584 citations · 607 across the 7 of their papers we have counts for
6 papers · 1 filter
Multi-objective Hyper-parameter Optimization of Behavioral Song Embeddings
Massimo Quadrana, Antoine Larreche-Mouly, Matthias Mauch
Song embeddings are a key component of most music recommendation engines. In this work, we study the hyper-parameter optimization of behavioral song embeddings based on Word2Vec on…
The Importance of Song Context and Song Order in Automated Music Playlist Generation
Andreu Vall, Massimo Quadrana, Markus Schedl +1
The automated generation of music playlists can be naturally regarded as a sequential task, where a recommender system suggests a stream of songs that constitute a listening sessio…
Modeling Musical Taste Evolution with Recurrent Neural Networks
Massimo Quadrana, Marta Reznakova, Tao Ye +2
Finding the music of the moment can often be a challenging problem, even for well-versed music listeners. Musical tastes are constantly in flux, and the problem of developing compu…
Sequence-Aware Recommender Systems
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often bas…
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation
Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi +1
Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features…
Toward Active Learning in Cross-domain Recommender Systems
Roberto Pagano, Massimo Quadrana, Mehdi Elahi +1
One of the main challenges in Recommender Systems (RSs) is the New User problem which happens when the system has to generate personalised recommendations for a new user whom the s…