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Статті в журналах з теми "PERSONALIZED QUERY"
Shen, Chih-Ya, Shao-Heng Ko, Guang-Siang Lee, Wang-Chien Lee, and De-Nian Yang. "Density Personalized Group Query." Proceedings of the VLDB Endowment 16, no. 4 (December 2022): 615–28. http://dx.doi.org/10.14778/3574245.3574249.
Повний текст джерелаChen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (April 2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.
Повний текст джерелаYao, Jing, Zhicheng Dou, and Ji-Rong Wen. "Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–29. http://dx.doi.org/10.1145/3470564.
Повний текст джерелаJiang, Dan-yang, and Hong-hui Chen. "Cohort-based personalized query auto-completion." Frontiers of Information Technology & Electronic Engineering 20, no. 9 (September 2019): 1246–58. http://dx.doi.org/10.1631/fitee.1800010.
Повний текст джерелаLIANG, Yaopei, and Dingming WU. "Location-aware personalized keyword query recommendation." Journal of Shenzhen University Science and Engineering 36, no. 04 (July 1, 2019): 467–72. http://dx.doi.org/10.3724/sp.j.1249.2019.04467.
Повний текст джерелаKhemmarat, Samamon, and Lixin Gao. "Predictive and Personalized Drug Query System." IEEE Journal of Biomedical and Health Informatics 21, no. 4 (July 2017): 1146–55. http://dx.doi.org/10.1109/jbhi.2016.2562183.
Повний текст джерелаHan, Meng, and Xiao Hu Qiu. "Personalized Search Engineer Model." Advanced Materials Research 268-270 (July 2011): 1216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1216.
Повний текст джерелаZhu, Qiuyu, Dongmei Li, Cong Dai, Qichen Han, and Yi Lin. "PLSA-Based Personalized Information Retrieval with Network Regularization." Journal of Information Technology Research 12, no. 1 (January 2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.
Повний текст джерелаXie, Jin, Fuxi Zhu, Huanmei Guan, Jiangqing Wang, Hao Feng, and Lin Zheng. "Personalized query recommendation using semantic factor model." China Communications 18, no. 8 (August 2021): 169–82. http://dx.doi.org/10.23919/jcc.2021.08.012.
Повний текст джерелаChunguang Ma, Lei Zhang, Songtao Yang, Xiaodong Zheng, and Pinhui Ke. "Achieve personalized anonymity through query blocks exchanging." China Communications 13, no. 11 (November 2016): 106–18. http://dx.doi.org/10.1109/cc.2016.7781722.
Повний текст джерелаДисертації з теми "PERSONALIZED QUERY"
Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.
Повний текст джерелаFischer, Stefan. "Personalized query result presentation and offer composition for E-procurement applications." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973118377.
Повний текст джерелаAsfari, Ounas. "Personalized Access to Contextual Information by using an Assistant for Query Reformulation." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112126.
Повний текст джерелаAccess to relevant information adapted to the needs and the context of the user is areal challenge in Web Search, owing to the increases of heterogeneous resources andthe varied data on the web. There are always certain needs behind the user query,these queries are often ambiguous and shortened, and thus we need to handle thesequeries intelligently to satisfy the user’s needs. For improving user query processing,we present a context-based hybrid method for query expansion that automaticallygenerates new reformulated queries in order to guide the information retrieval systemto provide context-based personalized results depending on the user profile andhis/her context. Here, we consider the user context as the actual state of the task thatthe user is undertaking when the information retrieval process takes place. Thus StateReformulated Queries (SRQ) are generated according to the task states and the userprofile which is constructed by considering related concepts from existing concepts ina domain ontology. Using a task model, we will show that it is possible to determinethe user’s current task automatically. We present an experimental study in order toquantify the improvement provided by our system compared to the direct querying ofa search engine without reformulation, or compared to the personalized reformulationbased on a user profile only. The Preliminary results have proved the relevance of ourapproach in certain contexts
Limbu, Dilip Kumar. "Contextual information retrieval from the WWW." Click here to access this resource online, 2008. http://hdl.handle.net/10292/450.
Повний текст джерелаSeher, Indra. "A personalised query expansion approach using context." Thesis, View thesis, 2007. http://handle.uws.edu.au:8081/1959.7/33427.
Повний текст джерелаSeher, Indra. "A personalised query expansion approach using context." View thesis, 2007. http://handle.uws.edu.au:8081/1959.7/33427.
