基于Agent的餐饮个性化推荐建模与仿真研究
|
摘要
随着电子商务环境下“信息过载”问题的日益严重,个性化服务推荐作为一种有效的解决途径受到社会各界的普遍重视,成为新兴电子商务研究领域之一。而随着移动网络技术的快速发展,在新兴移动电子商务环境下考虑情境的精确化个性推荐问题成为当前迫切需要解决的一项关键技术问题。
个性化推荐的传统研究方法存在数据稀疏、冷启动、数据收集难等致命问题,推荐策略的好坏需要进行考察和评估,移动商务环境下情境因素对顾客消费行为的影响不可忽略,采用基于Agent的建模与仿真方法不仅可以很好地解决上述问题,而且具有捕捉系统宏观“涌现性”的能力。因此,本文采用ABMS方法进行个性化推荐研究,以餐饮推荐系统为例,通过个体交互作用所产生的涌现特性来分析顾客的消费行为以及个性化推荐策略的有效性,论文的核心工作如下：
(1)将ABMS方法与电子商务个性化推荐研究相结合,并在一定的模型假设前提下,提出了基于Agent的餐饮个性化推荐模型架构。
(2)针对餐饮个性化推荐理论模型,通过对顾客特征和情境内涵的抽象与界定,区分影响顾客就餐行为的主要影响因素,分别建立了顾客模型及情境模型。
(3)在分析本仿真模型的Agent构成及其功能的基础上,重点对顾客及服务Agent的规则库进行设计,通过分析顾客特征及情境因素与顾客行为的关联关系,提炼出基于顾客个性化信息和考虑情境因素的两种规则库,并设计了一个交互管理Agent用于管理各Agent之间具体的信息和行为交互活动。
(4)基于REPAST进行仿真模型的设计及实现,重点是代表各Agent的自定义类的设计及实现,其中,顾客和情境模型作为类的属性,规则库作为其方法库。
最后,本文将两种规则作为两套运行方案进行实施,从而分别获取顾客特征及情境因素对个性化推荐和顾客行为的微观影响分析,以及最终顾客的宏观涌现行为分析。两种运行方案下模型的有效性评估结果表明：本文提出的基于顾客特征的个性化推荐模型与其它推荐研究的有效性数值范围相当,而考虑情境因素的个性化推荐模型的有效性又有了明显提高。
Along with the increasing of information overload problem under Electronic Commerce, personalized recommendation as an effective solution has become one of emerging E-Commerce research areas. With the rapid expansion of mobile network techniques, accurately personalized recommendation considering context under emerging Mobile Commerce is becoming a key technical problem need to be solved urgently.
There are several deadly problems in traditional research methods, such as data sparseness, cold start and difficult data collection. Besides, the effectiveness of recommendation strategy needs to be evaluated, and the important impact of context factors on consumer behavior under mobile commerce circumstance needs to be considered. All these problems can be solved by adopting Agent-based Modeling and Simulation (ABMS) method to study personalized recommendation. Moreover, it possesses the ability of seizing emergence of the whole system. Therefore, this paper makes some research on personalized recommendation using ABMS method. Taking catering recommendation system as an example, customer behavior and the effectiveness of personalized recommendation strategy are analyzed according to the emergence generated by the interaction of Agents.
Key works of this paper can be summarized in the following parts:
(1) By combining ABMD method and personalized recommendation under electronic commerce, the framework of Agent-based catering recommendation model is proposed under some assumed conditions.
(2) Aim at the theoretical model of catering personalized recommendation, main influencing factors which affect customer dining behavior are found, then a customer model and context model is established respectively.
(3) Based on the analysis of Agent components and their function of this simulation model, focus on the design of rules of customer and waiter Agents, rules based on customer individuation and context factors are proposed respectively through analyzing the relationship between customer characters and context factors with customer behavior. Moreover, an interaction management Agent is designed to supervising the interaction of information and behavior among Agents.
(4) The design and implement of this simulation model under REPAST, especially the user-defined classes representing various Agents, where customer and context models as the attributes of classes, rules as their methods.
