Relevant bibliographies by topics / Personalized Information Retrieval

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Personalized Information Retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Personalized Information Retrieval"

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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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Choi, Okkyung, Kangseok Kim, Duksang Wang, Hongjin Yeh, and Manpyo Hong. "Personalized Mobile Information Retrieval System." International Journal of Advanced Robotic Systems 9, no. 1 (2012): 11. http://dx.doi.org/10.5772/50910.

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Building a global Network Relations with the internet has made huge changes in personal information system and even comments left on a webpage of SNS(Social Network Services) are appreciated as important elements that would provide valuable information for someone. Social Network is a relation between individuals or groups, represented in a graph model, which converts the concept of psychological and social relations into a logical structure by using node and link. But, most of the current personalized systems on the basis of Social Network are built and constructed mainly in the PC environmen
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Zhang, Shu Dong, Yan Chen, and Bei Bei Gao. "Personalized Intelligent Information Retrieval Entrance Mechanism." Advanced Materials Research 108-111 (May 2010): 216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.216.

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In order to solve the problems of difficulties and differences in expression existed in traditional information retrieval, this paper presents a personalized intelligent information retrieval entrance mechanism based on domain ontology, which takes full account of various types of users' domain knowledge level and provide relevant retrieval methods for guiding personalized information, makes all kinds of users in a particular query environment can fully and effectively express their queries intentions.
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El-Ansari, Anas, Abderrahim Beni-Hssane, Mostafa Saadi, and Mohamed El Fissaoui. "PAPIR: privacy-aware personalized information retrieval." Journal of Ambient Intelligence and Humanized Computing 12, no. 10 (2021): 9891–907. http://dx.doi.org/10.1007/s12652-020-02736-y.

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Sheng, Zhong Biao, Hua Ping Jia, and Xiao Rong Tong. "Design of Personalized Intelligent Information Retrieval Model Based on Agent." Applied Mechanics and Materials 155-156 (February 2012): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1175.

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The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It i
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Hong, Ying. "Research of Personalized Intelligent Information Retrieval System Based on Agent." Advanced Materials Research 945-949 (June 2014): 3406–9. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.3406.

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To efficiently retrieve information from the vast source of the internet, search engines are required. There are some search engines that can help people to search for needed information, but they are difficult to ensure precision rate and personalization of information. To solve these problems, this paper proposed a personalized information retrieval system based on meta-search engine. This paper used multi-agent technology to construct the personalized information retrieval system, adopted user knowledge database to create and update user model and improved vector space model algorithm combi
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Saravanakumar, K., and Mahesh Moturi. "Semantic based Personalized Framework for Information Retrieval." International Journal of Computer Applications 20, no. 4 (2011): 14–17. http://dx.doi.org/10.5120/2423-3254.

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G.Sastry, Hanumat, Venkatadri M, and Lokanatha C. Reddy. "Personalized Information Retrieval Services for Digital Libraries." International Journal of Computer Applications 97, no. 11 (2014): 33–35. http://dx.doi.org/10.5120/17054-7295.

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Wu, Zongda, Chenglang Lu, Youlin Zhao, Jian Xie, Dongdong Zou, and Xinning Su. "The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews." Libri 71, no. 3 (2021): 227–37. http://dx.doi.org/10.1515/libri-2019-0140.

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Abstract This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for
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FU, Chong-guo, and Zhi-zhong TANG. "Peer-to-peer based personalized Web information retrieval." Journal of Computer Applications 30, no. 1 (2010): 114–17. http://dx.doi.org/10.3724/sp.j.1087.2010.00114.

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Dissertations / Theses on the topic "Personalized Information Retrieval"

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Kramer, Joshua David. "Agent based personalized information retrieval." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43539.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (p. 69-74).<br>by Joshua David Kramer.<br>M.Eng.
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YU, HONGMING. "A PERSONALIZED INFORMATION ENVIRONMENT SYSTEM FOR INFORMATION RETRIEVAL." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060875911.

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Kacem, Sahraoui Ameni. "Personalized information retrieval based on time-sensitive user profile." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30111/document.

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Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet ob
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Banwell, Linda M. "PLUS : a system architecture for Personalized Library User Support." Thesis, University of Exeter, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359596.

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Crain, Steven P. "Personalized search and recommendation for health information resources." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45805.

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Consumers face several challenges using the Internet to fill health-related needs. (1) In many cases, they face a language gap as they look for information that is written in unfamiliar technical language. (2) Medical information in social media is of variable quality and may be appealing even when it is dangerous. (3) Discussion groups provide valuable social support for necessary lifestyle changes, but are variable in their levels of activity. (4) Finding less popular groups is tedious. We present solutions to these challenges. We use a novel adaptation of topic models to address the langua
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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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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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Alhamid, Mohammed F. "Towards Context-Aware Personalized Recommendations in an Ambient Intelligence Environment." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32052.

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Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual inform
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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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Ould, Amer Nawal. "Recherche sociale et personnalisée d'Information." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM071.