Повний текст джерелаA thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy to the College of Health & Science, School of Computing and Mathematics, University of Western Sydney. Includes bibliography.
KATIYAR, ANTRA. "A CONTEXT SENSITIVE AND PERSONALIZED QUERY AUTOCOMPLETION TECHNIQUE." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15941.
Повний текст джерелаElshaweesh, Omar Ghaleb Mohammad. "Intelligent personalized approaches for semantic search and query expansion." Thesis, 2019. http://hdl.handle.net/10453/133361.
Повний текст джерелаIn today’s highly advanced technological world, the Internet has taken over all aspects of human life. Many services are advertised and provided to the users through online channels. The user looks for services and obtains them through different search engines. To obtain the best results that meet the needs and requirements of the users, researchers have extensively studied methods such as different personalization methods by which to improve the performance and efficiency of the retrieval process. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Recently, semantic techniques have had a significant importance in the field of personalized querying and personalized web search engines. This thesis focuses on both processes of personalized web search engines, first the reformulation of queries and second ranking query results. The importance of personalized web search lies in its ability to identify users' interests based on their personal profiles. This work contributes to personalized web search services in three aspects. These contributions can be summarized as follows: First, it creates user profiles based on a user’s browsing behaviour, as well as the semantic knowledge of a domain ontology, aiming to improve the quality of the search results. However, it is not easy to acquire personalized web search results, hence one of the problems that is encountered in this approach is how to get a precise representation of the user interests, as well as how to use it to find search results. The second contribution builds on the first contribution. A personalized web search approach is introduced by integrating user context history into the information retrieval process. This integration process aims to provide search results that meet the user’s needs. It also aims to create contextual profiles for the user based on several basic factors: user temporal behaviour during browsing, semantic knowledge of a specific domain ontology, as well as an algorithm based on re-ranking the search results. The previous solutions were related to the re-ranking of the returned search results to match the user’s requirements. The third contribution includes a comparison of three-term weight methods in personalized query expansion. This model has been built to incorporate both latent semantics and weighting terms. Experiments conducted in the real world to evaluate the proposed personalized web search approach; show promising results in the quality of reformulation and re-ranking processes compared to Google engine techniques.
Chiu, Ti-Kai, and 邱迪凱. "Personalized Recommendation with Query Expansion for SCORM Compliant Learning Objects." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/62982658150620265402.
Повний текст джерела國立成功大學
工程科學系碩博士班
94
With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. A Digital course may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen in the near future a vast amount of SCORM-compliant learning objects will be published and distributed cross many learning object repositories. Learners will more easily find learning objects they need in learning object repositories. Currently, most of the learning objects retrieval systems just provide keyword-based search results with no personalized ranking. In the future facing the huge volume of learning objects, learners could probably be lost in searching, selecting suitable and favorite learning objects. This thesis proposes an ontology-based query expansion mechanism to expand, according to his inferred intension, a learner’s query before submitting it for fetching learning objects and a personalized ranking mechanism to sort SCORM-compliant learning objects retrieved from a repository. The personalized ranking mechanism uses both a preference-based and a neighbor-interest-based approach in ranking the degree of relevance of learning objects retrieved to a user’s intension. If embedded with the mechanisms, a tutoring system will be able to provide easily and efficiently for active learners more suitable learning objects.
Jhao, Jin-Hong, and 趙金宏. "Activity Modeling and Interest Learning for Personalized Query Refinement and Result Reranking." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/j24ya9.
Повний текст джерела國立東華大學
資訊工程學系
95
The researches of client end personalized web search in the past, they always utilize the documents on user’s computer or the interactions between user and browser to extract user intentions, and using these intentions for personalized web search. In our research, we introduce the concept of activity modeling into personalized web search. First, we collect the actions that user acts on the application programs, and then we model the actions in user activity, so that we can use the activity to present user intention. When our system modeled user activity, the system also using it to learn user short-term interest, and store it in the database. As user short-term in-terest has accorded with the condition that we define, we convert user short-term in-terest into user long-term interest, and store it in the database equally. By this way, our system not only can avoid the “Cold Start” problem when it starts, but also join the potential interest of user into the search result. Further, according to the modeled activities and the learned interests, our system constructs a “Personal Profile” when the user issues a query. By using this profile, the system can refines the user query for making the search engine know the user's inten-tion even more before it sends the query to search engine. After the search engine passes the search result back, it reranks the original result by the personal profile. It enables the user to spend less time, and get more accurate information. Finally, the experiment of this research proves our system can improve the search result of the present hot search engine effectively and substantially ahead of the latest relative research for personalized web search.
