面向盲人的个性化图书搜索系统
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摘要
21世纪互联网和信息技术的发展正前所未有地影响和改变着人们的工作、学习和生活。互联网和信息技术的进步在给人们带来快捷、便利的同时,也给残疾人带来了新的机遇和挑战。如何有效解决残疾人群体在互联网时代的信息获取问题,已成为一个关乎社会发展的重要问题。信息无障碍理念的提出正是对这一亟需解决问题的正面回应。如何推广和发展信息无障碍理念,利用信息无障碍技术为残疾人群体构建良好的信息获取平台是解决这一问题的有效方式。
中国盲人数字图书馆的开设为盲人用户提供了一个很好的综合性阅读服务平台,然而对于盲人而言,如何快速定位所需信息则是他们在实际使用中遇到的最大障碍。因此在提供丰富电子图书资源的基础上,提供一个便捷、高效的图书搜索服务至关重要。本文就盲人用户的图书查找问题,提出一种面向盲人的个性化图书搜索方法。首先从用户输入的查询词入手,通过定义查询有向关联图来表达用户搜索与阅读行为间的关联关系,然后通过查询修正的方法来对用户原查询进行扩展,消除查询词模糊、歧义所带来的影响；然后在基于图书元数据的全文检索基础上,利用用户使用数据分析用户潜在兴趣,通过合理的用户行为建模来产生个性化排序,提供符合用户偏好的个性化搜索结果。最终针对盲人用户的访问特点,提供了无障碍的搜索页面,实现了原型系统。
In 21st century, the development of Internet and information technology is affecting and changing people's work, study and life unprecedently. While the progress in Internet and information technology brings convenience, it also brings new challenges and opportunities to people with disabilities. How to effectively solve their information access problem is becoming a major issue related to social development. The concept of Information Accessibility is a positive response to this issue. How to promote and develop this concept, make use of any related techniques to build a good information platform is an effective solution.
China Digital Library for Visual Impairment is a comprehensive reading site for the visually impaired. To blind people, the main obstacle while accessing to site is how to locate the information which they need. Therefore, it is essential to provide a convenient and effective book search service on the fundament of rich book resources. Considering the problem of finding needed book, this paper proposed a personalized book search method for visually impaired. Firstly take user input into account, we use a query modification method with constructing a user-query correlated graph to expand user origin query and diminish the query's ambiguity and fuzzy. Then, on the fundament of book metadata based full-text retrieval, we analyze the user data for user behavior modeling and provide a personalized book rank result which is consistent with user preference. Finally, we build a barrier-free book search interface and implement the prototype for visually impaired, according to their usage habit.
中国盲人数字图书馆的开设为盲人用户提供了一个很好的综合性阅读服务平台,然而对于盲人而言,如何快速定位所需信息则是他们在实际使用中遇到的最大障碍。因此在提供丰富电子图书资源的基础上,提供一个便捷、高效的图书搜索服务至关重要。本文就盲人用户的图书查找问题,提出一种面向盲人的个性化图书搜索方法。首先从用户输入的查询词入手,通过定义查询有向关联图来表达用户搜索与阅读行为间的关联关系,然后通过查询修正的方法来对用户原查询进行扩展,消除查询词模糊、歧义所带来的影响；然后在基于图书元数据的全文检索基础上,利用用户使用数据分析用户潜在兴趣,通过合理的用户行为建模来产生个性化排序,提供符合用户偏好的个性化搜索结果。最终针对盲人用户的访问特点,提供了无障碍的搜索页面,实现了原型系统。
In 21st century, the development of Internet and information technology is affecting and changing people's work, study and life unprecedently. While the progress in Internet and information technology brings convenience, it also brings new challenges and opportunities to people with disabilities. How to effectively solve their information access problem is becoming a major issue related to social development. The concept of Information Accessibility is a positive response to this issue. How to promote and develop this concept, make use of any related techniques to build a good information platform is an effective solution.
China Digital Library for Visual Impairment is a comprehensive reading site for the visually impaired. To blind people, the main obstacle while accessing to site is how to locate the information which they need. Therefore, it is essential to provide a convenient and effective book search service on the fundament of rich book resources. Considering the problem of finding needed book, this paper proposed a personalized book search method for visually impaired. Firstly take user input into account, we use a query modification method with constructing a user-query correlated graph to expand user origin query and diminish the query's ambiguity and fuzzy. Then, on the fundament of book metadata based full-text retrieval, we analyze the user data for user behavior modeling and provide a personalized book rank result which is consistent with user preference. Finally, we build a barrier-free book search interface and implement the prototype for visually impaired, according to their usage habit.
引文
[1]C. Stephanidis, A. Savidis. Universal access in the information society:methods, tools, and interaction technologies. Universal Access in the Information Society, 2001, 1(1):40-55.
[2]徐恩元,张赟玥.我国信息无障碍建设进程探究.图书馆论坛,2009,29(6)：237-240.
