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Conference Papers Year : 2019

A Search Engine Optimization Recommender System

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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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Dates and versions

hal-02320874 , version 1 (22-10-2019)

Identifiers

  • HAL Id : hal-02320874 , version 1

Cite

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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