Eleonora Fois
Representing items as word-embedding vectors and generating recommendations by measuring their linear independence
BORATTO, LUDOVICO;CARTA, SALVATORE MARIO;FENU, GIANNI;Saia, Roberto
2016-01-01
Abstract
In order to generate effective results, it is essential for a recommender system to model the information about the user interests in a profile. Even though word embeddings (i.e., vector representations of textual descriptions) have proven to be effective in many contexts, a content-based recommendation approach that employs them is still less effective than collaborative strategies (e.g., SVD). In order to overcome this issue, we introduce a novel criterion to evaluate the word-embedding representation of the items a user rated. The proposed approach defines a vector space in which the similarity between an unevaluated item and those in a user profile is measured in terms of linear independence. Experiments show its effectiveness to perform a better ranking of the items, w.r.t. collaborative filtering, both when compared to a latent-factor-based approach (SVD) and to a classic neighborhood user-based system.File | Dimensione | Formato | |
---|---|---|---|
RECSYS - Representing Items as Word-Embedding Vectors and Generating Recommendations by Measuring their Linear Independence.pdf Solo gestori archivio
Tipologia: versione editoriale
Dimensione 262.07 kB
Formato Adobe PDF
|
262.07 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.