[3]赵英,张培宾.针对视障人士的Web无障碍网站标准及设计研究.情报杂志,2010,29(4)：138-141.
[4]陈洪猛.全文检索技术的研究与实现[硕士学位论文].北京工业大学,2008.
[5]申展,江宝林等.全文检索模型综述.计算机科学,2004,31(5)：61-64.
[6]Donald E. Knuth. The art of Computer Programming:Sorting and Searching. Addison-Wesley Pub.1998.
[7]Gaston H. Gonnet, Ricardo A. Baeza-Yates, Tim Snider. New indices for text:PAT Trees and PAT arrays. Information Retrieval Data Structures & Algorithms, Prentice Hall,1992.
[8]G. Salton, J. Allan, A. Singhal. Automatic text decomposition and structuring. Information Processing and Management,1996,32(2):127-138.
[9]G. Salton, A. Singhal, M. Mitra, C. Buckley. Automatic text structuring and summarization. Information Processing and Management,1997,33(2):193-207.
[10]G. Salton, A. Wong, C. S. Yang. A vector space model for automatic indexing. Communications of the ACM,1975,18(11):620.
[11]S. Robertson. Understanding inverse document frequency:on theoretical arguments for IDF. Journal of documentation,2004,60:503-520.
[12]A. Micarelli, F. Gasparetti, F. Sciarrone and S. Gauch. Personalized Search On World Wide Web. The Adaptive Web,2007,6:195-230
[13]D. Goldberg, D. Nichols, B.M. Oki, D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM,1992,35(12): 61-70.
[14]A. Micarelli, F. Sciarrone. Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Modeling and User-Adapted Interaction,2004,14(2-3):159-200.
[15]S. Lawrence, C.L.Giles. Context and page analysis for improved web search. IEEE Internet Computing,1998,2(4):38-46.
[16]A. Spink, B.J. Jansen. A study of web search trends. Webology,2004,1(2):4
[17]J. Pitkow et al. Personalized Search. Communication of the ACM,2002, 45(9):50-55.
[18]E. Vellerman, C. Strobbe, J. Koch, C.A. Velasco and M. Snaprud. A unified Web evaluation methodology using WCAG. Lecture Notes in Computer Science,2007, 4556:177-184.
[19]D. Mladenic. Personal Web Watcher:Implementation and Design. Technical Report. IJS-DP-7472, Pittsburgh:Carnegie Mellon University,1996.
[20]M. Pazzani, J. Muramatsu, D. Billsus. Syskill&Webert:Identifying Interesting Web Sites. In Proceedings of Thirteenth National Conference on Artificial Intelligence. Menlo Park:AAAI Press,1996,54-61.
[21]N. Good, J. Schafer, J. Konstan, A. Borchers et al. Combining Collaborative Filtering with Personal Agents for Better Recommendations. In Proceedings of AAAI'99. Menlo Park:AAAI Press,1999,439-446.
[22]魏毅峰,张亮.基于本体的搜索引擎模型设计.软件导刊,2010,9(7)：118-119.
[23]吴丽花,刘鲁.个性化推荐系统用户建模技术综述.情报学报,2006,25(1)：55-62.
[24]R. Rafter, K. Bradley, B. Smyth. Personalized Retrieval for Online Recruitment Services.22nd Annual Colloquium On IR Research,2000.
[25]J.A. Konstan et al. GroupLens:applying collaborative filtering to Usenet news. Communications of ACM,1997,40(3):77-87.
[26]G. Ziyu, W. Can et al. Document Recommendation in Social Tagging Services. WWW'10,2010,391-400.
[27]G. Koutrika, Y. Ioannidis. A Unified User Profile Framework for Query Disambiguation and Personalization. PIA2005,44-53.
[28]刘群,李素建.基于《知网》的词汇语义相似度计算.第三届汉语词汇语义学研讨会,2002.
[29]周粉,夏幼明.一种改进的基于知网的语义相似度计算方法.云南大学学报(自然科学版),2008,30(S2)：215-218.
[30]E. Hatcher,O. Gospodnetic. Lucene in Action. Greenwich:Manning Press,2004.
[31]G. Adomavicius, A. Tuzhilin. Toward the Next Generation of Recommender Systems:A Survery of the state-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-748.
[32]余小高,余小鹏.基于隐式评分的推荐系统研究.计算机应用,2009,29(6)：1585-1589.
[33]秦光洁,张颖.基于综合兴趣度的协同过滤推荐算法.计算机工程,2009,35(17)：81-83.
[34]丛晓琪,杨怀珍,刘枚莲.基于时间加权的协同过滤算法研究.计算机应用与软件,2009,26(8)：120-121.
[35]王岚,瞿正军,基于时间加权的协同过滤算法.计算机应用,2007,27(9)：2302-2303.
