Please use this identifier to cite or link to this item: https://repository.usc.edu.co/handle/20.500.12421/2689
Title: A search engine optimization recommender system
Authors: Hoyos, Christian D.
Duque, Juan C.
Barco, Andres F.
Vareilles, Elise
Issue Date: 19-Sep-2019
Publisher: CEUR-WS
Abstract: Search Engine Optimization reefers to the process of improving 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 engine optimization process becomes a necessity. On this regard, this paper presents a recommender system to improve search engine optimization 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. Recommendations are given in natural language using a predefined set of sentences
URI: https://repository.usc.edu.co/handle/20.500.12421/2689
ISSN: 16130073
Appears in Collections:Artículos Científicos

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