In the end, two operation plans are run based on two kinds of rules to make microcosmic impact analysis of customer characters and context factors upon personalized recommendation and customer behavior, as well as the macroscopic emergence analysis of customer. The effectiveness evaluation results indicate that the customer character-based recommendation model has similar effective value range with other recommendation research, and the effectiveness is improved dramatically in the other operation plan considering context factors.
个性化推荐的传统研究方法存在数据稀疏、冷启动、数据收集难等致命问题,推荐策略的好坏需要进行考察和评估,移动商务环境下情境因素对顾客消费行为的影响不可忽略,采用基于Agent的建模与仿真方法不仅可以很好地解决上述问题,而且具有捕捉系统宏观“涌现性”的能力。因此,本文采用ABMS方法进行个性化推荐研究,以餐饮推荐系统为例,通过个体交互作用所产生的涌现特性来分析顾客的消费行为以及个性化推荐策略的有效性,论文的核心工作如下：
(1)将ABMS方法与电子商务个性化推荐研究相结合,并在一定的模型假设前提下,提出了基于Agent的餐饮个性化推荐模型架构。
(2)针对餐饮个性化推荐理论模型,通过对顾客特征和情境内涵的抽象与界定,区分影响顾客就餐行为的主要影响因素,分别建立了顾客模型及情境模型。
(3)在分析本仿真模型的Agent构成及其功能的基础上,重点对顾客及服务Agent的规则库进行设计,通过分析顾客特征及情境因素与顾客行为的关联关系,提炼出基于顾客个性化信息和考虑情境因素的两种规则库,并设计了一个交互管理Agent用于管理各Agent之间具体的信息和行为交互活动。
(4)基于REPAST进行仿真模型的设计及实现,重点是代表各Agent的自定义类的设计及实现,其中,顾客和情境模型作为类的属性,规则库作为其方法库。
最后,本文将两种规则作为两套运行方案进行实施,从而分别获取顾客特征及情境因素对个性化推荐和顾客行为的微观影响分析,以及最终顾客的宏观涌现行为分析。两种运行方案下模型的有效性评估结果表明：本文提出的基于顾客特征的个性化推荐模型与其它推荐研究的有效性数值范围相当,而考虑情境因素的个性化推荐模型的有效性又有了明显提高。
Along with the increasing of information overload problem under Electronic Commerce, personalized recommendation as an effective solution has become one of emerging E-Commerce research areas. With the rapid expansion of mobile network techniques, accurately personalized recommendation considering context under emerging Mobile Commerce is becoming a key technical problem need to be solved urgently.
There are several deadly problems in traditional research methods, such as data sparseness, cold start and difficult data collection. Besides, the effectiveness of recommendation strategy needs to be evaluated, and the important impact of context factors on consumer behavior under mobile commerce circumstance needs to be considered. All these problems can be solved by adopting Agent-based Modeling and Simulation (ABMS) method to study personalized recommendation. Moreover, it possesses the ability of seizing emergence of the whole system. Therefore, this paper makes some research on personalized recommendation using ABMS method. Taking catering recommendation system as an example, customer behavior and the effectiveness of personalized recommendation strategy are analyzed according to the emergence generated by the interaction of Agents.
Key works of this paper can be summarized in the following parts:
(1) By combining ABMD method and personalized recommendation under electronic commerce, the framework of Agent-based catering recommendation model is proposed under some assumed conditions.
(2) Aim at the theoretical model of catering personalized recommendation, main influencing factors which affect customer dining behavior are found, then a customer model and context model is established respectively.
(3) Based on the analysis of Agent components and their function of this simulation model, focus on the design of rules of customer and waiter Agents, rules based on customer individuation and context factors are proposed respectively through analyzing the relationship between customer characters and context factors with customer behavior. Moreover, an interaction management Agent is designed to supervising the interaction of information and behavior among Agents.
(4) The design and implement of this simulation model under REPAST, especially the user-defined classes representing various Agents, where customer and context models as the attributes of classes, rules as their methods.
In the end, two operation plans are run based on two kinds of rules to make microcosmic impact analysis of customer characters and context factors upon personalized recommendation and customer behavior, as well as the macroscopic emergence analysis of customer. The effectiveness evaluation results indicate that the customer character-based recommendation model has similar effective value range with other recommendation research, and the effectiveness is improved dramatically in the other operation plan considering context factors.
