旅游个性化搜索系统的研究与实现
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摘要
本论文对旅游个性化搜索进行了研究,建立了旅游领域用户兴趣模型和主题实例(SID)模型,改进了查询扩展算法,建立了北京旅游信息知识库,分析了其距离属性、主题属性,扩展了概念属性和实例属性,实现了原型系统,满足了不同用户的需求。论文完成的主要工作如下：
(1)针对传统的用户兴趣模型的单一性,本文提出了结合用户行为信息和景点区域权重的的个性化用户兴趣模型,给出了该模型的构建框架,设计了面向不同用户的景点排序算法,并将其应用于个性化搜索中,实现了精准搜索的查询推荐功能和模糊搜索的查询扩展功能,并通过实验验证了该模型的有效性。
(2)改进了传统的关键词查询的单一模式,根据旅游领域特征,设计了相关度的互动模式,可准确定位用户搜索需求。实现了SID模型及算法,通过计算主题与主题相关度、主题与实例相关度以及实例与实例相关度,丰富了旅游领域知识信息库。改进了传统的搜索模式,并通过实验验证了该模式的有效性。
(3)基于建立的用户兴趣模型和SID模型,改进了旅游领域搜索的查询扩展功能。通过建立双模型的扩展词集,提高了用户搜索覆盖率。根据景点优先权和景点相关度大小,对搜索结果进行了再排序展示,提高了查准率。
(4)设计并实现了基于双模型的旅游个性化搜索系统,建立了知识库管理模块,该模块将景点优先权和主题相关度相结合,为搜索模块提供服务。完成了个性化搜索功能,结合用户输入的关键词和相关度,完成了个性化搜索的精准搜索、模糊搜索及推荐功能。
本论文实现了一个旅游个性化搜索原型系统,提供全面的旅游信息搜索服务,帮助用户在浩如烟海的旅游信息中找到和需求相匹配的信息。
This thesis mainly studies user Interest model in relevant fields and accomplishes subject relation model. Based on it query expansion technique is improved and tourism personalized search system is realized. The system takes the travel information of Beijing as knowledge base, analyses its distance properties, subject attribute and extends the concept attribute and instance attribute, which fully satisfies the different requirements of the users.
(1) In the traditional system, user interest model is simple, this thesis presents a personalized model that combined with user behavior information and area-weight on attractions, and gives the model frame, designs sort algorithm, and applied to personalized search, so as to realize the query recommend while precise search and query expansion function with fuzzy search, in the end, it proves the validity of the model and the accuracy through the experiment.
(2) To improve the single mode in traditional system, in which user can only input keywords, this thesis provides an interactive mode. According to the characteristics of tourism area, this thesis realizes the SDL model and relation algorithms, calculates the relation of the subjects, the concepts and the instances. We can enrich knowledge information database in the tourism domain, and facilitate the query expansion and search in later.
(3) Based on the user interest model and the SDL model, this thesis makes the improvements on query expansion skills in the field of tourism search by extending the keywords set with the double model, displays the user's demand information comprehensively. This way improves the coverage. According to the attentions priority and attentions relation, this system sorts the searching results again, which improve the precision ratio.
(4) This thesis designs and realizes the travel personalized search system based on the double model,and designs the knowledge database management module, the module combines the attractions priority and subject relation, provides services for the upper searching module. The system completes the personalized search function, which includes precise search, fuzzy search and corresponding recommend results.
The thesis implements a complete personalized search system, provides accurate comprehensive search function, makes full use of the characteristics of the domain knowledge information, combined with the user information and user behavior, launches a semantic analysis, and expands the searching keywords set.
