基于认知计算与情境感知的个性化信息自适应推荐模式框架研究
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摘要
【目的/意义】通过对个性化信息推荐中的用户认知、情境感知以及自适应等问题展开研究,进一步丰富个性化信息推荐的理论体系以及拓展个性化信息推荐的研究方法。【方法/过程】在对个性化信息推荐的概念和方法以及个性化信息自适应推荐分析的基础上,首先阐述认知计算的提出和发展及其基本观点,然后对情境感知的定义以及关键特征识别进行探讨,最后提出基于认知计算与情境感知的个性化信息自适应推荐模式及其框架,并针对其内涵展开深入分析。【结果/结论】通过借鉴认知计算与情境感知的基本观点和技术方法,重点研究认知计算与情境感知的融合、认知计算与情境感知融合下的用户偏好的提取、个性化信息自适应推荐模式3个方面内容,并最终构建基于认知计算和情境感知的个性化信息自适应推荐模式框架。
【Purpose/significance】Through the research of user cognition, context awareness and self-adaptive in personalized information recommendation, this paper improves the personalized information recommendation model to a certain extent, further enriches the theoretical system of personalized information recommendation and expands the research methods of personalized information recommendation.【Method/process】On the basis of the concept and method of personalized information recommendation and the analysis of personalized information self-adaptive recommendation, firstly, cognitive computing and its basic views is proposed, then the definition and key feature recognition of context aware are discussed, finally the personalized information self-adaptive recommendation model and framework is put forward based on cognitive computing and context aware, and its connotation is analyzed in depth.【Result/conclusion】By drawing on the basic ideas and technical methods of cognitive computing and context awareness, this paper focuses on the following three aspects: the integration of cognitive computing and situational awareness, the extraction of user preferences under the integration of cognitive computing and context awareness, the personalized information self-adaptive recommendation mode, and finally constructs the framework of personalized information self-adaptive recommendation model based on cognitive computing and context awareness.
【Purpose/significance】Through the research of user cognition, context awareness and self-adaptive in personalized information recommendation, this paper improves the personalized information recommendation model to a certain extent, further enriches the theoretical system of personalized information recommendation and expands the research methods of personalized information recommendation.【Method/process】On the basis of the concept and method of personalized information recommendation and the analysis of personalized information self-adaptive recommendation, firstly, cognitive computing and its basic views is proposed, then the definition and key feature recognition of context aware are discussed, finally the personalized information self-adaptive recommendation model and framework is put forward based on cognitive computing and context aware, and its connotation is analyzed in depth.【Result/conclusion】By drawing on the basic ideas and technical methods of cognitive computing and context awareness, this paper focuses on the following three aspects: the integration of cognitive computing and situational awareness, the extraction of user preferences under the integration of cognitive computing and context awareness, the personalized information self-adaptive recommendation mode, and finally constructs the framework of personalized information self-adaptive recommendation model based on cognitive computing and context awareness.
引文
1国家自然科学基金委员会.国家自然科学基金“十三五”发展规划[EB/OL].http://www.nsfc.gov.cn/publish/portal0/tab405/info50064.htm,2017-12-20.
2 黄向阳,张智雄,刘细文,等.中国科学院文献情报中心“十三五”发展重点[J].数字图书馆论坛,2016,(11):21-26.
3 Bobadilla J,Ortega F,Hernando A.Recommender syste ms survey[J].Knowledge-Based Systems,2013,(46):109-132.
4 Ghazarian S,Nematbakhsh M A.Enhancing Memory-based Collaborative Filtering for Group Recommender Systems[J].Expert Systems with Applications,2015,42(7):3801-3812.
5 Jannach D,Zanker M,Felfernig A,et al.推荐系统[M].蒋凡,译.北京:人民邮电出版社,2013:9-86.
6 Sahoo J,Das A K,Goswami A.An efficient approach for mining association rules from high utility item sets[J].Expert Systems with Applications,2015,(42):5754-5778.
7 Hussain S F,Suryani A.On Retrieving Intelligently Plagiarized Documents Using Semantic Similarity[J].Engineering Applications of Artificial Intelligence,2015,(45):246-258.
8 KIM J,LEE D,CHUNG K Y.Item recommendation based on context-aware model for personalized uhealthcare service[J].Multimedia Tools and Applications,2014,71(7):855-872.
9 Kim HN,El Saddik Abdulmotaleb.Exploring social tagging for personalized community recommendations[J].User Modeling and User Adapted Interaction,2013,23(2-3):249-285.
10 唐晓波,孙飞.基于复杂信任网络的社会化媒体好友推荐研究[J].情报理论与实践,2015,38(11):96-102.