[2]徐恩元,张赟玥.我国信息无障碍建设进程探究.图书馆论坛,2009,29(6)：237-240.
[3]赵英,张培宾.针对视障人士的Web无障碍网站标准及设计研究.情报杂志,2010,29(4)：138-141.
[4]陈洪猛.全文检索技术的研究与实现[硕士学位论文].北京工业大学,2008.
[5]申展,江宝林等.全文检索模型综述.计算机科学,2004,31(5)：61-64.
[6]Donald E. Knuth. The art of Computer Programming:Sorting and Searching. Addison-Wesley Pub.1998.
[7]Gaston H. Gonnet, Ricardo A. Baeza-Yates, Tim Snider. New indices for text:PAT Trees and PAT arrays. Information Retrieval Data Structures & Algorithms, Prentice Hall,1992.
[8]G. Salton, J. Allan, A. Singhal. Automatic text decomposition and structuring. Information Processing and Management,1996,32(2):127-138.
[9]G. Salton, A. Singhal, M. Mitra, C. Buckley. Automatic text structuring and summarization. Information Processing and Management,1997,33(2):193-207.
[10]G. Salton, A. Wong, C. S. Yang. A vector space model for automatic indexing. Communications of the ACM,1975,18(11):620.
[11]S. Robertson. Understanding inverse document frequency:on theoretical arguments for IDF. Journal of documentation,2004,60:503-520.
[12]A. Micarelli, F. Gasparetti, F. Sciarrone and S. Gauch. Personalized Search On World Wide Web. The Adaptive Web,2007,6:195-230
[13]D. Goldberg, D. Nichols, B.M. Oki, D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM,1992,35(12): 61-70.
[14]A. Micarelli, F. Sciarrone. Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Modeling and User-Adapted Interaction,2004,14(2-3):159-200.
[15]S. Lawrence, C.L.Giles. Context and page analysis for improved web search. IEEE Internet Computing,1998,2(4):38-46.
[16]A. Spink, B.J. Jansen. A study of web search trends. Webology,2004,1(2):4
[17]J. Pitkow et al. Personalized Search. Communication of the ACM,2002, 45(9):50-55.
[18]E. Vellerman, C. Strobbe, J. Koch, C.A. Velasco and M. Snaprud. A unified Web evaluation methodology using WCAG. Lecture Notes in Computer Science,2007, 4556:177-184.
[19]D. Mladenic. Personal Web Watcher:Implementation and Design. Technical Report. IJS-DP-7472, Pittsburgh:Carnegie Mellon University,1996.
[20]M. Pazzani, J. Muramatsu, D. Billsus. Syskill&Webert:Identifying Interesting Web Sites. In Proceedings of Thirteenth National Conference on Artificial Intelligence. Menlo Park:AAAI Press,1996,54-61.
[21]N. Good, J. Schafer, J. Konstan, A. Borchers et al. Combining Collaborative Filtering with Personal Agents for Better Recommendations. In Proceedings of AAAI'99. Menlo Park:AAAI Press,1999,439-446.
[22]魏毅峰,张亮.基于本体的搜索引擎模型设计.软件导刊,2010,9(7)：118-119.
[23]吴丽花,刘鲁.个性化推荐系统用户建模技术综述.情报学报,2006,25(1)：55-62.
[24]R. Rafter, K. Bradley, B. Smyth. Personalized Retrieval for Online Recruitment Services.22nd Annual Colloquium On IR Research,2000.
[25]J.A. Konstan et al. GroupLens:applying collaborative filtering to Usenet news. Communications of ACM,1997,40(3):77-87.
[26]G. Ziyu, W. Can et al. Document Recommendation in Social Tagging Services. WWW'10,2010,391-400.
[27]G. Koutrika, Y. Ioannidis. A Unified User Profile Framework for Query Disambiguation and Personalization. PIA2005,44-53.
[28]刘群,李素建.基于《知网》的词汇语义相似度计算.第三届汉语词汇语义学研讨会,2002.
[29]周粉,夏幼明.一种改进的基于知网的语义相似度计算方法.云南大学学报(自然科学版),2008,30(S2)：215-218.
[30]E. Hatcher,O. Gospodnetic. Lucene in Action. Greenwich:Manning Press,2004.
[31]G. Adomavicius, A. Tuzhilin. Toward the Next Generation of Recommender Systems:A Survery of the state-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-748.
[32]余小高,余小鹏.基于隐式评分的推荐系统研究.计算机应用,2009,29(6)：1585-1589.
[33]秦光洁,张颖.基于综合兴趣度的协同过滤推荐算法.计算机工程,2009,35(17)：81-83.
[34]丛晓琪,杨怀珍,刘枚莲.基于时间加权的协同过滤算法研究.计算机应用与软件,2009,26(8)：120-121.
[35]王岚,瞿正军,基于时间加权的协同过滤算法.计算机应用,2007,27(9)：2302-2303.