Systèmes de recommandation contextuels : Vers une typologie de contexte

Negre, Elsa

doi:10.21494/iste.op.2020.0584

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The principle is to use a customer's needs and interests as input to propose a list of recommended products. Many applications rely solely on the products customers buy and explicitly rate them to represent their interests, but such systems can also take into account other attributes, including products viewed and demographic data [3]. The objective of a recommender system is to make it easier for the user to find relevant objects according to his preferences.…”

Section: Introductionmentioning

confidence: 99%

Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Lafraxo,

Hami,

Merrakchi

et al. 2023

Int. J. Onl. Eng.

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This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.

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Section: Introductionmentioning

confidence: 99%