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Relevant bibliographies by topics / PERSONALIZED QUERY

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Academic literature on the topic 'PERSONALIZED QUERY'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "PERSONALIZED QUERY"

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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 (2022): 615–28. http://dx.doi.org/10.14778/3574245.3574249.

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Research on new queries for finding dense subgraphs and groups has been actively pursued due to their many applications, especially in social network analysis and graph mining. However, existing work faces two major weaknesses: i) incapability of supporting personalized neighborhood density, and ii) inability to find sparse groups. To tackle the above issues, we propose a new query, called Density-Customized Social Group Query (DCSGQ), that accommodates the need for personalized density by allowing individual users to flexibly configure their social tightness (and sparseness) for the target gr

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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantica

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Yao, 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 (2022): 1–29. http://dx.doi.org/10.1145/3470564.

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Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many

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Jiang, Dan-yang, and Hong-hui Chen. "Cohort-based personalized query auto-completion." Frontiers of Information Technology & Electronic Engineering 20, no. 9 (2019): 1246–58. http://dx.doi.org/10.1631/fitee.1800010.

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LIANG, Yaopei, and Dingming WU. "Location-aware personalized keyword query recommendation." Journal of Shenzhen University Science and Engineering 36, no. 04 (2019): 467–72. http://dx.doi.org/10.3724/sp.j.1249.2019.04467.

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Khemmarat, Samamon, and Lixin Gao. "Predictive and Personalized Drug Query System." IEEE Journal of Biomedical and Health Informatics 21, no. 4 (2017): 1146–55. http://dx.doi.org/10.1109/jbhi.2016.2562183.

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Han, 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.

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To improve the accuracy of query result of search engineer and satisfy personalized requirements of users, we proposed the method of building and updating user personalized model. This method based on certain information which mine from users’ behaviors and customs in using search engineer. Through mining information from users’ query customs, visit frequency and browse Web in using Chinese search engineer, we pick up characters of use and interest of users, and then build personalized interest model of users. This paper studies technique details of building and updating personalized model. Se

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Zhu, 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 (2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.

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With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordi

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Xie, Jin, Fuxi Zhu, Huanmei Guan, Jiangqing Wang, Hao Feng, and Lin Zheng. "Personalized query recommendation using semantic factor model." China Communications 18, no. 8 (2021): 169–82. http://dx.doi.org/10.23919/jcc.2021.08.012.

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Chunguang Ma, Lei Zhang, Songtao Yang, Xiaodong Zheng, and Pinhui Ke. "Achieve personalized anonymity through query blocks exchanging." China Communications 13, no. 11 (2016): 106–18. http://dx.doi.org/10.1109/cc.2016.7781722.

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Dissertations / Theses on the topic "PERSONALIZED QUERY"

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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/.

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The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a part

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Fischer, 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.

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Asfari, Ounas. "Personalized Access to Contextual Information by using an Assistant for Query Reformulation." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112126.

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Les travaux présentés dans cette thèse rentrent dans le cadre de la Recherche d'Information (RI) et s'intéressent à une des questions de recherche actuellement en vogue dans ce domaine: la prise en compte du contexte de l'utilisateur pendant sa quête de l'information pertinente. Nous proposons une approche originale de reformulation automatique de requêtes basée sur le profil utilisateur et sa tâche actuelle. Plus précisément, notre approche tient compte deux éléments du contexte, les centres d'intérêts de l'utilisateur (son profil) et la tâche qu'il réalise, pour suggérer des requêtes appropr

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Limbu, Dilip Kumar. "Contextual information retrieval from the WWW." Click here to access this resource online, 2008. http://hdl.handle.net/10292/450.

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Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify

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Seher, Indra. "A personalised query expansion approach using context." Thesis, View thesis, 2007. http://handle.uws.edu.au:8081/1959.7/33427.

