基于知识树的推荐算法及其在移动电子商务上的应用. 电信科学 2011,27(6)
6	陶彩霞 , 谢晓军 , 陈康 , 等. 基于云计算的移动互联网大数据用户行为分析引擎设计. 电信科学 2013,29(3): 30～35
7	Li Y , Lu L , Feng L X . A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce. Expert Systems with Applications,2005, 28(1): 67～77
8	Jeong B , Lee J , Cho H . Improving memory-based collaborative filtering via similarity updating and prediction modulation. Information Sciences,2010, 180(1): 602～612
9	琚春华 , 鲍福光 . 基于情境和主体特征融入性的多维度个性化推荐模型研究. 通信学报 2012,33(9A): 17～27
10	Pan W K , Yang Q . Transfer learning in heterogeneous collaborative filtering domains. Artificial Intelligence,2013, 197(1): 39～55
11	张海燕 , 孟祥武 . 基于社会标签的推荐系统研究. 情报理论与实践 2012,35(5): 103～106
12	孟祥武 , 胡勋 , 王立才 , 等. 移动推荐系统及其应用. 软件学报 2013,24(1): 91～108
13	Ranganathan C , Ganapathy S . Key dimensions of business-to-consumer web sites. Information＆Management,2002, 39(6): 457～465
14	张佩云 , 陈恩红 , 黄波 . 基于社会网络面向个性化需求的可信服务推荐. 通信学报 2013(12): 49～59
15	Hsia T L , Wu J H , Li Y E . The E-commerce value matrix and use case model:a goal-driven methodology for eliciting B2C application requirements. Information＆Management,2008, 45(5): 321～330
16	DeMeo P , Nocera A , Terracina G , et al. Recommendation of similar users,resources and social networks in a social internetworking scenario. Information Sciences,2010, 181(1): 1285～1305

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	好友数	周评论与分享量	网购数
好友数	1	0.577^*	0.417
周评论与分享量	0.577^*	1	0.651^*
网购数	0.417	0.651^*	1

综合指数（目标层）	评价指标	指标属性
综合推荐指数	社会网络关系推荐强度（SN_s）:0.316	社会网络关系推荐强度：0.316
	兴趣偏好强度（IP_s）:0.338	品牌偏好程度：0.096
		价格偏好程度：0.116
		信用偏好程度：0.126
	商品流行性与声望强度（PR_s）:0.346	近期销售量：0.112好评率：0.128
		同类排行：0.106

推荐策略	推荐前10项	推荐前20项
（SN_s+IP_s+PR_s）+AHP	10.71%	8.73%
SN_s+IP_s+PR_s	10.16%	8.10%
IP_s+PR_s	9.13%	8.06%
PR_s	7.30%	5.52%
SN_s	6.83%	5.28%

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Researcb on Social Network Collaborative Filtering Based E-commerce Recommending

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