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Artykuły w czasopismach na temat „Personalized Web search”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Choi, Dae Young. "Towards Location-Based Personalized Voice Web Search on SmartphonesTowards Location-Based Personalized Voice Web Search on Smartphones." Journal of Computers 11, no. 1 (2016): 62–71. http://dx.doi.org/10.17706/jcp.11.1.62-71.

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Sukanya, L., and R. Vijaya. "A Framework for Privacy-Enhancing Personalized Web Search." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2698–701. http://dx.doi.org/10.31142/ijtsrd12885.

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Roy T P, Roy T. P., and Ginnu George. "A Novel Personalized Web Search with Offline Capability." International Journal of Scientific Research 3, no. 5 (2012): 243–45. http://dx.doi.org/10.15373/22778179/may2014/73.

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Nidhi, Saxena, and Gupta Bineet. "Personalized Web Search Architecture Supporting Privacy." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3351–57. https://doi.org/10.35940/ijeat.C5953.029320.

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Web is gigantic wellspring of information and data and each one relies upon it for accomplishing all kind of information. With the presence of enormous information and assortment of data it gets hard for end client to separate required data. Thus there comes the need to customize every client search so as to acquire important outcomes at the time of searching. This paper gives architecture of internet search utilizing an improved customized method which recognizes every client search keeping protection as a measure concern. The proposed plan is more valuable where more than one client get to s

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Gupta, Disha, and Nekita Chavhan. "Personalized Mobile Web Search Techniques." International Journal of Scientific and Engineering Research 4, no. 11 (2014): 1193–98. http://dx.doi.org/10.14299/ijser.2013.11.001.

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FAUZIA, PARVEEN M.F.RAHMAN, and CHOLE VIKRANT. "VOICE ENABLE PERSONALIZED WEB SEARCH." JournalNX - a Multidisciplinary Peer Reviewed Journal 3, no. 6 (2017): 38–41. https://doi.org/10.5281/zenodo.1421019.

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The technology of voice browsing is rapidly evolving these days. It is because the use of cell phones is increasing at a very high rate, as compared to connected PCs. Speech interface integrated browser is a web browser that helps users by using an interactive voice user interface ,useful to those who have difficulties in seeing and reading a web content. Listening and speaking are the natural modes of communication and information gathering. As a result we are now heading towards a more voice based approach of browsing rather than operating on textual mode. A voice browser or speech browser w

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M Durugkar, Sneha, and Saudagar S Barde. "Privacy Protection in Personalized Web Search Using Metric Prediction." International Journal of Scientific Engineering and Research 3, no. 9 (2015): 65–68. https://doi.org/10.70729/ijser15467.

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Tamboli, Najneen, and Sathish Kumar Penchala. "User Profile Based Personalized Web Search." International Journal of Managing Public Sector Information and Communication Technologies 7, no. 3 (2016): 15–22. http://dx.doi.org/10.5121/ijmpict.2016.7302.

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9

Kim, Je-Min, and Young-Tack Park. "Personalized Search Service in Semantic Web." KIPS Transactions:PartB 13B, no. 5 (2006): 533–40. http://dx.doi.org/10.3745/kipstb.2006.13b.5.533.

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Volkovich, Yana, and Nelly Litvak. "Asymptotic analysis for personalized Web search." Advances in Applied Probability 42, no. 2 (2010): 577–604. http://dx.doi.org/10.1239/aap/1275055243.

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PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equationwhere theRis are distributed asR. This equation is inspired by the original definition of the PageRank. In particular,Nmodels the number of incoming links to a page, andBstays for the user preference. Assuming thatNorBare heavy tailed, we employ the theory of regular variati

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11

Monika, R., V. Pavithra, D. Priya Dharshini, N. Vaishnavi, and S. Gowthami. "Privacy Based Personalized Web Search Engine." International Journal of Computer Trends and Technology 34, no. 3 (2016): 122–24. http://dx.doi.org/10.14445/22312803/ijctt-v34p121.

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Volkovich, Yana, and Nelly Litvak. "Asymptotic analysis for personalized Web search." Advances in Applied Probability 42, no. 02 (2010): 577–604. http://dx.doi.org/10.1017/s0001867800004201.