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Users of the Web usually use search engines to find answers to a variety of questions. Although search engines can rapidly process a large number of Web documents, in many cases, the answers returned by search engines are not relevant to the user’s information need, although they do contain the same keywords as the query. This is because the Web contains information sources created by numerous authors independently, and the authors’ vocabularies vary greatly. Furthermore, most words in natural languages have inherent ambiguity. This vocabulary mismatch between user queries and Web sources is o

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Seher, Indra. "A personalised query expansion approach using context." View thesis, 2007. http://handle.uws.edu.au:8081/1959.7/33427.

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Thesis (Ph.D.)--University of Western Sydney, 2007.<br>A 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.

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KATIYAR, ANTRA. "A CONTEXT SENSITIVE AND PERSONALIZED QUERY AUTOCOMPLETION TECHNIQUE." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15941.

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Query Autocompletion is a leading attribute of Search Engines which makes the user’s search experience better by predicting the query. QAC methods suggest query suggestions to users, after they enter some of the keystrokes in the search engine. This is done by predicting the query using past query logs and other trends. Current QAC methods use the Most Popular Completions as the suggestion results. Context and Personalized techniques are proposed already but they are used separately. The present methods being incorporated are the location and past searches sensitive QAC. In this propose

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Elshaweesh, Omar Ghaleb Mohammad. "Intelligent personalized approaches for semantic search and query expansion." Thesis, 2019. http://hdl.handle.net/10453/133361.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>In 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 par

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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.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>94<br>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,

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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.

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碩士<br>國立東華大學<br>資訊工程學系<br>95<br>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

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Book chapters on the topic "PERSONALIZED QUERY"

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Yu, Yijun, Huaizhong Lin, Yimin Yu, and Chun Chen. "Personalized Web Recommendation Based on Path Clustering." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_31.

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Kang, Sanggil, Wonik Park, and Young-Kuk Kim. "Dynamically Personalized Web Service System to Mobile Devices." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_35.

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Schubert, Monika, Alexander Felfernig, and Florian Reinfrank. "ReAction: Personalized Minimal Repair Adaptations for Customer Requests." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24764-4_2.

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Dolog, Peter, Heiner Stuckenschmidt, and Holger Wache. "Robust Query Processing for Personalized Information Access on the Semantic Web." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_29.

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Jung, 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. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25957-2_37.

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Bouadjenek, Mohamed Reda, Hakim Hacid, and Mokrane Bouzeghoub. "Personalized Social Query Expansion Using Social Annotations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58664-8_1.

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Mulhem, Philippe, Nawal Ould Amer, and Mathias Géry. "Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44406-2_17.

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Biancalana, Claudio, Antonello Lapolla, and Alessandro Micarelli. "Personalized Web Search Using Correlation Matrix for Query Expansion." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01344-7_14.

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Verberne, Suzan, Maya Sappelli, Kalervo Järvelin, and Wessel Kraaij. "User Simulations for Interactive Search: Evaluating Personalized Query Suggestion." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_75.

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Lee, Jung-Hun, and Suh-Hyun Cheon. "Extracting and Ranking Relevant Terms of Personalized Search Query." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24082-9_88.

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Conference papers on the topic "PERSONALIZED QUERY"

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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. ACM, 2020. http://dx.doi.org/10.1145/3397271.3401331.

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Bertier, Marin, Rachid Guerraoui, Vincent Leroy, and Anne-Marie Kermarrec. "Toward personalized query expansion." In the Second ACM EuroSys Workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1578002.1578004.

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Chen, 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. ACM, 2017. http://dx.doi.org/10.1145/3077136.3080652.

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Calegari, 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.

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Jiang, 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.

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Jiang, 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.

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Cai, Fei, Shangsong Liang, and Maarten de Rijke. "Time-sensitive Personalized Query Auto-Completion." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. ACM, 2014. http://dx.doi.org/10.1145/2661829.2661921.

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Lu, 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.

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Meng, 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.

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Chirita, Paul Alexandru, Claudiu S. Firan, and Wolfgang Nejdl. "Personalized query expansion for the web." In the 30th annual international ACM SIGIR conference. ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277746.

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