引文
[1]黄小琴.多Agent电子商务推荐系统研究[D].重庆：重庆大学,2003.
[2]Resnick, Varian. Recommender systems [J]. Communications of the ACM,1997, 40(3):56-58.
[3]余力,刘鲁.电子商务个性化推荐研究[J].计算机集成制造系统2004,10(10)：1306-1313.
[4]陶钰.基于多Agent的市场建模的若干问题研究[D].扬州：扬州大学,2007.
[5]廖守亿,戴金海等.复杂适应系统及基于Agent的建模与仿真方法[J].系统仿真学报,2004,16(1)：113-117.
[6]刘芳先,宋顺林.改进的协同过滤推荐算法[J].计算机工程与应用,2011,47(8)：74-75.
[7]李杰,徐勇,王云峰.面向个性化推荐的强关联规则挖掘[J].系统工程理论与实践,2009,29(8)：144-152.
[8]Kun Chang Lee, Soonjae Kwon. Online shopping recommendation mechanism and its influence on consumer decisions and behaviors:A causal map approach [J]. Expert Systems with Applications.2008,35:1567-1574.
[9]郭伟光,章蕾.基于用户模式聚类的协同过滤个性化推荐方法[J].情报杂志,2011,30(2)：160-163.
[10]洪文兴,翁洋,朱顺痣.垂直电子商务网站的混合型推荐系统[J].系统工程理论与实践,2010,30(5)：928-935.
[11]余力,刘鲁,李雪峰.用户多兴趣下的个性化推荐算法研究[J].计算机集成制造系统,2004,10(12)：1610-1615.
[12]Silvia Schiaffino, Analla Amandi. Building an expert travel agent as a software agent [J]. Expert Systems with Applications.2009,36:1291-1299.
[13]王岚.基于Multi-Agent的分布式应用系统研究[D].北京：首都经济贸易大学,2004.
[14]何炎祥,陈莘萌等.agent和多agent系统的设计与应用[M].武汉：武汉大学出版社,2001.
[15]Chang-Hyun Jo, Guobin Chen, James Choi. A new approach to the BDI agent-based modeling [C]. Proceedings of the 2004 ACM symposium on Applied computing,2004: 1541-1545.
[16]赵剑冬.基于Agent的产业集群企业竞争模型与仿真研究[D].广州：华南理工大学,2010.
[17]Bryan Raney, Nurhan Cetin, Andreas Vollmy. An Agent-Based Microsimulation Model of Swiss Travel:First Results [J]. Networks and Spatial Economics,2003, (3): 23-41.
[18]Arnaud Doniec, Rene Mandiau, et al. A behavioral multi-agent model for road traffic simulation [J]. Engineering Applications of Artificial Intelligence,2008, 21:1443-1454.
[19]李英,马寿峰.基于Agent的仿真系统建模[J].系统工程学报,2006,21(3)：225-231.
[20]谭满春.基于Agent与模糊逻辑的车辆换道仿真模型[J].系统工程学报,2007,22(1)：40-45.
[21]傅星.基于复杂适应系统理论的经济仿真研究[D].北京：首都经济贸易大学,2005.
[22]李宏亮,程华,金士尧.基于Agent的复杂系统分布仿真建模方法的研究[J].计算机工程与应用,2007,43(8)：209-213.
[23]廖守亿.复杂系统基于Agent的建模与仿真方法研究及应用[D].长沙：国防科学技术大学,2005.
[24]刘博,马亮.基于多Agent和HLA的潜艇战场损伤评估仿真研究[J].系统仿真学报,2008,20(9)：2334-2337.
[25]Fernando, Edmundo. An Automated Negotiation Model for M-commerce Using Mobile Agents[C]. Proceedings of the International Conference on Web Engineering,2003, 2722:72-75.
[26]Fernando, Edmundo. A Context-Aware Negotiation Model for M-Commerce[C]. International Conference on Mobility Aware Technologies and Applications,2005, 3744:230-239.
[27]干海,李一军,侯新培.基于Agent的电子商务自动谈判系统研究[J].系统工程理论与实践,2005,(11)：14-19.
[28]Yonghui Fu, Rajesh Piplani. Multi-agent enabled modeling and simulation towards collaborative inventory management in supply chains[C]. Proceedings of the 2000 Winter Simulation Conference,2000:1763-1771.