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PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equationwhere theRis are distributed asR. This equation is inspired by the original definition of the PageRank. In particular,Nmodels the number of incoming links to a page, andBstays for the user preference. Assuming thatNorBare heavy tailed, we employ the theory of regular variati

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13

Zhu, Zhengyu, Jingqiu Xu, Yunyan Tian, and Xiang Ren. "A novel personalized Web search model." Wuhan University Journal of Natural Sciences 12, no. 5 (2007): 897–901. http://dx.doi.org/10.1007/s11859-007-0009-9.

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14

Saxena, Nidhi, Shalini Agarwal, and Vinodini Katiyar. "Personalized Web Search using User Identity." International Journal of Computer Applications 147, no. 12 (2016): 14–17. http://dx.doi.org/10.5120/ijca2016911267.

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15

L., Sukanya, and Vijaya R. "A Framework for Privacy Enhancing Personalized Web Search." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2698–701. https://doi.org/10.31142/ijtsrd12885.

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The extending numerous of websites the all web customers are extended with the immense measure of data available in the web which is given by the Web Search Engine WSE . The point of the WSE is to give the significant item to the client with the conduct of the client click were they performed. WSE give the pertinent outcome in the interest of the client visit click based strategy. From this technique no affirmation to the client protection and furthermore no securities were giving to their information. Consequently, clients were perplexed for their private data amid seek has turned into a note

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16

Fang Liu, C. Yu, and Weiyi Meng. "Personalized web search for improving retrieval effectiveness." IEEE Transactions on Knowledge and Data Engineering 16, no. 1 (2004): 28–40. http://dx.doi.org/10.1109/tkde.2004.1264820.

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17

Zhicheng Dou, Ruihua Song, Ji-Rong Wen, and Xiaojie Yuan. "Evaluating the Effectiveness of Personalized Web Search." IEEE Transactions on Knowledge and Data Engineering 21, no. 8 (2009): 1178–90. http://dx.doi.org/10.1109/tkde.2008.172.

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18

Xu, Zheng, Hai-Yan Chen, and Jie Yu. "Generating Personalized Web Search Using Semantic Context." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/462782.

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The “one size fits the all” criticism of search engines is that when queries are submitted, the same results are returned to different users. In order to solve this problem, personalized search is proposed, since it can provide different search results based upon the preferences of users. However, existing methods concentrate more on the long-term and independent user profile, and thus reduce the effectiveness of personalized search. In this paper, the method captures the user context to provide accurate preferences of users for effectively personalized search. First, the short-term query cont

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19

Lidan Shou, He Bai, Ke Chen, and Gang Chen. "Supporting Privacy Protection in Personalized Web Search." IEEE Transactions on Knowledge and Data Engineering 26, no. 2 (2014): 453–67. http://dx.doi.org/10.1109/tkde.2012.201.

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20

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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21

Jayanthi, J., and M. Ezhilmathi. "Evaluating the Effectiveness of Web Search Metrics." Asian Journal of Computer Science and Technology 1, no. 2 (2012): 16–19. http://dx.doi.org/10.51983/ajcst-2012.1.2.1707.

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Software metrics are the key performance indicators, using which the performance of a system can be assessed quantitatively. Metrics can also be applied for personalized web search which can be used to retrieve relevant results for each individual user depending on their unique profile. Although personalized search based on user profile has been under research for many years and various metrics have been proposed, it is still uncertain whether personalization is unswervingly effective on different queries for different user profiles. We present a framework for personalized search which retriev

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22

Chebil, Wiem, Mohammad O. Wedyan, Haiyan Lu, and Omar Ghaleb Elshaweesh. "Context-Aware Personalized Web Search Using Navigation History." International Journal on Semantic Web and Information Systems 16, no. 2 (2020): 91–107. http://dx.doi.org/10.4018/ijswis.2020040105.

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It is highly desirable that web search engines know users well and provide just what the user needs. Although great effort has been devoted to achieve this dream, the commonly used web search engines still provide a “one-fit-all” results. One of the barriers is lack of an accurate representation of user search context that supports personalised web search. This article presents a method to represent user search context and incorporate this representation to produce personalised web search results based on Google search results. The key contributions are twofold: a method to build contextual us

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23

There, Mangala P., and Nitin Janwe. "Review on Improved Method for Supporting Privacy Protection in Personalized Web Search." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 558–62. http://dx.doi.org/10.31142/ijtsrd15751.

