Please wait a minute...
Advanced Search
现代图书情报技术  2015, Vol. 31 Issue (3): 58-66    DOI: 10.11925/infotech.1003-3513.2015.03.08
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
一种基于本体和位置感知的图书馆书籍推荐模型
李胜, 王叶茂
中南财经政法大学信息与安全工程学院 武汉 430074
An Ontology-based and Location-aware Book Recommendation Model in Library
Li Sheng, Wang Yemao
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430074, China
全文: PDF(752 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

[目的]改善图书馆的推荐服务, 帮助用户选择感兴趣的书籍资源。[方法]结合Wi-Fi室内定位技术, 提出一种基于本体和具有位置感知的图书馆书籍推荐模型。通过构建书籍分类本体, 结合用户偏好和区域组偏好, 在考虑推荐处理触发机制问题下进行推荐。[结果]与现有综合本体和协同过滤方法相比, 提出的模型在推荐精度和相关度上分别提高13.56%和21.79%, 相比单纯基于内容过滤的方法, 推荐结果的集合多样性提高48.03%。[局限]未讨论推荐模型中个人书籍偏好和区域组偏好的权重。[结论]本研究有利于改善图书馆的推荐服务, 提供位置相关的个性化书籍推荐。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
李胜
王叶茂
关键词 本体基于位置的书籍推荐组模型图书馆构建    
Abstract

[Objective] Improve the library recommendation service and help readers select interested books. [Methods] This paper proposes an Ontology-based and location-aware book recommendation model in library by applying Wi-Fi indoor positioning technology, which constructs user's profile based on the books classification Ontology, and then recommends books by combining regional group profile and considering the problem of when to make a recommendation. [Results] The proposed method outperforms the existing Ontology-based hybrid recommend­dation method in accuracy and correlation by 13.6% and 21.8% respectively, and shows 48.03% increase in the set diversity compared with the content-based filtering method. [Limitations] The weights of user preferences and regional group preferences in the recommendation model are not discussed. [Conclusions] This research can improve the library recommendation service and provide location-aware personalized book recommendation.

Key wordsOntology    Location-aware book recommendation    Group modeling    Library building
收稿日期: 2014-09-15     
:  G202  
通讯作者: 王叶茂, ORCID: 0000-0002-2183-558X, E-mail: yemao_wang@126.com。     E-mail: yemao_wang@126.com
作者简介: 作者贡献声明: 李胜:确定研究方向及研究方法,提出论文的修订意见;王叶茂:进行算法设计及实验分析,撰写与修订论文。
引用本文:   
李胜, 王叶茂. 一种基于本体和位置感知的图书馆书籍推荐模型[J]. 现代图书情报技术, 2015, 31(3): 58-66.
Li Sheng, Wang Yemao. An Ontology-based and Location-aware Book Recommendation Model in Library. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.03.08.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.03.08

