• Aucun résultat trouvé

Exploiting Twitter's Collective Knowledge for Music Recommendations

N/A
N/A
Protected

Academic year: 2022

Partager "Exploiting Twitter's Collective Knowledge for Music Recommendations"

Copied!
4
0
0

Texte intégral

Références

Documents relatifs

Abstract: Generating a sequence of music tracks recommendations to a group of users can be addressed by balancing the users’ satisfaction for a set of rec- ommendations (the

In order to evaluate our approach, we compare WiFER with the Spark entity recommendation system [4] that uses more than 100 features extracted from different data sources such as

To address this issue, we propose to learn the latent social listening representations by the DeepWalk method, and then integrate the learned representations into Factorization

One reason for increased knowledge ex- change between group members is group diversity (in terms of dimensions such as demographic and educational back- ground), i.e., the higher

Therefore, it is of high importance that patients are able to manage their diabetes treatment on their own aiming at near normal blood glucose levels (American Diabetes

7 http://en.wikipedia.org/wiki/Category:Wikipedia_administration.. Although explicit ratings for entities are generally available, finding the map- pings of these entities to a

Although the data demonstrate (1) that the domain of pain assessment terminology is poorly covered in the BioPortal resources, (2) that the way in which the

Continuous support during labour as a separate intervention is recommended in this guideline (see Recommendation No. 12) Evidence summary: Package of care for active management