[29]王刚,魏众,关伟,中金升.基于Multi-Agent的港口集装箱码头物流作业系统集成优化研究[J].物流技术,2007,26(4)：31-33.
[30]程贲,李兴兵,鲁延京等.基于Agent的居民就医选择建模与仿真[J].系统工程,2009,26(9)：96-101.
[31]龚晓光,黎志成.基于多智能体仿真的新产品市场扩散研究[J].系统工程理论与实践,2003,(12)：59-62.
[32]Nick Malleson, Alison Heppenstall, Linda See. Crime reduction through simulation: An agent-based model of burglary [J]. Computers, Environment and Urban Systems, 2010,34:236-250.
[33]W. Macmillan, H. Q. Huang. An agent-based simulation model of a primitive agricultural society [J]. Geoforum,2008,39:643-658.
[34]Chunyan Miao, Qiang Yang, Haijing Fang. A cognitive approach for agent-based personalized recommendation [J]. Knowledge-Based Systems.2007,20:397-405.
[35]Yoon Ho Cho, Jae Kyeong Kim, Soung Hie Kim. A personalized recommender system based on web usage mining and decision tree induction [J]. Expert Systems with Applications.2002,23:329-342.
[36]Wei-Po Lee, Tsung-Hsien Yang. Personalizing information appliances:a multi-agent framework for TV programmer recommendations [J]. Expert Systems with Applications, 2003,25:331-341.
[37]Wei-Po Lee. Towards agent-based decision making in the electronic marketplace: interactive recommendation and automated negotiation [J]. Expert Systems with Applications.2004,27:665-679.
[38]Hyung Jun Ahn. Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy [J]. Information Sciences,2010, 180 (9):1555-1570.
[39]闫艳,王锁柱.基于多Agent的电子商务个性化推荐系统模型研究[J].情报杂志,2007,(5)：59-61.
[40]杜静,叶剑，史红周等.基于贝叶斯网络的多Agent服务推荐机制研究[J].计算机科学,2010,37(4)：208-211.
[41]林霜梅,汪更生,陈弈秋.个性化推荐系统中的用户建模及特征选择[J].计算机工程,2007,33(17)：196-198.
[42]李建廷.基于简化ODP的用户兴趣模型[J].计算机工程与科学,2010,32(5)：121-123.
[43]李晓昀,阳小华,余颖.基于隐性反馈分析的个性化推荐研究[J].计算机工程与设计,2009,30(16)：3794-3796.
[44]PUI-MUN LEE. Behavioral Model of Online Purchasers in E-Commerce Environment [J]. Electronic Commerce Research,2002,2:75-85.
[45]Kwiseok Kwon, Jinhyung Cho, et al. Park. Influences of customer preference development on the effectiveness of recommendation strategies [J]. Electronic Commerce Research and Applications.2009,8:263-275.
[46]K. Schoefegger, P. Seitlinger, T. Ley. Towards a user model for personalized recommendations in work-integrated learning:A report on an experimental study with a collaborative tagging system [J]. Procedia Computer Science,2010,1:2829-2838.
[47]Kazi Zakia Sultana, Anupam Bhattacharjee, et al. A Model for Contextual Cooperative Query Answering in E-Commerce Applications [J]. Flexible Query Answering Systems, 2009,5822:25-36.
[48]戈高欣,吕廷杰等.基于情景感知的移动商务产业链中合作协调及分配机制的研究[J].微计算机应用,2008,29(11)：29-34.
[49]胡慕海,蔡淑琴,张宇.面向个性化推荐的情境化用户偏好研究[J].情报杂志,2010,29(10)：157-162.
[50]Joonhee K., Sungrim Kim. Context-Aware Recommendation Service Using Multi-leveled Information in Mobile Commerce[C].7th International Conference on E-Commerce and Web Technologies,2006,4082:122-131.
[51]Yu-Hong Feng, Teck-Hou Teng, Ah-Hwee Tan. Modelling situation awareness for Context-aware Decision Support [J]. Expert Systems with Applications,2009, 36:455-463.
