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A Search Engine Optimization Recommender System

Abstract : Search Engine Optimization reefers to the process of im- proving the position of a given website in a web search engine results. This is typically done by adding a set of parameters and metadata to the hypertext files of the website. As nowadays the majority of the web-content creators are non-experts, automation of the search en- gine optimization process becomes a necessity. On this regard, this paper presents a recommender system to improve search engine op- timization based on the site’s content and creator’s preferences. It exploits text analysis for labels and tags, artificial intelligence for deducing content intention and topics, and case-based reasoning for generating recommendations of parameters and metadata. Recom- mendations are given in natural language using a predefined set of sentences.
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Submitted on : Tuesday, October 22, 2019 - 12:32:25 PM
Last modification on : Friday, August 5, 2022 - 11:42:12 AM
Long-term archiving on: : Thursday, January 23, 2020 - 5:25:37 PM


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  • HAL Id : hal-02320874, version 1



Christian D. Hoyos, Juan C. Duque, Andrés F. Barco, Élise Vareilles. A Search Engine Optimization Recommender System. ConfWS’19 - 21st Configuration Workshop, Oct 2019, Hambourg, Germany. p.43-47. ⟨hal-02320874⟩



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