[1] Hahn J. Mobile Learning for The Twenty-first Century Librarian [J]. Reference Services Review, 2008, 36(3): 272-288.
[2] Mills K. M-Libraries: Information Use on the Move [R]. University of Cambridge, 2009.
[3] 石林, 徐飞, 徐守坤. 基于用户兴趣建模的个性化推荐[J].计算机应用与软件, 2013, 30(12): 211-214. (Shi Lin, Xu Fei, Xu Shoukun. Personalized Recommendation Based on Users' Interest Modeling [J]. Computer Application and Software, 2013, 30(12): 211-214.)
[4] Gruber T R. A Translation Approach to Portable Ontology Specification [J]. Knowledge Acquisition, 1993, 5(2): 199-220.
[5] Yan D W, Cen Y H, Zhang W, et al. Ontology-based Framework for Personalized Recommendation in Digital Libraries [J]. Journal of Southeast University: English Edition, 2006, 22(3): 385-388.
[6] 丁雪, 张玉峰. 基于本体的智能数字图书馆个性化推荐用户本体研究[J]. 现代情报, 2009, 29(12): 61-65. (Ding Xue, Zhang Yufeng. Research on User Ontology in Personalized Recommendation of Ontology-based Intelligent Digital Library [J]. Journal of Modern Information, 2009, 29(12): 61-65.)
[7] 汪英姿. 基于本体的个性化图书推荐方法研究[J].现代图书情报技术, 2012(12): 72-78. (Wang Yingzi. Research on Ontology-based Personalized Recommendation Method for Library Resources [J]. New Technology of Library and Information Service, 2012(12): 72-78.)
[8] 唐晓玲. 基于本体和协同过滤技术的推荐系统研究[J]. 情报科学, 2013, 31(12): 90-94. (Tang Xiaoling. Research on Recommendation System Based on Ontology and Collabora­tive Filtering Technology [J]. Information Science, 2013, 31(12): 90-94.)
[9] Hahn J. Location-based Recommendation Services in Library Book Stacks [J]. Reference Services Review, 2011, 39(4): 654-674.
[10] Chen C. An Intelligent Mobile Location-aware Book Recommendation System That Enhances Problem-based Learning in Libraries [J]. Interactive Learning Environments, 2013, 21(5): 469-495.
[11] 任沁. 基于领域本体的数字图书馆用户兴趣建模研究[D].武汉: 湖北工业大学, 2012. (Ren Qin. Research on User Interest Modeling in Digital Library Based on Domain Ontology [D]. Wuhan: Hubei University of Technology, 2012.)
[12] Li L, Zheng L, Yang F, et al. Modeling and Broadening Temporal User Interest in Personalized News Recommen­dation [J]. Expert Systems with Applications, 2014, 41(7): 3168-3177.
[13] Sugiyama K, Hatano K, Yoshikawa M. Adaptive Web Search Based on User Profile Constructed without Any Effort from Users [C]. In: Proceedings of the 13th International Conference on World Wide Web. New York: ACM, 2004: 675-684.
[14] Ganesan P, Garcua-molina H, Widom J. Exploiting Hierarchical Domain Structure to Compute Similarity [J]. ACM Transaction on Information System, 2003, 21(1): 64-93.
[15] 王洪伟, 邹莉. 考虑长期与短期兴趣因素的用户偏好建模[J]. 同济大学学报: 自然科学版, 2013, 41(6): 953-960. (Wang Hongwei, Zou Li. Modeling Users'Preference Based on Long-term and Short-term Interests [J]. Journal of Tongji University: Nature Science, 2013, 41(6): 953-960.)
[16] Cantador I, Szomszor M, Alani H, et al. Enriching Ontological User Profiles with Tagging History for Multi-domain Recommendations [C]. In: Proceedings of the 1st International Workshop on Collective Semantics: Collective Intelligence and the Semantic Web (CISWeb'08), Tenerife, Spain. 2008:5-19.
[17] Cantador I, Bellogin A, Castells P. Ontology-based Personalized and Context-aware Recommendations of New Items [C]. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT'08). Washington, DC: IEEE Computer Society, 2008: 562-565.
[18] Kang J, Choi J. An Ontology-based Recommendation System Using Long-term and Short-term Preferences [C]. In: Proceedings of 2011 International Conference on Information Science and Applications (ICISA), Jeju Island, South Korea. IEEE, 2011: 1-8.
[19] Yu Z, Zhou X, Hao Y, et al.TV Program Recommendation for Multiple Viewers Based on User Profile Merging[J].User Modeling and User-Adapted Interaction, 2006, 16(1): 63-82.
[20] Woerndl W, Huebner J, Bader R, et al. A Model for Proactivity in Mobile, Context-aware Recommender Systems [C]. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys'11). New York: ACM, 2011: 273-276.
[21] Vallet D, Castells P, Fernández M, et al. Personalized Content Retrieval in Context Using Ontological Knowledge [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(3): 336-346.
[22] Zhang M, Hurley N. Avoiding Monotony: Improving the Diversity of Recommendation Lists [C]. In: Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys'08 ). New York: ACM, 2008: 123-130.

[1] 邓诗琦,洪亮. 面向智能应用的领域本体构建研究*——以反电话诈骗领域为例[J]. 数据分析与知识发现, 2019, 3(7): 73-84.
[2] 高广尚. 用户画像构建方法研究综述*[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[3] 王颖,钱力,谢靖,常志军,孔贝贝. 科技大数据知识图谱构建模型与方法研究*[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
[4] 何有世,何述芳. 基于领域本体的产品网络口碑信息多层次细粒度情感挖掘*[J]. 数据分析与知识发现, 2018, 2(8): 60-68.
[5] 唐慧慧,王昊,张紫玄,王雪颖. 基于汉字标注的中文历史事件名抽取研究*[J]. 数据分析与知识发现, 2018, 2(7): 89-100.
[6] 庞贝贝,苟娟琼,穆文歆. 面向高校学生深度辅导领域的主题建模和主题上下位关系识别研究*[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
[7] 丁晟春,刘梦露,傅柱. 概念设计中基于知识流的多维设计知识统一建模技术研究*[J]. 数据分析与知识发现, 2018, 2(2): 11-19.
[8] 涂海丽,唐晓波. 基于标签的商品推荐模型研究*[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
[9] 陈二静,姜恩波. 文本相似度计算方法研究综述[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[10] 白如江,冷伏海,廖君华. 一种基于语义组块特征的改进Cosine文本相似度计算方法*[J]. 数据分析与知识发现, 2017, 1(6): 56-64.
[11] 吴丹,刘畅,李翼. 用户步行导航过程中的情感变化研究*[J]. 数据分析与知识发现, 2017, 1(5): 42-51.
[12] 刘健,毕强,刘庆旭,王福. 数字文献资源内容服务推荐研究*——基于本体规则推理和语义相似度计算[J]. 现代图书情报技术, 2016, 32(9): 70-77.
[13] 丁恒,陆伟. 标准文献知识服务系统设计与实现*[J]. 现代图书情报技术, 2016, 32(7-8): 120-128.
[14] 陆佳莹,袁勤俭,黄奇,钱韵洁. 基于概念格理论的产品领域本体构建研究*[J]. 现代图书情报技术, 2016, 32(5): 38-46.
[15] 张磊,马静,李丹丹,沈洋. 语义社会网络的超网络模型构建及关键节点自动化识别方法研究*[J]. 现代图书情报技术, 2016, 32(3): 8-17.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn