一种融合了基于朴素贝叶斯算法与情境感知的协同推荐系统——以大学图书馆实体图书推荐为例
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摘要
[目的/意义]将情境感知技术引入图书馆以提高服务的智能化,已成为数字图书馆的发展趋势之一。为了提高情境感知模型中推荐结果的准确度。[方法/过程]本文研究并提出了一种融合了朴素贝叶斯算法与情景感知功能的协同推荐模型,并通过实验对推荐效果进行了评估。具体为:首先,获取用户的当前任务和情景信息,同时提取历史信息库用户的行为偏好;其次基于属性加权贝叶斯算法计算用户的行为相似度,继而进行协同推荐;通过计算目标情景中所有情景属性对所推荐资源的影响的权值,对协同推荐所得评分进行加权处理,形成最终的预测预测;最后通过实验对模型进行检验。[结果/结论]结果表明:使用该模型得出的推荐结果优于传统的协同推荐结果。因此该模型能够更好地为为个性化信息服务提供支持。
[Purpose/Significance] The context-aware technology which facilitates the library with more intelligence of service,has became a new trends in electronic library development. [Method/Process] In order to improve the recommendation accuracy,this paper proposed a collaborative filtering model based on Naive bayes,and it had context-awareness. An experimental study was given to assess the effectiveness of the model. First,it described the user by acquiring their current context and historical preference. Then it used Naive bayes to measure the similarities of users. After that,we got the predicated recommendation result. [Limitations] The result proved this model was preferable to classic collaborative filtering method,thus could provide better personalized service to library collection recommendations.
[Purpose/Significance] The context-aware technology which facilitates the library with more intelligence of service,has became a new trends in electronic library development. [Method/Process] In order to improve the recommendation accuracy,this paper proposed a collaborative filtering model based on Naive bayes,and it had context-awareness. An experimental study was given to assess the effectiveness of the model. First,it described the user by acquiring their current context and historical preference. Then it used Naive bayes to measure the similarities of users. After that,we got the predicated recommendation result. [Limitations] The result proved this model was preferable to classic collaborative filtering method,thus could provide better personalized service to library collection recommendations.
引文
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[5] Kim J,Lee D,Chung K Y. Item Recommendation Based on Context-aware Model for Personalized U-healthcare Service[J]. Multimedia Tools&Applications,2014,71(2):855-872.
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[14]洪亮,钱晨,樊星.移动数字图书馆资源的情境感知个性化推荐方法研究[J].现代图书情报技术,2016,(Z1):110-119.
[15] Mighali V,Fiore G D,Patrono L,et al. Innovative Io T-aware Services for a Smart Museum[C]//International Conference on World Wide Web. ACM,2015:547-550.
[16]张帅,郭顺利.基于情境感知的高校移动图书馆个性化推荐模型研究[J].情报探索,2014,(10):6-11.
[17]周玲元,闫思琪,朱翔宇.“智慧图书馆”情境感知服务模式及评价研究[J].图书馆学研究,2017,(21):23-30.
[18] Adomavicius G,Tuzhilin A. Context-aware Recommender Systems[C]//ACM Conference on Recommender Systems. ACM,2008:335-336.
[19]曾子明.移动环境下基于情境感知的数字图书馆个性化信息推荐研究[J].图书情报工作,2013,57(12):123-128.
[20]秦锋,任诗流,程泽凯,罗慧.基于属性加权的朴素贝叶斯分类算法[J].计算机工程与应用,2008,(6):107-109.
[21]曾子明,李鑫.移动环境下基于情境感知的个性化信息推荐[J].情报杂志,2012,31(8):166-170.
[2] Dey A K,Abowd G D,Salber D. A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications[J]. Human-computer Interaction,2001,16(2-4):97-166.
[3] Dey A K. Understanding and Using Context Personal and Ubiquitous Computing Journal[J]. Personal&Ubiquitous Computing,2001,5(1):4-7.
[4]郭高尚.情景感知的移动推荐研究[D].邯郸:河北工程大学,2014.
[5] Kim J,Lee D,Chung K Y. Item Recommendation Based on Context-aware Model for Personalized U-healthcare Service[J]. Multimedia Tools&Applications,2014,71(2):855-872.
[6] Kwon O,Choi S,Park G. NAMA:A Context-aware Multi-agent Based Web Service Approach to Proactive Need Identification for Personalized Reminder Systems[J]. Expert Systems with Applications,2005,29(1):17-32.
[7] Mylonas P,Vallet D,Castells P,et al. Personalized Information Retrieval Based on Context and Ontological Knowledge[J].Knowledge Engineering Review,2008,23(1):73-100.
[8]李晟.基于情境感知的个性化电影推荐[D].北京:北京邮电大学,2012.
[9] Schilit B,Adams N,Want R. Context-Aware Computing Applications[C]//The Workshop on Mobile Computing Systems&Applications,1994:85-90.
[10] Lee,The Concept of the Context-Aware Computing and Its Application to the Library Information Services.
[11] Aittola M,Ryhnen T,Ojala T. Smart Library-Location-Aware Mobile Library Service[M]//Human-Computer Interaction with Mobile Devices and Services. Springer Berlin Heidelberg,2003:411-416.
[12]张剑.移动时代高校图书馆的情境感知服务系统研究[J].新世纪图书馆,2012,(5):42-45,1.
[13]黄传慧.基于情境感知的图书馆学术信息推荐系统构建研究[J].图书馆工作与研究,2015,(10):21-26.
[14]洪亮,钱晨,樊星.移动数字图书馆资源的情境感知个性化推荐方法研究[J].现代图书情报技术,2016,(Z1):110-119.
[15] Mighali V,Fiore G D,Patrono L,et al. Innovative Io T-aware Services for a Smart Museum[C]//International Conference on World Wide Web. ACM,2015:547-550.
[16]张帅,郭顺利.基于情境感知的高校移动图书馆个性化推荐模型研究[J].情报探索,2014,(10):6-11.
[17]周玲元,闫思琪,朱翔宇.“智慧图书馆”情境感知服务模式及评价研究[J].图书馆学研究,2017,(21):23-30.
[18] Adomavicius G,Tuzhilin A. Context-aware Recommender Systems[C]//ACM Conference on Recommender Systems. ACM,2008:335-336.
[19]曾子明.移动环境下基于情境感知的数字图书馆个性化信息推荐研究[J].图书情报工作,2013,57(12):123-128.
[20]秦锋,任诗流,程泽凯,罗慧.基于属性加权的朴素贝叶斯分类算法[J].计算机工程与应用,2008,(6):107-109.
[21]曾子明,李鑫.移动环境下基于情境感知的个性化信息推荐[J].情报杂志,2012,31(8):166-170.