11 艾丹祥,张玉峰,刘高勇,等.面向移动商务餐饮推荐的情境语义建模与规则推理[J].情报理论与实践,2016,39(2):82-88.
12 刘启华.基于兴趣社区和信任邻居的混合推荐研究[J].情报科学,2016,34(2):65-69.
13 Klein A,Ishikawa F,Honiden S.San GA:A self-adaptive network-aware approach to service composition[J].IEEE Transactions on Services Computing,2014,(3):452-464.
14 吴金红,张飞,周磊.面向泛在信息环境的自适应信息服务及其关键技术分析[J].情报理论与实践,2013,36(2):112-116.
15 Broinizi M,Mutti D,Ferreira J.Application configuration repository for adaptive service-based systems:Overcoming challenges in an evolutionary online advertising environment[C]//Proceedings of the 21th International Conference on Web Services(ICWS’14).Alaska,USA,2014:670-677.
16 漆月,周欢.基于图书分类号的自适应个性化图书推荐系统的研究[J].西南师范大学学报(自然科学版),2014,39(4):210-214.
17 谢洵.浅谈网络教育领域的自适应推送系统[J].中国教育信息化,2016,(12):90-93.
18 王志良,郑思仪,王先梅,等.心理认知计算的研究现状及发展趋势[J].模式识别与人工智能,2011,24(2):215-225.
19 What is Cognitive Computing?[EB/OL].http://www.wise geek.com/what-is-cognitive-computing.htm,2016-12-20.
20 (美)罗科,班布里奇著.聚合四大科技提高人类能力——纳米技术、生物技术、信息技术和认知科学[M].蔡曙山,译.北京:清华大学出版社,2010:67.
21 董超,毕晓君.认知计算的发展综述[J].电子世界,2014,(15):200-201.
22 国家自然科学基金委员会.重大研究计划“视听觉信息的认知计算”2008年度项目指南[EB/OL]..http://www.nsfc.gov.cn/publish/portal0/zdyjjh/2008/info24445.htm,2017-11-20.
23 沈晓卫.从大数据到认知计算[J].中国计算机学会通讯,2014,10(12):26-28.
24 李德毅.大数据认知——“2015大数据价值实现之路高峰论坛”主题报告[J].重庆理工大学学报(自然科学),2015,29(9):1-6.
25 郭涛.认知计算改变移动生活[N].中国计算机报,2015-04-06(24).
26 Hao Zheng,Yixiong Feng,Jianrong Tan,et al.An integrated cognitive computing approach for systematic conceptual design[J].Journal of Zhejiang University-Science(Applied Physics&Engineering),2016,17(4):286-294.
27 朱天宇,黄振亚,陈恩红,等.基于认知诊断的个性化试题推荐方法[J].计算机学报,2017,40(1):176-191.
28 Dey A K,Abowd G D,Salber D.A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-aware Applications[J].Journal of Human ComputerInteraction,2001,16(2):97-166.
29 S Faeli,B Loni,A Bellogin,et al.Implicit vs explicit trust in social matrix factorization[C]//8th ACM Conference on Recommender System,Silicon Valley:ACM Press,2014:317-320.
30 Mereno A.,Valls A.,Isern D.,et al.Sig Tur/E-Destination:Ontology-based Personalized Recommendation of Tourism and Leisure Activities[J].Engineering Applications of Artificial Intelligence,2013,26(1):633-651.
31 曾子明,陈贝贝.融合情境的智慧图书馆个性化服务研究[J].图书馆论坛,2016,(2):57-63.
32 Younghee N.A study on next generation digital library using context awareness technology[J].Library Hi-Tech,2013,31(2):236-253.
33 胡慕海,夏火松,吴金红,等.面向异构情景化推荐服务的关键情境特征识别[J].情报理论与实践,2015,38(1):104-109.
34 郭顺利,李秀霞.基于情境感知的移动图书馆用户信息需求模型构建[J].情报理论与实践,2014,37(8):64-68,73.
2 黄向阳,张智雄,刘细文,等.中国科学院文献情报中心“十三五”发展重点[J].数字图书馆论坛,2016,(11):21-26.
3 Bobadilla J,Ortega F,Hernando A.Recommender syste ms survey[J].Knowledge-Based Systems,2013,(46):109-132.
4 Ghazarian S,Nematbakhsh M A.Enhancing Memory-based Collaborative Filtering for Group Recommender Systems[J].Expert Systems with Applications,2015,42(7):3801-3812.
5 Jannach D,Zanker M,Felfernig A,et al.推荐系统[M].蒋凡,译.北京:人民邮电出版社,2013:9-86.
6 Sahoo J,Das A K,Goswami A.An efficient approach for mining association rules from high utility item sets[J].Expert Systems with Applications,2015,(42):5754-5778.
