About: Hybrid Semantic Recommender System for Chemical Compounds

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Hybrid Semantic Recommender System for Chemical Compounds
Creator	Couto, Francisco Barros, Márcia Moitinho, André
Source	PMC
abstract	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.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_12
has license	no-cc
sha1sum (hex)	a27935041c8624b20a7a107148d90b45d8d182ed
schema:url	https://doi.org/10.1007/978-3-030-45442-5_12
resource representing a document's title	Hybrid Semantic Recommender System for Chemical Compounds
has PubMed Central identifier	PMC7148023
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:a27935041c8624b20a7a107148d90b45d8d182ed#body_text
is schema:about of	named entity 'field' named entity 'Compounds' named entity 'Compounds' named entity 'EVALUATED' named entity 'ART' named entity 'RESEARCHER' named entity 'FILTERING ALGORITHMS' named entity 'FIELD' named entity 'LACK' named entity 'INCREASE' named entity 'implicit' named entity 'Chemical' named entity 'implicit' named entity 'Least Squares' named entity 'Compounds' named entity 'semantic similarity' named entity 'ontology' named entity 'ALS' named entity 'ChEBI' named entity 'Chemical Compounds' named entity 'Chemical Compounds' named entity 'Chemical Compounds' named entity 'Hybrid' named entity 'algorithm' named entity 'ALS' named entity 'semantic similarity' named entity 'Hybrid algorithm' named entity 'ALS' named entity 'ChEBI' named entity 'semantic' named entity 'dimensionality reduction' named entity 'ontologies' named entity 'test set' named entity 'semantic similarity' named entity 'Semantic Similarity' named entity 'ontology' named entity 'Disease Ontology' named entity 'algorithm' named entity 'ALS' named entity 'ALS' named entity 'semantic similarity' named entity 'ontology' named entity 'ChEBI' named entity 'ALS' named entity 'Hybrid algorithm' named entity 'negative feedback' named entity 'semantic similarity' named entity 'RSs' named entity 'semantic' named entity 'algorithm' named entity 'ontologies' named entity 'ALS' named entity 'ChEBI' named entity 'semantic' named entity 'test set' named entity 'cosine similarity' named entity 'ALS' named entity 'test set' named entity 'Jaccard' named entity 'shortest path' named entity 'semantic similarity' named entity 'Pearson Correlation' named entity 'ontology' named entity 'ALS' named entity 'nDCG' named entity 'ontology' named entity 'semantic' named entity 'test set' named entity 'algorithm' named entity 'similarity measure' named entity 'ALS' named entity 'F-measure' named entity 'data collected' named entity 'ChEBI' named entity 'ALS' named entity 'ontologies' named entity 'DiShIn' named entity 'Ontology' named entity 'algorithm' named entity 'Pearson Correlation' named entity 'semantic similarity'

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