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, Liste des tableaux, vol.4, issue.1

.. , Description des jeux de données Le jeu Twitter Sanders est utilisé pour l'apprentissage de FEDA comme cible virtuelle, p.42

, Scores en précision sur le Golden Standard [Che+12] en fonction des sources considérées pour l'apprentissage d'un SVM classique, p.43

F. and S. , Scores en précision sur le Golden Standard [Che+12] pour les modèles à transfert explicite, pp.44-48

, Description des films inclus dans le jeu de données [Che+12] (nombre de tweets associés et résultat au box office, exprimé en dollars) . . . . 46 4

, Caractéristiques et initialisations associées

, Le nom de chaque jeu de données se lit de la façon suivante : les deux premières lettres indiquent la source (Ratebeer ou Amazon), le chiffre après le u indique le nombre d'utilisateurs considérés et celui après le i, le nombre d'items considérés, Tailles des jeux de données utilisés

.. Une-critique-du-site-ratebeer.-com,

.. Une-critique-du-site-amazon.-com,

L. , Au) en erreur quadratique moyenne sur les critiques de test (meilleurs résultats en gras) On remarque que l'ajout de la dimension textuelle améliore les résultats sur tous les datasets. De plus, sur quasiment tous les datasets, l'utilisation du texte brut présente des résultats équivalents ou meilleurs que, Résultats des modèles sur les bases Ratebeer (RB) et Amazon

, Résultats des modèles sur les différentes bases en erreur de classification (positif/négatif) sur les critiques de test (meilleurs résultats en gras)

.. , Encoreune fois, l'ajout de la dimension textuelle améliore les résultats sur tous les datasets, pp.62-67

, Exemples de critiques où le texte apporte une meilleure classification sur Au21352i12253 Note décrit la note associée à cette revue, MF et Text décrivent les prédictions obtenues à l'aide de chacun de ces modèles

R. , Exemples de critiques où le texte apporte une meilleure classification sur

M. , Prédictions de texte de critiques issues de notre

.. Propriétés-des-jeux-de-données, , p.81

, Descriptif des jeux de données AMMICO et Flickr-Pisa explicitant leur nombre d'items (POI) et de parcours-utilisateur (traces)

M. Résultats-en-prédiction-de-notes, E. Tsvd, T. , and C. , , p.85