7 Hussain S F,Suryani A.On Retrieving Intelligently Plagiarized Documents Using Semantic Similarity[J].Engineering Applications of Artificial Intelligence,2015,(45):246-258.
8 KIM J,LEE D,CHUNG K Y.Item recommendation based on context-aware model for personalized uhealthcare service[J].Multimedia Tools and Applications,2014,71(7):855-872.
9 Kim HN,El Saddik Abdulmotaleb.Exploring social tagging for personalized community recommendations[J].User Modeling and User Adapted Interaction,2013,23(2-3):249-285.
10 唐晓波,孙飞.基于复杂信任网络的社会化媒体好友推荐研究[J].情报理论与实践,2015,38(11):96-102.
11 艾丹祥,张玉峰,刘高勇,等.面向移动商务餐饮推荐的情境语义建模与规则推理[J].情报理论与实践,2016,39(2):82-88.
12 刘启华.基于兴趣社区和信任邻居的混合推荐研究[J].情报科学,2016,34(2):65-69.
13 Klein A,Ishikawa F,Honiden S.San GA:A self-adaptive network-aware approach to service composition[J].IEEE Transactions on Services Computing,2014,(3):452-464.
14 吴金红,张飞,周磊.面向泛在信息环境的自适应信息服务及其关键技术分析[J].情报理论与实践,2013,36(2):112-116.
15 Broinizi M,Mutti D,Ferreira J.Application configuration repository for adaptive service-based systems:Overcoming challenges in an evolutionary online advertising environment[C]//Proceedings of the 21th International Conference on Web Services(ICWS’14).Alaska,USA,2014:670-677.
16 漆月,周欢.基于图书分类号的自适应个性化图书推荐系统的研究[J].西南师范大学学报(自然科学版),2014,39(4):210-214.
17 谢洵.浅谈网络教育领域的自适应推送系统[J].中国教育信息化,2016,(12):90-93.
18 王志良,郑思仪,王先梅,等.心理认知计算的研究现状及发展趋势[J].模式识别与人工智能,2011,24(2):215-225.
19 What is Cognitive Computing?[EB/OL].http://www.wise geek.com/what-is-cognitive-computing.htm,2016-12-20.
20 (美)罗科,班布里奇著.聚合四大科技提高人类能力——纳米技术、生物技术、信息技术和认知科学[M].蔡曙山,译.北京:清华大学出版社,2010:67.
21 董超,毕晓君.认知计算的发展综述[J].电子世界,2014,(15):200-201.
22 国家自然科学基金委员会.重大研究计划“视听觉信息的认知计算”2008年度项目指南[EB/OL]..http://www.nsfc.gov.cn/publish/portal0/zdyjjh/2008/info24445.htm,2017-11-20.
23 沈晓卫.从大数据到认知计算[J].中国计算机学会通讯,2014,10(12):26-28.
24 李德毅.大数据认知——“2015大数据价值实现之路高峰论坛”主题报告[J].重庆理工大学学报(自然科学),2015,29(9):1-6.
25 郭涛.认知计算改变移动生活[N].中国计算机报,2015-04-06(24).
26 Hao Zheng,Yixiong Feng,Jianrong Tan,et al.An integrated cognitive computing approach for systematic conceptual design[J].Journal of Zhejiang University-Science(Applied Physics&Engineering),2016,17(4):286-294.
27 朱天宇,黄振亚,陈恩红,等.基于认知诊断的个性化试题推荐方法[J].计算机学报,2017,40(1):176-191.
28 Dey A K,Abowd G D,Salber D.A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-aware Applications[J].Journal of Human ComputerInteraction,2001,16(2):97-166.
29 S Faeli,B Loni,A Bellogin,et al.Implicit vs explicit trust in social matrix factorization[C]//8th ACM Conference on Recommender System,Silicon Valley:ACM Press,2014:317-320.
30 Mereno A.,Valls A.,Isern D.,et al.Sig Tur/E-Destination:Ontology-based Personalized Recommendation of Tourism and Leisure Activities[J].Engineering Applications of Artificial Intelligence,2013,26(1):633-651.
31 曾子明,陈贝贝.融合情境的智慧图书馆个性化服务研究[J].图书馆论坛,2016,(2):57-63.
32 Younghee N.A study on next generation digital library using context awareness technology[J].Library Hi-Tech,2013,31(2):236-253.
33 胡慕海,夏火松,吴金红,等.面向异构情景化推荐服务的关键情境特征识别[J].情报理论与实践,2015,38(1):104-109.
34 郭顺利,李秀霞.基于情境感知的移动图书馆用户信息需求模型构建[J].情报理论与实践,2014,37(8):64-68,73.