电子商务中的商品推荐系统

摘要/Abstract

摘要： 对于迅速崛起的各种电子商务网站来说，为了促进网站发展和增加经济效益，吸引新客户并留住老客户是一种有效的手段.设计和实现高效的商品推荐算法是各大网站最为关注的技术之一.在电子商务网站中常见的一种推荐方式是以广告的形式在边栏推荐商品.目前，商品推荐系统根据推荐算法分为基于内容、协同过滤和混合的推荐系统.然而，现有推荐算法在电子商务网站的实际应用中正面临挑战，包括推荐结果的多样化、个性化和智能化以及时效化.现有算法需要不断改进来解决这些问题，从而完善电子商务推荐系统.

关键词: 电子商务, 推荐系统, 多样化, 个性化, 智能化, 时效化

Abstract: For e-commerce sites, in order to promote the development and win more benefits, attracting and keeping the customers becomes very important. One of the most useful technologies is recommendation algorithms. In e-commerce sites, sidebar advertising is a common form of recommendation, which can be divided into three main categories: content-based, collaborative filtering and hybrid recommendation algorithms. However, current recommendation algorithms are challenged by new application requirements, such as diversification, personalization, intelligentization and timeliness. It is urgent to design new algorithms to meet these requirements.

Key words: e-commerce, recommendation systems, diversification, personalization, intelligentization, timeliness

中图分类号:

TP31

余文喆, 张蓉, 王立. 电子商务中的商品推荐系统[J]. 华东师范大学学报(自然科学版), 2013, 2013(3): 46-53.

YU Wen-zhe, ZHANG Rong, WANG Li. Recommendation in E-commerce[J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 46-53.

参考文献

［1］ WEN H J, CHEN H G, HWANG H G. E-commerce web site design: strategies and models［J］. Information Management & Computer Security, 2001, 9(1): 5-12.

［2］ ARENS W F. 当代广告学［M］. 8版.北京：人民邮电出版社, 2006.

［3］ EBAY. Online Retail Media［EB/OL］. 2012. http://www2.ebayadvertising.com/uk/online-retail-media.

［4］ HABEGGER J. Why Amazon is about to Become a Force in Online Advertising［EB/OL］. 2011. http://www.commercialalert.org/issues/culture/internet-socialmedia/why-amazon-is-about-to-become-a-force-in-online-advertising.

［5］ O’REILLY T. The Convergence of Advertising and E-Commerce［EB/OL］. 2010. http://radar.oreilly.com/2010/02/convergence-advertising-mobile-ecommerce.html.

［6］ SCHAFER J B, KONSTAN J, RIEDI J. Recommender systems in e-commerce［C］. Proceedings of the 1st ACM conference on Electronic commerce, 1999: 158-166.

［7］ ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions［J］. Knowledge and Data Engineering, 2005, 17(6): 734-749.

［8］ BAEZA-YATES R, RIBEIRO-NETO B. Modern Information Retrieval［M］. ［S.l.］ Addison-Wesley, 1999.

［9］ MOONEY R J, ROY L. Content-based book recommending using learning for text categorization［C］. Proceedings of the fifth ACM conference on Digital libraries, 2000: 195-204.

［10］ GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry［J］. Communications of the ACM, 1992, 35(12): 61-70.

［11］ RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews［C］. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, 1994: 175-186.

［12］ SHARDANAND U, MAES P. Social information filtering: algorithms for automating “word of mouth”［C］. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 1995: 210-217.

［13］ LINDEN G, SMITH B, YORK J. Amazon.com recommendations: item-to-item collaborative filtering［J］. Internet Computing, IEEE, 2003, 7(1): 76-80.

［14］ NETFLIX. The Netflix Prize［EB/OL］. 2009. http://www.netflixprize.com.

［15］ RODGERS J L, NICEWANDER W A. Thirteen ways to look at the correlation coefficient［J］. American Statistician, 1988, 42(1): 59-66.

［16］ BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering［C］. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998: 43-52.

［17］ DESHPANDE M, KARYPIS G. Item-based top-N recommendation algorithms［J］. ACM Transactions on Information Systems, 2004, 22(1): 143-177.

［18］ CLAYPOOL M, GOKHALE A, MIRANDA T, et al. Combining content-based and collaborative filters in an online newspaper［C］. Proceedings of ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, 1999.