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Panchajanyeswari, M. Achar. "Creating an Advanced Web Based Environment using Semantic Web." International Journal of Management, Technology, and Social Sciences (IJMTS) 2, no. 1 (2017): 38–44. https://doi.org/10.5281/zenodo.821123.

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E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning

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25

A., Priyanka, and Satpalsing D. "Enhancing Privacy and Security in Personalized Web Search." International Journal of Computer Applications 150, no. 9 (2016): 1–6. http://dx.doi.org/10.5120/ijca2016911617.

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Sajjan, Rajani S., and Suvarna A. Veer. "Providing Privacy in Profile Based Personalized Web Search." International Journal of Computer Sciences and Engineering 7, no. 6 (2019): 830–36. http://dx.doi.org/10.26438/ijcse/v7i6.830836.

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Yoon, Sung Hee. "Personalized Web Search using Query based User Profile." Journal of the Korea Academia-Industrial cooperation Society 17, no. 2 (2016): 690–96. http://dx.doi.org/10.5762/kais.2016.17.2.690.

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S.K., Jayanthi, and Prema S. "IMPROVING PERSONALIZED WEB SEARCH USING BOOKSHELF DATA STRUCTURE." ICTACT Journal on Soft Computing 03, no. 01 (2012): 434–39. http://dx.doi.org/10.21917/ijsc.2012.0067.

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Kumar, Krishan. "Privacy Protection in Personalized Web Search using Obfuscation." International Journal of Emerging Trends in Engineering Research 8, no. 4 (2020): 1410–16. http://dx.doi.org/10.30534/ijeter/2020/76842020.

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Malthankar, Sharvari V., and Shilpa Kolte. "Client Side Privacy Protection Using Personalized Web Search." Procedia Computer Science 79 (2016): 1029–35. http://dx.doi.org/10.1016/j.procs.2016.03.130.

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31

Ghaly, Mahmoud Abou. "Ranking of Web Pages in a Personalized Search." Journal of Computer and Communications 11, no. 02 (2023): 89–101. http://dx.doi.org/10.4236/jcc.2023.112007.

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Chawla, Suruchi. "Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions." International Journal of Applied Evolutionary Computation 7, no. 1 (2016): 33–49. http://dx.doi.org/10.4018/ijaec.2016010103.

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In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classific

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33

Nitya, sri Nellore. "Integration with Modern AI Techniques for Personalized Web Search." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 1 (2019): 1–6. https://doi.org/10.5281/zenodo.14615481.

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Personalized web search (PWS) aims to improve user satisfaction by tailoring search results to individual preferences. Despite significant advancements, challenges such as user intent ambiguity, data sparsity, and privacy concerns persist. The integration of modern AI techniques, including deep learning, reinforcement learning, and natural language processing (NLP), offers promising solutions. This paper explores the application of these AI techniques in enhancing PWS, with a focus on user profiling, query disambiguation, and privacy-preserving mechanisms. Experimental results demonstrate that

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34

Thomas, Blessy, and Jilson P. Jose. "A Survey on Web Search Results Personalization." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 03 (2015): 1582–84. https://doi.org/10.5281/zenodo.14770802.

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Web is a huge information repository covering almost every topic, in which a human user could be interested. As the size and richness of information on the web increases, diversity and complexity of the tasks users tries to perform also increases. With the overwhelming volume of information on the web, the task of finding relevant information related to a specific query or topic is becoming increasingly difficult. Now a day’s commonly used task on internet is web search. User gets variety of related information for their queries. To provide more relevant and effective results to user, Pe

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35

Umamaheswari, B., and Pramod Patil. "Personalized Web Search and User Profile Mining using Ontology." International Journal of Computer Applications 86, no. 3 (2014): 26–29. http://dx.doi.org/10.5120/14967-3145.

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Jayanthi, J., and Dr K. S. Jayakumar. "An Integrated Page Ranking Algorithm for Personalized Web Search." International Journal of Computer Applications 12, no. 11 (2011): 1–5. http://dx.doi.org/10.5120/1732-2350.

