November 2024
Intermediate to advanced
420 pages
12h 55m
French
Content preview from Le guide du praticien pour les données graphiques
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Chapitre 12. Recommandations en matière de production
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Presque toutes les applications que tu utilises aujourd'hui ont une section"recommandée pour toi".
Pense à tes applications préférées pour les médias numériques, les vêtements ou les fournisseurs de vente au détail. Nous comptons sur le volet de recommandation de nos applications médias pour trouver de nouveaux films à regarder ou des livres à lire. Des marques comme Nike adaptent ton expérience in-app avec des garde-robes personnelles et personnalisées. Même l'application de ton épicerie locale te recommande des coupons pour ta prochaine visite.
Les recommandations et la personnalisation ont infiltré presque tous les coins et recoins de notre expérience numérique.
Mais comment construire un processus qui fournit des recommandations au sein d'une application à la vitesse à laquelle nous avons tous appris à nous attendre ?
Comme nous l'avons vu au chapitre 10, il est tout à fait possible de relier des sources de données à un graphique et de créer des recommandations personnalisées pour un utilisateur. Cependant, la quantité de données nécessaire pour traiter à grande échelle une recommandation basée sur un graphique limite considérablement l'utilisation du filtrage collaboratif dans une application de production.
Nous ne pensons pas qu'un utilisateur de l'appli de vêtements Nike va attendre les multiples ...
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