(1)针对传统的用户兴趣模型的单一性,本文提出了结合用户行为信息和景点区域权重的的个性化用户兴趣模型,给出了该模型的构建框架,设计了面向不同用户的景点排序算法,并将其应用于个性化搜索中,实现了精准搜索的查询推荐功能和模糊搜索的查询扩展功能,并通过实验验证了该模型的有效性。
(2)改进了传统的关键词查询的单一模式,根据旅游领域特征,设计了相关度的互动模式,可准确定位用户搜索需求。实现了SID模型及算法,通过计算主题与主题相关度、主题与实例相关度以及实例与实例相关度,丰富了旅游领域知识信息库。改进了传统的搜索模式,并通过实验验证了该模式的有效性。
(3)基于建立的用户兴趣模型和SID模型,改进了旅游领域搜索的查询扩展功能。通过建立双模型的扩展词集,提高了用户搜索覆盖率。根据景点优先权和景点相关度大小,对搜索结果进行了再排序展示,提高了查准率。
(4)设计并实现了基于双模型的旅游个性化搜索系统,建立了知识库管理模块,该模块将景点优先权和主题相关度相结合,为搜索模块提供服务。完成了个性化搜索功能,结合用户输入的关键词和相关度,完成了个性化搜索的精准搜索、模糊搜索及推荐功能。
本论文实现了一个旅游个性化搜索原型系统,提供全面的旅游信息搜索服务,帮助用户在浩如烟海的旅游信息中找到和需求相匹配的信息。
This thesis mainly studies user Interest model in relevant fields and accomplishes subject relation model. Based on it query expansion technique is improved and tourism personalized search system is realized. The system takes the travel information of Beijing as knowledge base, analyses its distance properties, subject attribute and extends the concept attribute and instance attribute, which fully satisfies the different requirements of the users.
(1) In the traditional system, user interest model is simple, this thesis presents a personalized model that combined with user behavior information and area-weight on attractions, and gives the model frame, designs sort algorithm, and applied to personalized search, so as to realize the query recommend while precise search and query expansion function with fuzzy search, in the end, it proves the validity of the model and the accuracy through the experiment.
(2) To improve the single mode in traditional system, in which user can only input keywords, this thesis provides an interactive mode. According to the characteristics of tourism area, this thesis realizes the SDL model and relation algorithms, calculates the relation of the subjects, the concepts and the instances. We can enrich knowledge information database in the tourism domain, and facilitate the query expansion and search in later.
(3) Based on the user interest model and the SDL model, this thesis makes the improvements on query expansion skills in the field of tourism search by extending the keywords set with the double model, displays the user's demand information comprehensively. This way improves the coverage. According to the attentions priority and attentions relation, this system sorts the searching results again, which improve the precision ratio.
(4) This thesis designs and realizes the travel personalized search system based on the double model,and designs the knowledge database management module, the module combines the attractions priority and subject relation, provides services for the upper searching module. The system completes the personalized search function, which includes precise search, fuzzy search and corresponding recommend results.
The thesis implements a complete personalized search system, provides accurate comprehensive search function, makes full use of the characteristics of the domain knowledge information, combined with the user information and user behavior, launches a semantic analysis, and expands the searching keywords set.
引文
[1]王小林.搜索引擎的设计研究[J].计算机技术与发展,2007,17(2)：6-7.
[2]史炜.个性化搜索引擎的研究与设计[D].成都.电子科技大学,2010.
[3]李树青,韩忠愿.个性化搜索引擎原理与技术[M].科学出版社,2008.
[4]刘忠宝.个性化搜索引擎的研究与实现[D].北京.北京大学,2007.
[5]曹晓龙,宋威,梁久祯.基于TF-IQF模型和图聚类的个性化搜索研究[J].计算机工程与科学.2011,33(12)：116-120.
[6]廖军,方北湘.Internet下个性化信息检索研究[J].网络与信息.2010,(7)：8-9.
[7]王苏舰,李鹏,吕华侨.个性化搜索引擎应用于信息服务业初探[J].现代情报.2010,30(4)：16-19.
[8]张蕾.语义Web本体语言及OWL研究[J].成都信息工程学院学报,2007,22(2)：161-165.
[9]程霞,单芳.旅游网站特性对潜在游客行为意向的影响[J].四川师范大学学报：社会科学版,2011,8(1)：63-69.
[10]Li Wei-jiang, Ru Hua-suo, Zhao Tie-jun. A New Algorithm of Topical Crawler[C]. Computer Science and Engineering. WCSE'09,2009:1:443-446.
[11]聂卉.基于规则的智能检索系统推理机制的研究[J].情报学报,2008(6)：832-838.
[12]王冬.基于PageRank的页面排序改进算法[J].计算机工程与设计,2008,(11)：5921-5923.
[13]PFeng Qiu, PJunghoo Cho.Automatic identification of user interest for Personalized search[C].In:Proc of the 15th International Conference on World Wide Web. ACM Press,2006:727-736.