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Babu, K. R. Remesh, and Philip Samuel. "Concept Networks for Personalized Web Search Using Genetic Algorithm." Procedia Computer Science 46 (2015): 566–73. http://dx.doi.org/10.1016/j.procs.2015.02.092.

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Bouadjenek, Mohamed Reda, Hakim Hacid, Mokrane Bouzeghoub, and Athena Vakali. "PerSaDoR: Personalized social document representation for improving web search." Information Sciences 369 (November 2016): 614–33. http://dx.doi.org/10.1016/j.ins.2016.07.046.

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Yang, Yanwu. "Personalized Search Strategies for Spatial Information on the Web." IEEE Intelligent Systems 27, no. 1 (2012): 12–20. http://dx.doi.org/10.1109/mis.2010.108.

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C.N., Pushpa, Vinay Kumar, Thriveni J., and Venugopal K.R. "PWIS: Personalized Web Image Search using One-Click Method." International Journal of Computer Applications 130, no. 10 (2015): 39–47. http://dx.doi.org/10.5120/ijca2015907101.

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Deshingkar,, Shravani Shankar. "Personalized News Aggregator System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04687.

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This study describes the creation of a responsive real-time news aggregator web application based on ReactJS. The web application retrieves recent headlines and categorized news from third-party APIs (e.g., NewsAPI.org), providing a dynamic and interactive user experience. React's component-based structure and DOM provide high performance of maintainability. Some of important features are category-wise filtering, virtual and ease the search functionality, dark mode support, and responsive design through CSS Flexbox and Media Queries. The study contrasts the performance, scalability, and usabil

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Sekhar Babu, B., P. Lakshmi Prasanna, and P. Vidyullatha. "Personalized web search on e-commerce using ontology based association mining." International Journal of Engineering & Technology 7, no. 1.1 (2017): 286. http://dx.doi.org/10.14419/ijet.v7i1.1.9487.

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In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web

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43

Han, Cui Feng. "The Module Design of Personalized Information Service System in College Library Based on Agent." Advanced Materials Research 268-270 (July 2011): 1812–16. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1812.

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The research on personalized information service system in college library based on intelligent agent are put forward for improving personalize service quality and level of college library information management system. The related techniques and methods that can be used for the application and research on personalized information service system are discussed deeply in this thesis. Based on analyzing the nowadays situation of study about personalized service for library information management system, and researching the related techniques of intelligent agent, search engine and web information

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44

Chawla, Suruchi. "Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search." Journal of Information Technology Research 11, no. 2 (2018): 110–27. http://dx.doi.org/10.4018/jitr.2018040107.

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The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant d

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Song, Chang-Woo, Jong-Hun Kim, Kyung-Yong Chung, Joong-Kyung Ryu, and Jung-Hyun Lee. "Contents Recommendation Search System using Personalized Profile on Semantic Web." Journal of the Korea Contents Association 8, no. 1 (2008): 318–27. http://dx.doi.org/10.5392/jkca.2008.8.1.318.

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Singh, Aarti, and Basim Alhadidi. "Knowledge Oriented Personalized Search Engine: A Step towards Wisdom Web." International Journal of Computer Applications 76, no. 8 (2013): 1–9. http://dx.doi.org/10.5120/13264-0744.

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J., Jayanthi, Ezhilmathi M., and Rathi S. "NOVEL RELEVANCE METRIC PREDICTION ALGORITHM FOR A PERSONALIZED WEB SEARCH." ICTACT Journal on Soft Computing 03, no. 04 (2013): 596–604. http://dx.doi.org/10.21917/ijsc.2013.0086.

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Chawla, Suruchi. "Trust in Personalized Web Search based on Clustered Query Sessions." International Journal of Computer Applications 59, no. 7 (2012): 36–44. http://dx.doi.org/10.5120/9563-4032.

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Mohammed. "A Novel Page Ranking Algorithm for a Personalized Web Search." Journal of Computer Science 8, no. 7 (2012): 1029–35. http://dx.doi.org/10.3844/jcssp.2012.1029.1035.

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Erola, Arnau, Jordi Castellà-Roca, Alexandre Viejo, and Josep M. Mateo-Sanz. "Exploiting social networks to provide privacy in personalized web search." Journal of Systems and Software 84, no. 10 (2011): 1734–45. http://dx.doi.org/10.1016/j.jss.2011.05.009.

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