参考文献/References:
[1] SARWAR B,KARYPIS G,KONSTON J,et al.Item-based collaborative filtering recommendation algorithms[C].Proceedings of the 10th international conference on the World Wide Web.Hong Kong: ACM,2001: 285-295.
[2] LINDEN G D, JACOBI J A, BENSON E A.Collaborative recommendations using item-to-item similarity mappings:US 6266649B1[P].2001-07-24.
[3] KLEINBERG J,SANDLER M.Using mixture models for collaborative filtering[J].Journal of Computer and System Sciences,2008, 74(1): 49-69.
[4] SON J, KIM S B.Content-based filtering for recommendation systems using multiattribute networks[J].Expert Systems with Applications, 2017, 89: 404- 412.
[5] KOOHI H, KIANI K.A new method to find neighbor users that improves the performance of collaborative Filtering[J].Expert Systems with Applications, 2017, 83:30-39.
[6] GEUENS S, COUSSEMENT K, De BOCK K W. A framework for configuring collaborative filtering-based recommendations derived from purchase data[J].European Journal of Operational Research.2018,265(1):208-218.
[7] 何佳知.基于内容和协同过滤的混合算法在推荐系统中的应用研究[D].上海:东华大学,2016:8-12.
[8] WEI Jianliang, MENG Fei, ARUNKUMAR N.A personalized authoritative user-based recommendation for social tagging[J].Future Generation Computer Systems, 2018, 86: 355-361.
[9] 郑充林.协同过滤的服装推荐算法的改进研究[D].上海:东华大学,2013:1-5.
[10] 陈丹儿,应玉龙.基于项目属性和BP神经网络的协同过滤推荐[J].信息技术,2015(3):70-73.
CHEN Dan’er, YING Yulong.Collaborative filtering method based on item’s characteristics and BP neural network[J].Information Technology, 2015(3):70-73.(in Chinese)
[11] 单毓馥,李丙洋.电子商务推荐系统中服装推荐问题研究[J].毛纺科技,2016,44(5):66-69.
SHAN Yufu,LI Bingyang.Researeh on apparel recommendation in e-commerce recommender systems[J]. Wool Textile Journal,2016,44(5):66-69.(in Chinese)
[12] U Liji, CHAI Yahui, CHEN Jianrui.Improved persona-lized recommendation based on user attributes clustering and score matrix filling[J].Computer Standards and Interfaces, 2018,57:59-67.
[13] WANG Hongbing, TAO Yong, YU Qi, et al.Incorporating both qualitative and quantitative preferences for service recommendation[J].Journal of Parallel and Distributed Computing, 2018,114:46-69.
[14] 武峰.阿里巴巴“双11”技术的演变历程、发展理念及未来创新建议[J].经营与管理,2018(3):18-21.
WU Feng.Alibaba "double 11" technology evolution process, development concept and future innovation suggestions[J].Management and Administration,2018(3):18-21.(in Chinese)
[15] 杨守德,赵德海.中国网络零售业发展的收敛性与空间溢出效应研究[J].经济体制改革,2018(3):38- 45.
YANG Shoude, ZHAO Dehai.Research on the convergence and spatial spillover effect of the development of China’s network retail industry[J].Reform of Economic System,2018(3):38- 45.(in Chinese)
[16] 赵思思,吴锋,舒磊.考虑消费者行为的电商脉冲式需求形成机理研究[J].软科学,2018(8):98-100,116.
ZHAO Sisi, WU Feng, SHU Lei.Research on formation mechanism of online pulse demand considering customer behavior[J].Soft Science, 2018(8):98-100,116.(in Chinese)
[17] SEJAL D, GANESHSINGH T, VENUGOPAL K R, et al.Image recommendation based on ANOVA cosine similarity[J].Procedia Computer Science, 2016,89:562-567.
[18] WANG Qingxian, LUO Xin, LI Yan, et al.Incremental slope-one recommenders[J].Neurocomputing, 2018, 272: 606-618.
[19] 王行甫,付欢欢,王琳.基于余弦相似度和实例加权改进的贝叶斯算法[J].计算机系统应用,2016,25(8):166-170.
WANG Xingfu, FU Huanhuan, WANG Lin.Improved Na?ve Bayes algorithm based on weighted instance with cosine similarity[J].Computer Systems Applications, 2016, 25(8):166-170.(in Chinese)
[20] 夏修臣,王秀英.基于余弦相似度的改进C 4.5决策树算法[J].计算机工程与设计,2018,39(1):120-125.
XIA Xiuchen, WANG Xiuying.Improved C 4.5 decision tree algorithm based on cosine similarity[J].Computer Engineering and Design,2018,39(1):120-125.(in Chinese)
[21] DADOUCHI C, AGARD B.Lowering penalties related to stock-outs by shifting demand in product recommendation systems[J].Decision Support Systems,2018, 114:61-69.
[22] WANG Qingxian, LUO Xin, LI Yan, et al.Incremental Slope-One recommenders[J].Neurocomputing,2018, 272: 606-618.(责任编辑:邢宝妹)