[14]尹砷.基于用户兴趣的个性化搜索引擎的分析与研究[D].北京.北京工商大学.2009.
[15]余正涛,宋立哲,樊孝忠.基于本体的个性化领域信息服务[J].计算机工程.2005,31(5)：22-24,81.
[16]http://baike.baidu.com/view/7893.htm.
[17]覃俊,易云飞,李林.改进k均值聚类算法在网络入侵检测中的应用研究[J].中南民族大学学报.2008,27(3)：75-78.
[18]马健,董辉.基于改进FP Growth算法在中药提取信息中研究[J].楚雄师范学院学报.2011,26(9)：17-23.
[19]Zhongming Ma, Gautam Pant, Olivia R, Liu Ssheng. Interest-Based Personalized Search[J]. ACM Transactions on Information Systems.2007.
[20]Hyun Chul Lee, Allan Borodin.Cluster Based Personalized Search[C].Computer Science.2009, (5427):167-183.
[21]Hamidreza Baghi,Yevgen Biletskiy.Improving Document Search Using Social Bookmarking[C].Lecture Notes in Computer Science.2009, (5549):183-186.
[22]Oisin Boydell.Title and Snippet Based Result Re-ranking in Collaborative Web Search[C].Lecture Notes in Computer Science.2006, (3936):484-487.
[23]U Rohini,Vamshi Ambati.Improving Re-ranking of Search Results Using Collaborative Filtering[C].Lecture Notes in Computer Science.2006,(4182):205-216.
[24]Fan Zhu, Horace H. S. Ip, Apple W. P. Fok, Jiaheng Cao.PeRES:A Personalized Recommendation Education System Based on Multi-agents & SCORM[C].Lecture Notes in Computer Science.2008,(4823):31-42.
[25]王宇.基于搜索历史的用户兴趣建模[D].上海.复旦大学硕士学位论文.2011.
[26]张磊.个性化推荐和搜索中若干关键问题的研究[D].北京.北京邮电大学.2009.
[27]张跃火.基于用户兴趣偏好模型的个性化搜索算法[D].重庆.重庆大学.2010.
[28]董超.基于主题信息服务的垂直搜索引擎的设计与实现[D].北京.北京邮电大学.2010.
[29]张璇.基于用户兴趣的个性化搜索引擎技术研究[D].长沙.湖南大学.2010.
[30]郁学,李敏强.基于PCA-SOM的混合协同过滤模型[J].系统工程理论与实践,2010,30(10)：1850-1854.
[31]于江德,樊孝忠,庞文博.基于条件随机场模型的语义角色标注[J].东南大学学报,2007,23(3)：361-364.
[32]W. Bruce Croft, Donald Metzler, Trevor Strohman.搜索引擎信息检索实践[M].北京：机械工业出版社,2010,2.
[33]许勇,宋柔.基于CRF的百科全书文本段落划分[J].计算机工程,2007,33(10)：16-18.
[34]管建和,甘剑峰.基于Lucene全文检索引擎的应用研究与实现[J].计算机工程与设计,2007,28(2)：489-491.
[35]郑榕增,林世平.基于Lucene的中文倒排索引技术的研究[J].计算机技术与发展,2010,20(3)：80-83.
[36]岳莉.基于Lucene的全文检索系统的研究与应用[D].西安.电子科技大 学.2010.
[37]http://baike.baidu.com/view/2796958.htm.
[38]王鹏,李长云.嵌入式环境下全文搜索系统的设计与实现[J].计算机工程与设计,2011,32(4)：1288-1291.
[39]杨如祥,曾献辉.一种基于网页特征提取的网站全文搜索系统的设计与实现[J].东华大学学报(自然科学版),2007,33(5)：639-643.
[40]Mingyang Sun, Weifeng Sun, Lei Shu, Mingchu Li, Lei Xue. FoSSicker:A personalized search engine by location-awareness[J]. Computing, Networking and Communications (ICNC),2012,456-460.
[41]关庆珍.基于本体的个性化信息搜索的用户模型研究[D].西南大学硕士论文,2008.
[42]刘霞,阳小华.基于本体的个性化信息检索研究[J].电脑知识与技术.2009,5(25)7079-7083.