［19］ BALABANOVIC M, SHOHAM Y. Fab: content-based, collaborative recommendation［J］. Communications of the ACM, 1997, 40(3): 66-72.

［20］ BASU C, HIRSH H, COHEN W. Recommendation as classification: using social and content-based information in recommendation［C］. Proceedings of the 15th national/10th conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, 1998: 714-720.

［21］ AGRAWAL R, GOLLAPUDI S, HALVERSON A, et al. Diversifying search results［C］. Proceedings of the Second ACM International Conference on Web Search and Data Mining, 2009.

［22］ ZIEGLER C N, MCNEE S M, KONSTAN J A, et al. Improving recommendation lists through topic diversification［C］. Proceedings of the 14th international conference on World Wide Web, 2005: 22-32.

［23］ YU C, LAKSHMANAN L, AMER-YAHIA S. It takes variety to make a world: diversification in recommender systems［C］. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, 2009: 368-378.

［24］ BOIM R, MILO T, NOVGORODOV S. DiRec: Diversified recommendations for semantic-less Collaborative Filtering［C］. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, 2011: 1312-1315.

［25］ ADOMAVICIUS G, TUZHILIN A. Personalization technologies: a process-oriented perspective［J］. Communications of the ACM, 2005, 48(10): 83-90.

［26］ VAN DER HEIJDEN H, KOTSIS G, KRONSTEINER R. Mobile Recommendation Systems for Decision Making ‘On the Go’［C］. Proceedings of the International Conference on Mobile Business, 2005: 137-143.

[1]	冯路平, 施力业, 吴雯, 郑骏, 胡文心, 郑巍. 基于偏好级别陌生人信息辅助的推荐系统[J]. 华东师范大学学报（自然科学版）, 2024, 2024(2): 53-64.
[2]	陈道佳, 陈志云. 分层级描述感知的个性化推荐系统[J]. 华东师范大学学报（自然科学版）, 2023, 2023(6): 73-84.
[3]	朱思涵, 浦剑. 基于序列特征的点击率预测模型[J]. 华东师范大学学报（自然科学版）, 2020, 2020(4): 134-146.
[4]	王凯, 李博涵, 万朔, 张安曼, 关东海. 基于反向最远邻的商品推荐算法研究[J]. 华东师范大学学报(自然科学版), 2019, 2019(3): 63-77.
[5]	汤路民, 余若男, 董启文, 洪道诚, 傅云斌. 基于非干预式感知的个性化学业求助资源推荐研究进展[J]. 华东师范大学学报(自然科学版), 2018, 2018(5): 17-29.
[6]	张巍, 郑骏, 逄娇娜, 白玥. 基于自相似矩阵的协同过滤推荐算法[J]. 华东师范大学学报(自然科学版), 2018, 2018(4): 120-128,146.
[7]	宋春芝, 董晓蕾, 曹珍富. 高效可验证的隐私保护推荐系统[J]. 华东师范大学学报(自然科学版), 2018, 2018(2): 41-51,62.
[8]	周兰凤, 麻双克, 付正, 张晴. 基于复杂属性商品的混合协同过滤推荐模型[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 154-161,185.
[9]	袁培森, 任吴北, 任守纲, 朱淑鑫, 徐焕良. 面向食品安全领域的个性化知识搜索系统研究[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 117-124,137.
[10]	陈雷慧, 匡俊, 陈辉, 曾炜, 郑建兵, 高明. 跨领域推荐技术综述[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 101-116,137.
[11]	王倩宜, 欧阳荣彬, 龙新征. SaaS云服务环境下的管理信息系统方案研究[J]. 华东师范大学学报(自然科学版), 2015, 2015(6): 134-142.
[12]	宋乐怡, 熊辉, 张蓉. 下一代移动推荐系统[J]. 华东师范大学学报(自然科学版), 2013, 2013(3): 37-45.
[13]	岳昆，王朝禄，朱运磊，武浩，刘惟一. 基于概率图模型的互联网广告点击率预测[J]. 华东师范大学学报(自然科学版), 2013, 2013(3): 15-25.
[14]	J·特雷维拉努斯， E·欧佛比. 包容性学习：开放教育资源和云服务[J]. 华东师范大学学报(自然科学版), 2012, 2012(2): 35-41.