一种上下文感知的E-commerce评级大数据赋权方法

doi:10.3969/j.issn.1007-2861.2015.04.021

上海大学学报(自然科学版) ›› 2016, Vol. 22 ›› Issue (1): 36-44.doi: 10.3969/j.issn.1007-2861.2015.04.021

一种上下文感知的E-commerce评级大数据赋权方法

齐连永1,2, 窦万春1, 周毓明1

1. 南京大学计算机科学与技术系, 南京 210093; 2. 曲阜师范大学信息科学与工程学院, 山东日照 276826

收稿日期:2015-11-30 出版日期:2016-02-29 发布日期:2016-02-29
通讯作者: 窦万春(1971—),男,教授,博士生导师,研究方向为服务计算、大数据、云计算.E-mail: douwc@nju.edu.cn
基金资助:
国家自然科学基金资助项目(61402258); 江苏省重点研发计划资助项目(BE2015154); 国家电网公司科技资助项目; 中国博士后科学基金资助项目(2015M571739); 江苏省自然科学基金资助项目(BK20130014)

A context-aware weighting approach for big data of quality ratings in E-commerce

QI Lianyong1,2, DOU Wanchun1, ZHOU Yuming1

1. Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China; 2. School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, Shandong, China

Received:2015-11-30 Online:2016-02-29 Published:2016-02-29

摘要/Abstract

摘要：

电子商务(E-commerce)的飞速发展, 产生了大量针对商品的在线评级数据, 通过分析评级数据, 用户可以对商品的质量进行评估. 然而, 评级数据的海量性和差异性使得用户难以快速而准确地评估商品的质量. 鉴于此, 提出一种基于E-commerce 评级的上下文感知赋权方法(context-aware weighting approach, CWA), 以选出少数“重要”的评级数据并抛弃大多数“不重要”的评级数据, 从而确保商品质量评估的快速性和准确性. 最后, 通过一组实验验证了CWA 的有效性.

关键词: E-commerce, 大数据, 赋权, 上下文, 用户评级

Abstract:

With the fast development of E-commerce, large amounts of quality rating data for commodities are generated online. By analyzing the rating data, users can evaluate the commodities’quality. However, due to the massiveness and diversity of the rating data, it is a challenge for users to evaluate the commodity quality quickly and accurately. To this end, a context-aware weighting approach for E-commerce ratings, context-aware weighting approach (CWA) is proposed. With CWA, a few important rating data are selected and most unimportant data dropped. Thus the commodity quality can be evaluated quickly and accurately. A series of experiments validate effectiveness of the proposed CWA.

Key words: big data, context, E-commerce, user rating, weighting

齐连永1,2, 窦万春1, 周毓明1. 一种上下文感知的E-commerce评级大数据赋权方法[J]. 上海大学学报(自然科学版), 2016, 22(1): 36-44.

QI Lianyong1,2, DOU Wanchun1, ZHOU Yuming1. A context-aware weighting approach for big data of quality ratings in E-commerce[J]. Journal of Shanghai University（Natural Science Edition）, 2016, 22(1): 36-44.

参考文献

[1] Yin H Z, Cui B, Chen L, et al. Modeling location-based user rating profiles for personalized recommendation [J]. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): 1-41.
[2] Wan Y. The Matthew effect in online review helpfulness [C]//15th International Conference on Electronic Commerce. 2013: 38-49.
[3] Vickrey W. Measuring marginal utility by reactions to risk [J]. Econometrica, 1945, 13(4): 319-333.
[4] Wu Y, Yan C G, Ding Z J, et al. A novel method for calculating service reputation [J]. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 634-642.
[5] Miller G A. The magical number seven plus or minus two: some limits on our capacity for processing information [J]. Psychological Review, 1956, 63(2): 81-97.

[6] Gibbard A. A Pareto-consistent libertarian claim [J]. Journal of Economic Theory, 1974, 7(4): 388-410.
[7] Hu N, Kohb N S, Reddy S K. Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales [J]. Decision Support Systems, 2014, 57: 42-53.
[8] Minnesota University. MovieLens 1M Dataset [EB/OL]. [2015-11-10]. http://grouplens.org/datasets/movielens/.
[9] Qi L Y, Yang R T, Lin W M, et al. A QoS-aware web service selection method based on credibility evaluation [C]//IEEE International Conference on High Performance Computing and
Communications. 2010: 471-476.
[10] Song R, Li B, Wu X, et al. A preference and honesty aware trust model for web services [C]//APSEC. 2012: 61-66.
[11] Zhong Y, Fan Y, Huang K, et al. Time-aware service recommendation for mashup creation in an evolving service ecosystem [C]//IEEE International Conference on Web Services. 2014: 25-32.
[12] 龙军, 刘昕民, 袁鑫攀, 等. 一种基于信任推理与演化的Web 服务组合策略[J]. 计算机学报, 2012, 35(2): 298-314.
[13] Liu J, Bai Y J, Patil M, et al. Selecting a list of network user identifiers based on long-term and short-term history data: U.S.,US8527526 B1 [P]. 2013-09-03.

一种上下文感知的E-commerce评级大数据赋权方法

A context-aware weighting approach for big data of quality ratings in E-commerce

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