Relevant bibliographies by topics / HyperlinkInduced Topic Search (HITS)

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

Academic literature on the topic 'HyperlinkInduced Topic Search (HITS)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "HyperlinkInduced Topic Search (HITS)"

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John, Vaseekaran, and Srinivasan N. "A Repartitioning Temporary Customized Model for Interest-Point Recommendations with Hyperlink-Induced Topic Search." Indian Journal of Science and Technology 16, no. 5 (2023): 339–47. https://doi.org/10.17485/IJST/v16i5.1996.

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ABSTRACT <strong>Objectives:</strong>&nbsp;To determine the significance of each object (node) in a graph, researchers often employ link analysis techniques, such as the Hyperlink- Induced Topic Search (HITS) algorithm. This will be performed for several reasons, including analyzing the confidentiality of social networks and optimizing search results based on the hierarchical nature of the Internet&rsquo;s interconnections.&nbsp;<strong>Methods:</strong>&nbsp;This work proposes a new version of HITS called the Boundary grading HITS method (BG-HITS). We offer a technique for calculating edge we
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Ma, Rui Xin, Gui Shi Deng, and Xiao Wang. "Improvement and Optimization of HITS in Web Mining." Advanced Materials Research 187 (February 2011): 728–34. http://dx.doi.org/10.4028/www.scientific.net/amr.187.728.

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This paper combines text and link analysis together, analyzes every single page’s recent reference amplification, proposes a mixed weight HITS algorithm. From the comparison we can see, the results of the improved algorithm effectively restrains the topic deviation phenomenon and efficiently increases the search veracity.
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Yan, Zhijia, Wenting Yang, Xiaoling Huang, Xiangrong Shi, and Wenyu Zhang. "A Reputation-Enhanced Hybrid Approach for Supplier Selection with Intuitionistic Fuzzy Evaluation Information." Mathematics 7, no. 3 (2019): 298. http://dx.doi.org/10.3390/math7030298.

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Selecting optimal suppliers in fuzzy environments has become a major challenge for enterprises. Reputation plays an important role in the process of supplier selection because of its fuzziness, dynamicity, and transitivity. In this study, we first present a novel intuitionistic fuzzy sets (IFS)-hyperlink-induced topic search (HITS) method that combines the intuitionistic fuzzy set with the hyperlink-induced topic search (HITS) algorithm to extend the ability of processing fuzzy information in order to obtain post-propagated reputation values of suppliers. Then, we employ the dynamic intuitioni
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Zhan, Hua Qun. "A Improved Topics Search Algorithm Based on PSO Strategy for Web Mining." Key Engineering Materials 439-440 (June 2010): 1481–86. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1481.

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HITS algorithm assigns same weight to links between Web pages,which results in topic drift. In this paper,a new focused crawling approach based on PSO Algorithm is proposed（PSOHITS）. The method electively seeks out pages that are relevant to a pre-defined set of topics using PSO Algorithm,increases the crawling chance of the web page following the web page with the low content-relevance,and broadens the relevant-searching scope of crawlers.Meanwhile,the hyperlink metadata is used to predict the topic-relevance of the web page pointed and quickens the information crawling. Experiments show that
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PEDERSEN, BOLETTE SANDFORD. "Using shallow linguistic analysis to improve search on Danish compounds." Natural Language Engineering 13, no. 1 (2006): 75–90. http://dx.doi.org/10.1017/s1351324906004256.

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In this paper we focus on a specific search-related query expansion topic, namely search on Danish compounds and expansion to some of their synonymous phrases. Compounds constitute a specific issue in search, in particular in languages where they are written in one word, as is the case for Danish and the other Scandinavian languages. For such languages, expansion of the query compound into separate lemmas is a way of finding the often frequent alternative synonymous phrases in which the content of a compound can also be expressed. However, it is crucial to note that the number of irrelevant hi
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Badia, Giovanna. "Google Scholar Out-Performs Many Subscription Databases when Keyword Searching." Evidence Based Library and Information Practice 5, no. 3 (2010): 39. http://dx.doi.org/10.18438/b8d328.