[52]Moon-Hee Park, Jin-Hyuk Hong, Sung-Bae Cho. Location-based recommendation system using Bayesian user's preference model in mobile devices [C].4th International Conference on Ubiquitous Intelligence and Computing. Berlin:Springer, 2007:1130-1139.
[53]高旻,吴中福.基于个性化情境和项目的协同推荐研究[J].东南大学学报,2009,39：27-31.
[54]安芳,电子商务个性化信息推荐服务的研究[D].北京：对外经济贸易大学,2006.
[55]Harry K. H. Chow, K. L. Choy, W. B. Lee. A dynamic logistics process knowledge-based system-An RFID multi-agent approach [J]. Knowledge-Based Systems,2007,20: 357-372.
[56]韩毅,范冉冉.基于复杂适应系统(CAS)对社会的探究[J].系统科学学报,2008,16(3)：48-51.
[57]陈运动.餐饮业的顾客满意与顾客忠诚研究[D].天津：天津大学,2007.
[58]王巧容,赵海燕,曹健.个性化服务中的用户建模技术[J].小型微型计算机系统,2011,32(1)：39-46.
[59]姜昌华,韩伟,胡幼华.REPAST——个多Agent仿真平台[J].系统仿真学报,2006,18(8)：2319-2322.
[60]傅游,杜宇.基于Agent的REPAST建模仿真平台[J].软件工程,2009,(2)：53-55.
[61]郝成民,湘伟,郭世杰,晏洪勇.REPAST基于Agent建模仿真的可扩展平台[J].计算机仿真,2007,24(11):285-288.
[62]Mobasher B, Dai H, Luo T. Improving the effectiveness of collaborative filtering on anonymous Web usage data[C]. Proc of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP2001). Seattle,USA,2001:49-56.
[63]王有为,许博,卫学启,凌鸿.基于用户访问序列聚类的网站导行系统[J].系统工程理论与实践,2010,30(7)：1305-1311.
[64]冀俊忠,沙志强,刘椿年.一种基于贝叶斯网客户购物模型的商品推荐方法[J].计算机应用研究,2005,(4)：65-68.
[65]王海波,李朋轩,徐一秋.面向访问序列模式的页面推荐方法[J].计算机工程与应用,2010,46(25)：127-130.
[2]Resnick, Varian. Recommender systems [J]. Communications of the ACM,1997, 40(3):56-58.
[3]余力,刘鲁.电子商务个性化推荐研究[J].计算机集成制造系统2004,10(10)：1306-1313.
[4]陶钰.基于多Agent的市场建模的若干问题研究[D].扬州：扬州大学,2007.
[5]廖守亿,戴金海等.复杂适应系统及基于Agent的建模与仿真方法[J].系统仿真学报,2004,16(1)：113-117.
[6]刘芳先,宋顺林.改进的协同过滤推荐算法[J].计算机工程与应用,2011,47(8)：74-75.
[7]李杰,徐勇,王云峰.面向个性化推荐的强关联规则挖掘[J].系统工程理论与实践,2009,29(8)：144-152.
[8]Kun Chang Lee, Soonjae Kwon. Online shopping recommendation mechanism and its influence on consumer decisions and behaviors:A causal map approach [J]. Expert Systems with Applications.2008,35:1567-1574.
[9]郭伟光,章蕾.基于用户模式聚类的协同过滤个性化推荐方法[J].情报杂志,2011,30(2)：160-163.
[10]洪文兴,翁洋,朱顺痣.垂直电子商务网站的混合型推荐系统[J].系统工程理论与实践,2010,30(5)：928-935.
[11]余力,刘鲁,李雪峰.用户多兴趣下的个性化推荐算法研究[J].计算机集成制造系统,2004,10(12)：1610-1615.
[12]Silvia Schiaffino, Analla Amandi. Building an expert travel agent as a software agent [J]. Expert Systems with Applications.2009,36:1291-1299.
[13]王岚.基于Multi-Agent的分布式应用系统研究[D].北京：首都经济贸易大学,2004.
[14]何炎祥,陈莘萌等.agent和多agent系统的设计与应用[M].武汉：武汉大学出版社,2001.
[15]Chang-Hyun Jo, Guobin Chen, James Choi. A new approach to the BDI agent-based modeling [C]. Proceedings of the 2004 ACM symposium on Applied computing,2004: 1541-1545.