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Une large gamme de services et de plateformes rendent l’utilisateur de plus en plus interactif avec le web. De nombreuses informations qui concernent à la fois les utilisateurs et les ressources (documents, images, vidéos, commentaires, tweets, tags, etc.) sont constamment générées. Ces informations peuvent être très utiles dans les tâches de recherche d’information, pour la modélisation des utilisateurs et des ressources. Cependant, les modèles classiques de recherche d’information n’intègrent pas le contexte social de l’utilisateur et des ressources. Par conséquent, de nombreuses recherches
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Books on the topic "Personalized Information Retrieval"

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Solla, Alberto Gil. TV-Anytime: Paving the Way for Personalized TV. Springer Berlin Heidelberg, 2013.

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Arcadi, Navarro, and SpringerLink (Online service), eds. Bioinformatics for Personalized Medicine: 10th Spanish Symposium, JBI 2010, Torremolinos, Spain, October 27-29, 2010. Revised Selected Papers. Springer Berlin Heidelberg, 2012.

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Andrés, Gonzáles Rafael, Chen Nong 1976-, and Dahanayake Ajantha 1954-, eds. Personalized information retrieval and access: Concepts, methods and practices. Information Science Reference, 2008.

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Solla, Alberto Gil, and Rafael G. Sotelo Bovino. TV-Anytime: Paving the Way for Personalized TV. Springer, 2015.

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Book chapters on the topic "Personalized Information Retrieval"

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Xu, Wei-ran, Dong-xin Liu, Jun Guo, Yi-chao Cai, and Ri-le Hu. "Supervised Dual-PLSA for Personalized SMS Filtering." In Information Retrieval Technology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_22.

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Ishizuka, Kenkichi. "Personalized Social Search Based on Agglomerative Hierarchical Graph Clustering." In Information Retrieval Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03520-4_4.

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Huang, Yunping, Le Sun, and Zhe Wang. "A Unified Graph-Based Iterative Reinforcement Approach to Personalized Search." In Information Retrieval Technology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_17.

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Schwarzkopf, Eric, and Anthony Jameson. "Personalized Support for Interaction with Scientific Information Portals." In Adaptive Multimedia Retrieval. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25981-7_4.

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Bueno, David, and Amos A. David. "METIORE: A Personalized Information Retrieval System." In User Modeling 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44566-8_17.

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Liu, Ya’ning, Rui Yan, and Hongfei Yan. "Guess What You Will Cite: Personalized Citation Recommendation Based on Users’ Preference." In Information Retrieval Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45068-6_37.

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Candela, Leonardo, and Umberto Straccia. "The Personalized, Collaborative Digital Library Environment CYCLADES and Its Collections Management." In Distributed Multimedia Information Retrieval. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24610-7_12.

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Harbaoui, Azza, Sahbi Sidhom, Malek Ghenima, and Henda Ben Ghezala. "Personalized Information Retrieval: Application to Virtual Communities." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07731-4_43.

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Fernández, Juan Manuel, Marc Solà, Alexander Steblin, Eloisa Vargiu, and Felip Miralles. "The Relevance of Providing Useful and Personalized Information to Therapists and Caregivers in Tele*." In Information Filtering and Retrieval. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46135-9_6.

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Zhang, Yin, Jiangqin Wu, and Yueting Zhuang. "Personalized Multimedia Retrieval in CADAL Digital Library." In Advances in Multimedia Information Processing - PCM 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89796-5_72.

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Conference papers on the topic "Personalized Information Retrieval"

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Meng-Fei, Zhang, and Sui Ai-Na. "Personalized Multimedia-Information Retrieval Model." In 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.121.

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Carman, Mark J., and Fabio Crestani. "Towards personalized distributed information retrieval." In the 31st annual international ACM SIGIR conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390468.

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Dengya Zhu and Heinz Dreher. "Personalized information retrieval in digital ecosystems." In 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies (DEST). IEEE, 2008. http://dx.doi.org/10.1109/dest.2008.4635207.

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Zhou, Ping. "Research on Personalized Blog Information Retrieval." In 2010 International Conference on Web Information Systems and Mining (WISM). IEEE, 2010. http://dx.doi.org/10.1109/wism.2010.61.

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Carman, Mark J., Mark Baillie, and Fabio Crestani. "Tag data and personalized information retrieval." In Proceeding of the 2008 ACM workshop. ACM Press, 2008. http://dx.doi.org/10.1145/1458583.1458591.

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Ghosh, Hiranmay, P. Poornachander, Anupama Mallik, and Santanu Chaudhury. "Learning ontology for personalized video retrieval." In Workshop on multimedia information retrieval. ACM Press, 2007. http://dx.doi.org/10.1145/1290067.1290075.

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Yadav, Nikhil, and Christian Poellabauer. "An architecture for personalized health information retrieval." In the 2012 international workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2389707.2389716.

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Zhuhadar, Leyla, and Olfa Nasraoui. "Semantic Information Retrieval for Personalized E-Learning." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.130.

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Tyler, Sarah K., Jian Wang, and Yi Zhang. "Utilizing re-finding for personalized information retrieval." In the 19th ACM international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1871437.1871649.

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Alloui, Tarek, Imane Boussebough, and Allaoua Chaoui. "An intelligent system for personalized information retrieval." In the International C* Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2494444.2494448.

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Journal articles Dissertations / Theses
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