[43]严隽薇,黄勋,刘敏,朱延波,倪亥彬.基于本体用户兴趣模型的个性化推荐算法[J].计算机集成制造系统,2010,16(12)：2757-2762.
[44]Prasad M. Deshpande, Deepak P., Krishna Kummamuru.Efficient Online top-k Retrieval with Arbitrary Similarity Measures, Proceedings of the 11th International Conference on Extending Database Technology, France, 2008:356-367.
[45]吴青,夏红霞,赵广辉,刘春燕.基于Lucene全文检索引擎的应用与改进[J].武汉理工大学学报,2008,(07)：145-148.
[46]南智敏,钱松荣.引入漂移特性的用户兴趣模型优化研究[J].微型电脑应用,2012,28(3)：30-32.
[47]施雅贤,李璞,肖宝,韦婷婷,蒋运承.面向旅游领域的语义查询扩展方法[J].软件技术与数据库,2010,36(18)：43-45.
[48]李兆龙.基于领域本体的旅游信息检索系统研究与实现[D].北京.北京邮电大学.2012.
[49]李荣,杨冬,刘磊.基于本体的概念相似度计算方法研究[J].计算机研究与发展,2011,48(z2)：154-156.
[50]李卫疆,赵铁军,王宪刚.基于上下文的查询扩展[J].计算机研究与发展,2010,47(2)：300-304.
[2]史炜.个性化搜索引擎的研究与设计[D].成都.电子科技大学,2010.
[3]李树青,韩忠愿.个性化搜索引擎原理与技术[M].科学出版社,2008.
[4]刘忠宝.个性化搜索引擎的研究与实现[D].北京.北京大学,2007.
[5]曹晓龙,宋威,梁久祯.基于TF-IQF模型和图聚类的个性化搜索研究[J].计算机工程与科学.2011,33(12)：116-120.
[6]廖军,方北湘.Internet下个性化信息检索研究[J].网络与信息.2010,(7)：8-9.
[7]王苏舰,李鹏,吕华侨.个性化搜索引擎应用于信息服务业初探[J].现代情报.2010,30(4)：16-19.
[8]张蕾.语义Web本体语言及OWL研究[J].成都信息工程学院学报,2007,22(2)：161-165.
[9]程霞,单芳.旅游网站特性对潜在游客行为意向的影响[J].四川师范大学学报：社会科学版,2011,8(1)：63-69.
[10]Li Wei-jiang, Ru Hua-suo, Zhao Tie-jun. A New Algorithm of Topical Crawler[C]. Computer Science and Engineering. WCSE'09,2009:1:443-446.
[11]聂卉.基于规则的智能检索系统推理机制的研究[J].情报学报,2008(6)：832-838.
[12]王冬.基于PageRank的页面排序改进算法[J].计算机工程与设计,2008,(11)：5921-5923.
[13]PFeng Qiu, PJunghoo Cho.Automatic identification of user interest for Personalized search[C].In:Proc of the 15th International Conference on World Wide Web. ACM Press,2006:727-736.
[14]尹砷.基于用户兴趣的个性化搜索引擎的分析与研究[D].北京.北京工商大学.2009.
[15]余正涛,宋立哲,樊孝忠.基于本体的个性化领域信息服务[J].计算机工程.2005,31(5)：22-24,81.
[16]http://baike.baidu.com/view/7893.htm.
[17]覃俊,易云飞,李林.改进k均值聚类算法在网络入侵检测中的应用研究[J].中南民族大学学报.2008,27(3)：75-78.
[18]马健,董辉.基于改进FP Growth算法在中药提取信息中研究[J].楚雄师范学院学报.2011,26(9)：17-23.
[19]Zhongming Ma, Gautam Pant, Olivia R, Liu Ssheng. Interest-Based Personalized Search[J]. ACM Transactions on Information Systems.2007.
[20]Hyun Chul Lee, Allan Borodin.Cluster Based Personalized Search[C].Computer Science.2009, (5427):167-183.
[21]Hamidreza Baghi,Yevgen Biletskiy.Improving Document Search Using Social Bookmarking[C].Lecture Notes in Computer Science.2009, (5549):183-186.
[22]Oisin Boydell.Title and Snippet Based Result Re-ranking in Collaborative Web Search[C].Lecture Notes in Computer Science.2006, (3936):484-487.
