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Using Machine Learning To Build A Search Engine

Priyanka R, Megha G S

Abstract


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 (PR), Weighted PR(WPR) and Hyperlink Induced Topic Search (HITS) algorithms using machine learning technique to greatly automate the methods and classification of Web pages. Search engines play a critical role in the growth of the internet; they assist many internet users in quickly finding relevant information. It can be used to do the basic process of retrieving information.

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References


Karwa, R., & Honmane, V. (2019, May). Building search engine using machine learning technique. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 1061-1064). IEEE.

Liang, C. (2011, July). User profile for personalized web search. In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (Vol. 3, pp. 1847-1850). IEEE.

Hongqing, G., Peiyong, S., Wenzhong, G., & Kun, G. (2018, November). Component-based Assembling Tool and Runtime Engine for the Machine Learning Process. In 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB) (pp. 1-7). IEEE..

Oyama, S., Kokubo, T., & Ishida, T. (2004). Domain-specific web search with keyword spices. IEEE Transactions on knowledge and data engineering, 16(1), 17-27.

Usta, A., Altingovde, I. S., Ozcan, R., & Ulusoy, O. (2021). Learning to Rank for Educational Search Engines. IEEE Transactions on Learning Technologies.

Alchalabi, A. E., Elsharnoby, M., & Khawaldeh, S. (2016, July). Rightscope: Detecting search campaingns positive and negative queries. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC) 1. 290-295. IEEE.

Lu, H., Su, S., Tian, Z., & Zhu, C. (2019). A novel search engine for Internet of Everything based on dynamic prediction. China Communications, 16(3), 42-52.

Wang, Z., Wang, Q., & Wang, D. (2006, October). Application of domain-specific search method in meta-search engine on internet. In The Proceedings of the Multiconference on" Computational Engineering in Systems Applications" 2. 2078-2085. IEEE.

Hatcher, W. G., Qian, C., Gao, W., Liang, F., Hua, K., & Yu, W. (2021). Towards Efficient and Intelligent Internet of Things Search Engine. IEEE Access, 9, 15778-15795.

Liang, F., Qian, C., Hatcher, W. G., & Yu, W. (2019). Search engine for the internet of things: Lessons from web search, vision, and opportunities. IEEE Access, 7, 104673-104691..


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