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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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. |
format | Online Article Text |
id | pubmed-7148023 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT barrosmarcia hybridsemanticrecommendersystemforchemicalcompounds AT moitinhoandre hybridsemanticrecommendersystemforchemicalcompounds AT coutofranciscom hybridsemanticrecommendersystemforchemicalcompounds |