[23]U Rohini,Vamshi Ambati.Improving Re-ranking of Search Results Using Collaborative Filtering[C].Lecture Notes in Computer Science.2006,(4182):205-216.
[24]Fan Zhu, Horace H. S. Ip, Apple W. P. Fok, Jiaheng Cao.PeRES:A Personalized Recommendation Education System Based on Multi-agents & SCORM[C].Lecture Notes in Computer Science.2008,(4823):31-42.
[25]王宇.基于搜索历史的用户兴趣建模[D].上海.复旦大学硕士学位论文.2011.
[26]张磊.个性化推荐和搜索中若干关键问题的研究[D].北京.北京邮电大学.2009.
[27]张跃火.基于用户兴趣偏好模型的个性化搜索算法[D].重庆.重庆大学.2010.
[28]董超.基于主题信息服务的垂直搜索引擎的设计与实现[D].北京.北京邮电大学.2010.
[29]张璇.基于用户兴趣的个性化搜索引擎技术研究[D].长沙.湖南大学.2010.
[30]郁学,李敏强.基于PCA-SOM的混合协同过滤模型[J].系统工程理论与实践,2010,30(10)：1850-1854.
[31]于江德,樊孝忠,庞文博.基于条件随机场模型的语义角色标注[J].东南大学学报,2007,23(3)：361-364.
[32]W. Bruce Croft, Donald Metzler, Trevor Strohman.搜索引擎信息检索实践[M].北京：机械工业出版社,2010,2.
[33]许勇,宋柔.基于CRF的百科全书文本段落划分[J].计算机工程,2007,33(10)：16-18.
[34]管建和,甘剑峰.基于Lucene全文检索引擎的应用研究与实现[J].计算机工程与设计,2007,28(2)：489-491.
[35]郑榕增,林世平.基于Lucene的中文倒排索引技术的研究[J].计算机技术与发展,2010,20(3)：80-83.
[36]岳莉.基于Lucene的全文检索系统的研究与应用[D].西安.电子科技大 学.2010.
[37]http://baike.baidu.com/view/2796958.htm.
[38]王鹏,李长云.嵌入式环境下全文搜索系统的设计与实现[J].计算机工程与设计,2011,32(4)：1288-1291.
[39]杨如祥,曾献辉.一种基于网页特征提取的网站全文搜索系统的设计与实现[J].东华大学学报(自然科学版),2007,33(5)：639-643.
[40]Mingyang Sun, Weifeng Sun, Lei Shu, Mingchu Li, Lei Xue. FoSSicker:A personalized search engine by location-awareness[J]. Computing, Networking and Communications (ICNC),2012,456-460.
[41]关庆珍.基于本体的个性化信息搜索的用户模型研究[D].西南大学硕士论文,2008.
[42]刘霞,阳小华.基于本体的个性化信息检索研究[J].电脑知识与技术.2009,5(25)7079-7083.
[43]严隽薇,黄勋,刘敏,朱延波,倪亥彬.基于本体用户兴趣模型的个性化推荐算法[J].计算机集成制造系统,2010,16(12)：2757-2762.
[44]Prasad M. Deshpande, Deepak P., Krishna Kummamuru.Efficient Online top-k Retrieval with Arbitrary Similarity Measures, Proceedings of the 11th International Conference on Extending Database Technology, France, 2008:356-367.
[45]吴青,夏红霞,赵广辉,刘春燕.基于Lucene全文检索引擎的应用与改进[J].武汉理工大学学报,2008,(07)：145-148.
[46]南智敏,钱松荣.引入漂移特性的用户兴趣模型优化研究[J].微型电脑应用,2012,28(3)：30-32.
[47]施雅贤,李璞,肖宝,韦婷婷,蒋运承.面向旅游领域的语义查询扩展方法[J].软件技术与数据库,2010,36(18)：43-45.
[48]李兆龙.基于领域本体的旅游信息检索系统研究与实现[D].北京.北京邮电大学.2012.
[49]李荣,杨冬,刘磊.基于本体的概念相似度计算方法研究[J].计算机研究与发展,2011,48(z2)：154-156.
[50]李卫疆,赵铁军,王宪刚.基于上下文的查询扩展[J].计算机研究与发展,2010,47(2)：300-304.
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