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A Review of:&#x0D; Walters, W. H. (2009). Google Scholar search performance: Comparative recall and precision. portal: Libraries and the Academy, 9(1), 5-24.&#x0D; &#x0D; Objective – To compare the search performance (i.e., recall and precision) of Google Scholar with that of 11 other bibliographic databases when using a keyword search to find references on later-life migration.&#x0D; &#x0D; Design – Comparative database evaluation.&#x0D; &#x0D; Setting – Not stated in the article. It appears from the author’s affiliation that this research took place in an academic institution of higher learn
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Vaseekaran, John, and Dr N. Srinivasan. "Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 12191–235. http://dx.doi.org/10.51201/jusst/21/05409.

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The demand for smartphone apps has grown with the rising interest in artificial intelligence. Thanks to a vast number of applicant service applications, choosing the smartphone apps you want to use has been very complex for consumers. It is therefore essential that the customer interface is improved and that individual suggestions are made. Conventional recommendation approaches can in some cases be effective but have some drawbacks, which generally lead to unreliable recommendations. This study provides a basis for recommending smartphone applications, which is built on the algorithm of Hyper
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Priyanka, R., and G. S. Megha. "Using Machine Learning To Build A Search Engine." Journal of Advance Research in Mobile Computing 3, no. 2 (2021): 1–5. https://doi.org/10.5281/zenodo.5215150.

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The Internet is a massive server and the most preferred abundant data source. We use search engine as a popular method to retrieve information from the internet. A search engine is a website through which users can search the content of the Internet. It is one of the primary ways that internet users find to obtain suitable information. Now a days search engine providers grows in popularity because they offer increased accuracy and extra functionality which is not possible in the general. Searching for information on the internet differs in several ways. In this paper we propose Page Ranking (P
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Zhao, Li Dong, Sheng Hui Liu, and Qing Chai. "A Novel Improved Web Structure Mining Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 3741–44. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3741.

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In this paper, a compact and effective web structure mining algorithm is developed according to the hypertext induced topic search (HITS). Firstly, the environments of traditional web structure mining are explored. Secondly, by analyzing the existing techniques, the existing problem and the lack of previous methods are explored. Finally, amid to overcome the deficiency of the traditional method, the paper introduced the time parameter and proposed a novel web structure mining algorithm. It turns out that the proposed algorithm has better effective and accuracy compared to the traditional metho
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Cheng, Xian, Liao Stephen Shaoyi, and Zhongsheng Hua. "Measuring the systemic importance of interconnected industries in the world economic system." Industrial Management & Data Systems 117, no. 1 (2017): 110–30. http://dx.doi.org/10.1108/imds-10-2015-0442.

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Purpose The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries. Design/methodology/approach First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals
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Dissertations / Theses on the topic "HyperlinkInduced Topic Search (HITS)"

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FILHO, FRANCISCO BENJAMIM. "XHITS: EXTENDING THE HITS ALGORITHM FOR DISTILLATION OF BROAD SEARCH TOPIC ON WWW." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7081@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O ambiente baseado em hyperlink possui na sua topologia informações substanciais sobre o seu conteúdo. Baseado nesse tipo de ambiente, Jon Kleingerg desenvolveu um conjunto de algoritmos, popularmente conhecido como HITS (Hyperlink Induced Topic Search), que utiliza a estrutura de hyperlinks na WWW para extrair essas informações. O foco central desses algoritmos é a classificação de tópicos de busca de caráter geral na WWW, através da descoberta de páginas que representam autoridade sobre tais tópicos. Para tanto, os algoritmos formu
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Book chapters on the topic "HyperlinkInduced Topic Search (HITS)"

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Chawla, Suruchi. "Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1877-8.ch008.

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This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recom
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Chawla, Suruchi. "Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search." In Information Retrieval and Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch012.

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This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recom
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Usbeck Ricardo, Ngonga Ngomo Axel-Cyrille, Röder Michael, et al. "AGDISTIS &ndash; Agnostic Disambiguation of Named Entities Using Linked Open Data." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-419-0-1113.

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Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. We address this issue by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm
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Conference papers on the topic "HyperlinkInduced Topic Search (HITS)"

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Hoa, Tran Trong, and Nguyen Ngoc Ha. "Edge-weighting Hyperlink-Induced Topic Search (E-HITS) Algorithm." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. ACM, 2017. http://dx.doi.org/10.1145/3110025.3110111.

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Journal articles
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