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Hybrid Semantic Recommender System for Chemical Compounds

Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the developm...

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Detalles Bibliográficos
Autores principales: Barros, Márcia, Moitinho, André, Couto, Francisco M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148023/
http://dx.doi.org/10.1007/978-3-030-45442-5_12
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author Barros, Márcia
Moitinho, André
Couto, Francisco M.
author_facet Barros, Márcia
Moitinho, André
Couto, Francisco M.
author_sort Barros, Márcia
collection PubMed
description Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking (BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.
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spelling pubmed-71480232020-04-13 Hybrid Semantic Recommender System for Chemical Compounds Barros, Márcia Moitinho, André Couto, Francisco M. Advances in Information Retrieval Article Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this particular field presents a challenge for the development of new recommendations models. In this work, we propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking (BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). We evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO. 2020-03-24 /pmc/articles/PMC7148023/ http://dx.doi.org/10.1007/978-3-030-45442-5_12 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Barros, Márcia
Moitinho, André
Couto, Francisco M.
Hybrid Semantic Recommender System for Chemical Compounds
title Hybrid Semantic Recommender System for Chemical Compounds
title_full Hybrid Semantic Recommender System for Chemical Compounds
title_fullStr Hybrid Semantic Recommender System for Chemical Compounds
title_full_unstemmed Hybrid Semantic Recommender System for Chemical Compounds
title_short Hybrid Semantic Recommender System for Chemical Compounds
title_sort hybrid semantic recommender system for chemical compounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148023/
http://dx.doi.org/10.1007/978-3-030-45442-5_12
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