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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | A Brief Study On LTR And Usage Of Elasticsearch For Search Result Ranking |
| Authors Name: | Sai Manoj Tekumalla , A V Praveen Krishna , Nithin Kalavagunta , Sai Ganesh Katakam |
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| Author Reg. ID: |
IJNRD_202798
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| Published Paper Id: | IJNRD2307457 |
| Published In: | Volume 8 Issue 7, July-2023 |
| DOI: | |
| Abstract: | In this era of internet, information is a vital asset that forms the basis for almost every trade and constructs any meaningful interaction among various stakeholders. Tools like web browsers provide reliable sources of information to users who can find anything they want with minimal effort. A typical browser or information store uses a search engine to allow users to search for the information they need by entering relevant keywords, following which the engine generally returns a set of elements, each representing the information associated with a record in the server. Hence it is highly important to optimize the mechanism using which these engines return search results and the order in which the results are arranged, to provide satisfying user experience. Different search result ranking techniques are used by search engines, distinguished by the ranking algorithm each one uses and its suitability to the data maintained by the engine. Learning to rank (LTR) is a class of such algorithms, implementing which involves the process of training a ranking machine or model using various approaches to rank the search results. This research paper includes the findings acquired as an outcome of a brief study that was conducted on LTR algorithmic approaches for search result ranking and an elaborate account of the Elasticsearch platform, which is a cloud-based search engine configuration tool. This tool was used to develop a search engine with a customised ranking mechanism for the project associated with this research work to display the understanding achieved during the study. |
| Keywords: | Web Browsers, Search Engines, Ranking |
| Cite Article: | "A Brief Study On LTR And Usage Of Elasticsearch For Search Result Ranking ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 7, page no.e494-e502, July-2023, Available :http://www.ijnrd.org/papers/IJNRD2307457.pdf |
| Downloads: | 000118762 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2307457 Registration ID: 202798 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): Page No: e494-e502 Country: Vizianagaram, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2307457 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2307457 |
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ISSN: 2456-4184
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