[16]赵剑冬.基于Agent的产业集群企业竞争模型与仿真研究[D].广州：华南理工大学,2010.
[17]Bryan Raney, Nurhan Cetin, Andreas Vollmy. An Agent-Based Microsimulation Model of Swiss Travel:First Results [J]. Networks and Spatial Economics,2003, (3): 23-41.
[18]Arnaud Doniec, Rene Mandiau, et al. A behavioral multi-agent model for road traffic simulation [J]. Engineering Applications of Artificial Intelligence,2008, 21:1443-1454.
[19]李英,马寿峰.基于Agent的仿真系统建模[J].系统工程学报,2006,21(3)：225-231.
[20]谭满春.基于Agent与模糊逻辑的车辆换道仿真模型[J].系统工程学报,2007,22(1)：40-45.
[21]傅星.基于复杂适应系统理论的经济仿真研究[D].北京：首都经济贸易大学,2005.
[22]李宏亮,程华,金士尧.基于Agent的复杂系统分布仿真建模方法的研究[J].计算机工程与应用,2007,43(8)：209-213.
[23]廖守亿.复杂系统基于Agent的建模与仿真方法研究及应用[D].长沙：国防科学技术大学,2005.
[24]刘博,马亮.基于多Agent和HLA的潜艇战场损伤评估仿真研究[J].系统仿真学报,2008,20(9)：2334-2337.
[25]Fernando, Edmundo. An Automated Negotiation Model for M-commerce Using Mobile Agents[C]. Proceedings of the International Conference on Web Engineering,2003, 2722:72-75.
[26]Fernando, Edmundo. A Context-Aware Negotiation Model for M-Commerce[C]. International Conference on Mobility Aware Technologies and Applications,2005, 3744:230-239.
[27]干海,李一军,侯新培.基于Agent的电子商务自动谈判系统研究[J].系统工程理论与实践,2005,(11)：14-19.
[28]Yonghui Fu, Rajesh Piplani. Multi-agent enabled modeling and simulation towards collaborative inventory management in supply chains[C]. Proceedings of the 2000 Winter Simulation Conference,2000:1763-1771.
[29]王刚,魏众,关伟,中金升.基于Multi-Agent的港口集装箱码头物流作业系统集成优化研究[J].物流技术,2007,26(4)：31-33.
[30]程贲,李兴兵,鲁延京等.基于Agent的居民就医选择建模与仿真[J].系统工程,2009,26(9)：96-101.
[31]龚晓光,黎志成.基于多智能体仿真的新产品市场扩散研究[J].系统工程理论与实践,2003,(12)：59-62.
[32]Nick Malleson, Alison Heppenstall, Linda See. Crime reduction through simulation: An agent-based model of burglary [J]. Computers, Environment and Urban Systems, 2010,34:236-250.
[33]W. Macmillan, H. Q. Huang. An agent-based simulation model of a primitive agricultural society [J]. Geoforum,2008,39:643-658.
[34]Chunyan Miao, Qiang Yang, Haijing Fang. A cognitive approach for agent-based personalized recommendation [J]. Knowledge-Based Systems.2007,20:397-405.
[35]Yoon Ho Cho, Jae Kyeong Kim, Soung Hie Kim. A personalized recommender system based on web usage mining and decision tree induction [J]. Expert Systems with Applications.2002,23:329-342.
[36]Wei-Po Lee, Tsung-Hsien Yang. Personalizing information appliances:a multi-agent framework for TV programmer recommendations [J]. Expert Systems with Applications, 2003,25:331-341.
[37]Wei-Po Lee. Towards agent-based decision making in the electronic marketplace: interactive recommendation and automated negotiation [J]. Expert Systems with Applications.2004,27:665-679.
[38]Hyung Jun Ahn. Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy [J]. Information Sciences,2010, 180 (9):1555-1570.
[39]闫艳,王锁柱.基于多Agent的电子商务个性化推荐系统模型研究[J].情报杂志,2007,(5)：59-61.
[40]杜静,叶剑，史红周等.基于贝叶斯网络的多Agent服务推荐机制研究[J].计算机科学,2010,37(4)：208-211.
