期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于跨电商行为的交叉推荐算法 被引量：8

Crossing Recommendation Based on Multi-B2C Behavior
下载PDF
导出
摘要 利用百分点科技推荐引擎提供的原始数据,分析了用户跨电商的行为,提出了一种可在多个电商之间进行交叉推荐的算法。结果证明,该算法不仅在精确性上较完全冷启动的随机推荐有巨大的提高,而且所推荐的商品可以保持相当的多样性与新颖性。分析显示有约5%～10%的点击、收藏和购买行为发生在有交叉行为的用户身上,这些用户的活跃性明显强于非交叉用户。这些结果暗示交叉用户可能是网上购物的重度用户。该文展现了全新的研究思路,研讨了全新的分析对象,其思路和结果对于电子商务研究有重要价值。 Personalized recommendation has now been widely used in E-commerce, but there are still some problems to be solved such as cold-start problem, data sparsity, diversity-accuracy dilemma and so on. Existing literatures have focused on single data set, lacking a systematic understanding about the accessing behavior involving multiple web sites. Thanks to the real data, provided by Baifendian Information Technology recommendation engine, we analyze users＇ behavior on multi-B2Cs （business-to-customers） and propose a crossing recommendation algorithm which is able to recommend items of a B2C site to users according to the records of users in other B2C web sites. This algorithm largely improves accuracy compared with purely random recommendation under completely cold-start environment and can still keep high diversity and novelty.
作者 张亮 柏林森 周涛
机构地区 北京百分点信息科技有限公司 电子科技大学互联网科学中心 杭州师范大学阿里巴巴商学院
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2013年第1期154-160,共7页 Journal of University of Electronic Science and Technology of China
基金 国家自然科学基金面上项目(60973069) 中央高校基本科研业务费(ZYGX2010Z001)
关键词 冷启动问题 交叉推荐 电子商务 跨电商行为 推荐系统 cold-start problem crossing recommendation E-commerce multi-B2C behaviors recommender systems
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献20

  • 1L0 L, MEDO M, YEUNG C H, et al. Recommender Systems[J]. Physics Reports, 2012(519): 1-49.
  • 2DAHLEN B J, KONSTAN J A, HERLOCKER J L, et al. Jumpstarting movielens: user benefits of starting a collaborative filtering system with "dead data", TR 98-017[R]. Minnesota, USA: University of Minnesota.
  • 3LINDEN G SMITH B, York J. Amazon.corn recommendations item-to-item collaborative filtering[J]. IEEE Intemet Computing, 2003, 7(1): 76-80.
  • 4SALTON (2 Automatic text processing[M]. New York: Addison-Wesley, 1989.
  • 5GOLDBERG K, ROEDER T, GUPTA D, Eigentaste: a constant time collaborative filtering algorithm[J]. Information Retrieval J, 2001, 4(2): 133-151.
  • 6TERVEEN L, HILL W, AMENTO B, et al. PHOAKS: A system for sharing recommendations[J]. Communications of the ACM, 1997, 40(3): 59-62.
  • 7ZHANG Z K, LIU C, ZHANG Y C, et al. Solving the cold-start problem in recommender systems with social tags[J]. EPL, 2010, 92(2): 28002.
  • 8HUANG Z, CHEN H, ZENG D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filter[J]. IEEE Trans Information Systems, 2004, 22(1): 116-142.
  • 9ZHOU T, ZOLThn K, LIU J Cog et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J]. PNAS, 2010, 107(10): 4511-4515.
  • 10ZHOU T, REN J, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Phys Rev E, 2007(76): 046115.

二级参考文献137

  • 1Resnick P, lakovou N, Sushak M, et al. GroupLens: An open architecture for collaborative filtering of netnews. Proc 1994 Computer Supported Cooperative Work Conf, Chapel Hill, 1994: 175-186
  • 2Hill W, Stead L, Rosenstein M, et al. Recommending and evaluating choices in a virtual community of use. Proc Conf Human Factors in Computing Systems. Denver, 1995:194 -201
  • 3梅田望夫.网络巨变元年-你必须参加的大未来.先觉:先觉出版社,2006
  • 4Adomavicius G, Tuzhilin A. Expert-driven validation of Rule Based User Models in personalization applications. Data Mining and Knowledge Discovery, 2001, 5(1-2):33-58
  • 5Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the art and possible extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749
  • 6Rich E. User modeling via stereotypes. Cognitive Science, 1979, 3(4) : 329-354
  • 7Goldberg D, Nichols D, Oki BM, et al. Using collaborative filtering to weave an information tapestry. Comm ACM, 1992, 35(12):61-70
  • 8Konstan JA, Miller BN, Maltz D, el al. GroupLens: Applying collaborative filtering to usenet news. Comm ACM, 1997, 40(3) : 77-87
  • 9Shardanand U, Maes P. Social information filtering: Algorithms for automating ‘Word of Mouth'. Proe Conf Human Factors in Computing Systems Denver, 1995: 210-217
  • 10Linden G, Smith B, York J. Amazon. corn recommendations: hem-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76-80

共引文献748

同被引文献69

  • 1马建国,邢玲,李幼平,文丽.广播型网格的用户兴趣图谱[J].电子学报,2005,33(1):142-146. 被引量：5
  • 2张丙奇.基于领域知识的个性化推荐算法研究[J].计算机工程,2005,31(21):7-9. 被引量：34
  • 3CHIH - FONG T, CHIHLI H. Cluster ensembles in collaborative filtering recommendation [ J ]. Applied Soft Computing,2012(12) :1417 -1425.
  • 4WALTER C N, MARIALUISA H A, RAFAEL V G. Social knowledge - based recommender system applica- tion to the movies domain[J]. Expert Systems with Ap- plications ,2012 ( 39 ) : 10990 - 11000.
  • 5LISE G. Link mining: a survey [ J ]. ACM SIGK DD Explorations New sletter,2005,7 (2) : 3 - 12.
  • 6LU L Y, ZHOU T. Link prediction on complex net- works : a survey [ J ]. Physica A : Statistical Mechanics and Its Applications ,2011,390 (6) : 1150 - 1170.
  • 7SCHAFER L, GRAHAM J W. Missing data:our view of the state of the art [ J ]. Psychol Methods, 2002,7 (2) ..147 - 177.
  • 8KOSSINETS G. Effects of missing data in social net- works [ J ]. Social Networks,2006,28 (3) : 247 - 268.
  • 9CHEN C C, WAN Y H, CHUNG M C. An effective recommendation method for cold start new users using trust and distrust networks [ J ]. Information Sciences, 2013(224) :19 -36.
  • 10AMIR A, MOHAMMAD S. A hybrid recommenda- tion technique based on product category attributes [ J ]. Expert Systems with Applications, 2009 ( 36 ) : 11480 - 11488.

引证文献8

二级引证文献27

电子科技大学学报
2013年 第1期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部