Relevant bibliographies by topics / PERSONALIZED QUERY

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

Academic literature on the topic 'PERSONALIZED QUERY'

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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 (December 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 group. The proposed DCSGQ is general due to flexible in configuration of neighboring social density in queries. We prove the NP-hardness and inapproximability of DCSGQ, formulate an Integer Program (IP) as a baseline, and propose an efficient algorithm, FSGSel-RR, by relaxing the IP. We then propose a fixed-parameter tractable algorithm with a performance guarantee, named FSGSel-TD, and further combine it with FSGSel-RR into a hybrid approach, named FSGSel-Hybrid, in order to strike a good balance between solution quality and efficiency. Extensive experiments on multiple large real datasets demonstrate the superior solution quality and efficiency of our approaches over existing subgraph and group queries.
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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 (April 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 semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.
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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 (July 31, 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 ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.
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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 (September 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 (July 1, 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 (July 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. Set up a personalized Chinese search engineer model.
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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 (January 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. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.
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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 (August 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 (November 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 particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.
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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 appropriées à son contexte. Nous proposons, en particulier, toute une démarche originale permettant de bien interpréter et réécrire la requête initiale en fonction des activités réalisées dans la tâche courante de l'utilisateur.Nous considérons qu'une tâche est jalonnée par des activités, nous proposons alors d'interpréter le besoin de l'utilisateur, représenté initialement par la requête, selon ses activités actuelles dans la tâche (et son profil) et de suggérer des reformulations de requêtes appropriées à ces activités.Une implémentation de cette approche est faite, et elle est suivie d’une étude expérimentale. Nous proposons également une procédure d'évaluation qui tient compte l'évaluation des termes d'expansion, et l'évaluation des résultats retournés en utilisant les requêtes reformulées, appelés SRQ State Reformulated Query. Donc, trois facteurs d’évaluation sont proposés sur lesquels nous nous appuierons pour l'analyse et l'évaluation des résultats. L’objective est de quantifier l'amélioration apportée par notre système dans certains contextes par rapport aux autres systèmes. Nous prouvons que notre approche qui prend en compte la tâche actuelle de l'utilisateur est effectivement plus performante que les approches basées, soit uniquement sur la requête initiale, ou encore celle basée sur la requête reformulée en considérant uniquement le profil de l'utilisateur
Access 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
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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 queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web.
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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 often addressed through query expansion. Moreover, user questions are often short. The results of a search can be improved when the length of the question is long. Various query expansion methods that add useful question-related terms before processing the question have been proposed and proven to increase the performance of the result. Some of these query expansion methods add contextual information related to the user and the question. On the other hand, human communications are quite successful and seem to be very easy. This is mainly due to the understanding of language and the world knowledge that humans have. Human communication is more successful when there is an implicit understanding of everyday situations of others who take part in the communication. Here the implicit situational information, or the “context” that humans share, enables them to have a more meaningful interaction amongst themselves. Similar to human–human communications, improving computers’ access to context can increase the richness of human–computer communications, giving more useful computational services to users. Based on the above factors, this research proposes a method to make use of context in order to understand and process user requests. Here, the term “context” means the meanings associated with key query terms and preferences that have to be decided in order to process the query. As in a natural environment, results produced to different users for the same question could vary in an automated system. If the automated system knows users’ preferences related to the question, then it could make use of these preferences to process user queries, producing more relevant and useful results to the user. Hence, a new approach for a personalised query expansion is proposed in this research, where user queries are expanded with user preferences and hence the expanded queries that will be used for processing vary for different users. An architecture that is required for such a Web application to carryout a personalised query expansion with contextual information is also proposed in the thesis. The preferences that could be used for the query expansion are therefore user-specific. Users have different set of preferences depending on the tasks they want to perform. Similar tasks that have same types of preferences can be grouped into task based domains. Hence, user preferences will be the same in a domain, and will vary across domains. Furthermore, there can be different types of subtasks that could be performed within a domain. The set of preferences that could be used for each sub task could vary, and it will be a sub set of the set of preferences of the domain. Hence, an approach for a personalised query expansion which adds user, domain and task-specific preferences to user queries is proposed in this research. The main stages of this expansion are identified and discussed in this thesis. Each of these stages requires different contextual information which is represented in the context model. Out of the main stages identified in the query expansion process, the first three stages, the domain identification, task identification, and missing parameter identification, are explored in the thesis. As the preferences used for the expansion depend on the query domain, it is necessary to identify the domain of the query at first instance. Hence, a domain identification algorithm which makes use of eight different features is proposed in the thesis to identify domains of given queries. This domain identification also reduces the ambiguity of query terms. When the query domain is identified, context/associating meanings of query terms are known. This limits the scope of the possible misinterpretations of query terms. A domain ontology, domain dictionary, and user profile are used by the domain identification algorithm. The domain ontology consists of objects and their categories, attributes of objects and their categories, relationships among objects, and instances and their categories in the domain. The domain dictionary consists of objects and attributes. This is created automatically from the domain ontology. The user profile has the long term preferences of the user that are domain-specific and general. When the domain of the query is known, in order to decide the preferences of the user, the task specified in the query has to be identified. This task identification process is found to be similar in domains with similar activities. Hence, domains are grouped at this stage. These domain groups and the rules that could be used to find out the tasks in the domain groups are identified and discussed in the thesis. For each sub tasks in the domain groups, the types of preferences that could be used to expand user queries are identified and are used to expand user queries. An experiment is designed to evaluate the performance of the proposed approach. The first three stages of the query expansion, the domain identification, task identification, and missing parameter identification, are implemented and evaluated. Samples of five domains are implemented, and queries are collected in these domains from various users. In order to create new domains, a wizard is provided by the system. This system also allows editing the existing domains, domain groups, and types of preferences in sub tasks of the domain groups. Instances of the attributes are manually identified and added to the system using the interface provided by the system. In each of the stages of the query expansion, the results of the queries are manually identified, and are compared with the results produced by the system. The results have confirmed that the proposed method has a positive impact in query expansion. The experiments, results and evaluation of the proposed query expansion approach are also presented in the thesis. The proposed approach for the query expansion could be used by search engines, organisations with a limited set of task domains, and any application that can be improved by making use of personalised query expansion.
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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.
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 proposed work of thesis, we will talk about a hybrid technique by combining both the context sensitive, trending and personalized suggestions. The improvements which are made in the base paper are that a new approach can be proposed by combining the three techniques to create a hybrid technique. It intends to incorporate three major research works: Time sensitive (based on time series and trends), Context Sensitive (based on recent searches done) and Personalized (based on gender, location and age-group) query auto completion. Thus an algorithm that considers all these parameters will be better at predicting the user query. The results predicted are better in reducing the user keystrokes during the search and also reduces the searching time, and also enhances the reliability of the search engine. Further improvements can be done by extracting the user’s browsing history to determine keywords, interests and other user-specific data for enhancing the result predictions.
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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.
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 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.
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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
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.
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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
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.
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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, 368–77. Berlin, Heidelberg: 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, 416–26. Berlin, Heidelberg: 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, 13–24. Berlin, Heidelberg: 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, 343–55. Berlin, Heidelberg: 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, 473–86. Berlin, Heidelberg: 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, 1–25. Berlin, Heidelberg: 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, 235–43. Cham: 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, 186–98. Berlin, Heidelberg: 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, 678–90. Cham: 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, 724–31. Berlin, Heidelberg: 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. New York, NY, USA: 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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277746.

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You might also be interested in the extended bibliographies on the topic 'PERSONALIZED QUERY' for particular source types:
Journal articles
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