[41]林霜梅,汪更生,陈弈秋.个性化推荐系统中的用户建模及特征选择[J].计算机工程,2007,33(17)：196-198.
[42]李建廷.基于简化ODP的用户兴趣模型[J].计算机工程与科学,2010,32(5)：121-123.
[43]李晓昀,阳小华,余颖.基于隐性反馈分析的个性化推荐研究[J].计算机工程与设计,2009,30(16)：3794-3796.
[44]PUI-MUN LEE. Behavioral Model of Online Purchasers in E-Commerce Environment [J]. Electronic Commerce Research,2002,2:75-85.
[45]Kwiseok Kwon, Jinhyung Cho, et al. Park. Influences of customer preference development on the effectiveness of recommendation strategies [J]. Electronic Commerce Research and Applications.2009,8:263-275.
[46]K. Schoefegger, P. Seitlinger, T. Ley. Towards a user model for personalized recommendations in work-integrated learning:A report on an experimental study with a collaborative tagging system [J]. Procedia Computer Science,2010,1:2829-2838.
[47]Kazi Zakia Sultana, Anupam Bhattacharjee, et al. A Model for Contextual Cooperative Query Answering in E-Commerce Applications [J]. Flexible Query Answering Systems, 2009,5822:25-36.
[48]戈高欣,吕廷杰等.基于情景感知的移动商务产业链中合作协调及分配机制的研究[J].微计算机应用,2008,29(11)：29-34.
[49]胡慕海,蔡淑琴,张宇.面向个性化推荐的情境化用户偏好研究[J].情报杂志,2010,29(10)：157-162.
[50]Joonhee K., Sungrim Kim. Context-Aware Recommendation Service Using Multi-leveled Information in Mobile Commerce[C].7th International Conference on E-Commerce and Web Technologies,2006,4082:122-131.
[51]Yu-Hong Feng, Teck-Hou Teng, Ah-Hwee Tan. Modelling situation awareness for Context-aware Decision Support [J]. Expert Systems with Applications,2009, 36:455-463.
[52]Moon-Hee Park, Jin-Hyuk Hong, Sung-Bae Cho. Location-based recommendation system using Bayesian user's preference model in mobile devices [C].4th International Conference on Ubiquitous Intelligence and Computing. Berlin:Springer, 2007:1130-1139.
[53]高旻,吴中福.基于个性化情境和项目的协同推荐研究[J].东南大学学报,2009,39：27-31.
[54]安芳,电子商务个性化信息推荐服务的研究[D].北京：对外经济贸易大学,2006.
[55]Harry K. H. Chow, K. L. Choy, W. B. Lee. A dynamic logistics process knowledge-based system-An RFID multi-agent approach [J]. Knowledge-Based Systems,2007,20: 357-372.
[56]韩毅,范冉冉.基于复杂适应系统(CAS)对社会的探究[J].系统科学学报,2008,16(3)：48-51.
[57]陈运动.餐饮业的顾客满意与顾客忠诚研究[D].天津：天津大学,2007.
[58]王巧容,赵海燕,曹健.个性化服务中的用户建模技术[J].小型微型计算机系统,2011,32(1)：39-46.
[59]姜昌华,韩伟,胡幼华.REPAST——个多Agent仿真平台[J].系统仿真学报,2006,18(8)：2319-2322.
[60]傅游,杜宇.基于Agent的REPAST建模仿真平台[J].软件工程,2009,(2)：53-55.
[61]郝成民,湘伟,郭世杰,晏洪勇.REPAST基于Agent建模仿真的可扩展平台[J].计算机仿真,2007,24(11):285-288.
[62]Mobasher B, Dai H, Luo T. Improving the effectiveness of collaborative filtering on anonymous Web usage data[C]. Proc of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP2001). Seattle,USA,2001:49-56.
[63]王有为,许博,卫学启,凌鸿.基于用户访问序列聚类的网站导行系统[J].系统工程理论与实践,2010,30(7)：1305-1311.
[64]冀俊忠,沙志强,刘椿年.一种基于贝叶斯网客户购物模型的商品推荐方法[J].计算机应用研究,2005,(4)：65-68.
[65]王海波,李朋轩,徐一秋.面向访问序列模式的页面推荐方法[J].计算机工程与应用,2010,46(25)：127-130.