research
∙
08/24/2023
On the Consistency of Average Embeddings for Item Recommendation
A prevalent practice in recommender systems consists in averaging item e...
share
research
∙
07/06/2023
Track Mix Generation on Music Streaming Services using Transformers
This paper introduces Track Mix, a personalized playlist generation syst...
research
∙
04/17/2023
Attention Mixtures for Time-Aware Sequential Recommendation
Transformers emerged as powerful methods for sequential recommendation. ...
research
∙
04/12/2023
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
Music streaming services often aim to recommend songs for users to exten...
research
∙
07/15/2022
Flow Moods: Recommending Music by Moods on Deezer
The music streaming service Deezer extensively relies on its Flow algori...
research
∙
05/29/2022
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerg...
research
∙
08/02/2021
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
On an artist's profile page, music streaming services frequently recomme...
research
∙
07/26/2021
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Collaborative Metric Learning (CML) recently emerged as a powerful parad...
research
∙
06/07/2021
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
Music streaming services heavily rely on recommender systems to improve ...
research
∙
10/13/2020
Modeling the Music Genre Perception across Language-Bound Cultures
The music genre perception expressed through human annotations of artist...
research
∙
09/16/2020
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
Annotating music items with music genres is crucial for music recommenda...
research
∙
09/14/2020
Carousel Personalization in Music Streaming Apps with Contextual Bandits
Media services providers, such as music streaming platforms, frequently ...
research
∙
02/05/2020
FastGAE: Fast, Scalable and Effective Graph Autoencoders with Stochastic Subgraph Decoding
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful ...
research
∙
01/21/2020
Simple and Effective Graph Autoencoders with One-Hop Linear Models
Graph autoencoders (AE) and variational autoencoders (VAE) recently emer...
research
∙
10/02/2019
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Graph autoencoders (AE) and variational autoencoders (VAE) recently emer...
research
∙
05/23/2019
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emer...
research
∙
02/23/2019