Частини книг з теми "PERSONALIZED QUERY"
Yu, Yijun, Huaizhong Lin, Yimin Yu, and Chun Chen. "Personalized Web Recommendation Based on Path Clustering." In Flexible Query Answering Systems, 368–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_31.
Повний текст джерелаKang, Sanggil, Wonik Park, and Young-Kuk Kim. "Dynamically Personalized Web Service System to Mobile Devices." In Flexible Query Answering Systems, 416–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_35.
Повний текст джерелаSchubert, Monika, Alexander Felfernig, and Florian Reinfrank. "ReAction: Personalized Minimal Repair Adaptations for Customer Requests." In Flexible Query Answering Systems, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24764-4_2.
Повний текст джерелаDolog, Peter, Heiner Stuckenschmidt, and Holger Wache. "Robust Query Processing for Personalized Information Access on the Semantic Web." In Flexible Query Answering Systems, 343–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_29.
Повний текст джерелаJung, Kyung-Yong, Dong-Hyun Park, and Jung-Hyun Lee. "Personalized Movie Recommender System through Hybrid 2-Way Filtering with Extracted Information." In Flexible Query Answering Systems, 473–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25957-2_37.
Повний текст джерелаBouadjenek, Mohamed Reda, Hakim Hacid, and Mokrane Bouzeghoub. "Personalized Social Query Expansion Using Social Annotations." In Lecture Notes in Computer Science, 1–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58664-8_1.
Повний текст джерелаMulhem, Philippe, Nawal Ould Amer, and Mathias Géry. "Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System." In Lecture Notes in Computer Science, 235–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44406-2_17.
Повний текст джерелаBiancalana, Claudio, Antonello Lapolla, and Alessandro Micarelli. "Personalized Web Search Using Correlation Matrix for Query Expansion." In Lecture Notes in Business Information Processing, 186–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01344-7_14.
Повний текст джерелаVerberne, Suzan, Maya Sappelli, Kalervo Järvelin, and Wessel Kraaij. "User Simulations for Interactive Search: Evaluating Personalized Query Suggestion." In Lecture Notes in Computer Science, 678–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_75.
Повний текст джерелаLee, Jung-Hun, and Suh-Hyun Cheon. "Extracting and Ranking Relevant Terms of Personalized Search Query." In Convergence and Hybrid Information Technology, 724–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24082-9_88.
Повний текст джерелаТези доповідей конференцій з теми "PERSONALIZED QUERY"
Zhong, Jianling, Weiwei Guo, Huiji Gao, and Bo Long. "Personalized Query Suggestions." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401331.
Повний текст джерелаBertier, Marin, Rachid Guerraoui, Vincent Leroy, and Anne-Marie Kermarrec. "Toward personalized query expansion." In the Second ACM EuroSys Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1578002.1578004.
Повний текст джерелаChen, Wanyu, Fei Cai, Honghui Chen, and Maarten de Rijke. "Personalized Query Suggestion Diversification." In SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3077136.3080652.
Повний текст джерелаCalegari, Silvia, and Gabriella Pasi. "Personalized Ontology-Based Query Expansion." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.242.
Повний текст джерелаJiang, Danyang, Fei Cai, and Honghui Chen. "Location-Sensitive Personalized Query Auto-Completion." In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2018. http://dx.doi.org/10.1109/ihmsc.2018.00012.
Повний текст джерелаJiang, Di, Kenneth Wai-Ting Leung, Jan Vosecky, and Wilfred Ng. "Personalized Query Suggestion With Diversity Awareness." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816668.
Повний текст джерелаCai, Fei, Shangsong Liang, and Maarten de Rijke. "Time-sensitive Personalized Query Auto-Completion." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2661829.2661921.
Повний текст джерелаLu, Zhixing, Zongmin Cui, Lihua Wang, Xiao Yang, and Xiaolei Lv. "A Flexible Personalized Topic Query Scheme." In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). IEEE, 2020. http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00152.
Повний текст джерелаMeng, Xiangfu, Jian Zhou, and Xiaopeng Zhang. "Personalized categorization of XML query results." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6378045.
Повний текст джерелаChirita, Paul Alexandru, Claudiu S. Firan, and Wolfgang Nejdl. "Personalized query expansion for the web." In the 30th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277746.
Повний текст джерела