The design of recommendation algorithms aware of the user's context has been the subject
of great interest in the scientific community, especially in the music domain where contextual …

Mobile Internet allows consumers the access to all musics on the mobile platform. Since it is
not feasible to manually select music due to the size constraint of mobile devices, music …

In emotion-aware music recommender systems, the user's current emotion is identified and
considered in recommending music to him. We have two motivations to extend the existing …

Integrating information about the listener's cultural background when building music
recommender systems has recently been identified as a means to improve recommendation …

Factorization machines (FMs) and their neural network variants (neural FMs) for modeling
second-order feature interactions are effective in building modern recommendation systems …

In the last decade, music consumption has changed dramatically as humans have
increasingly started to use music streaming platforms. While such platforms provide access …

In this paper, we study the role of serendipity in music playlists. Serendipity is an important
construct in recommendations, and finding an indicator of serendipity in a user-created …

In the era of big data, users often seem to be unable to make choices in the face of such a
huge range of options. This is especially the case with music; consequently, personalized …

Cette thèse a pour objet l'utilisation de l'intelligence artificielle (IA) pour la recommandation
de musique en tant que prédiction de goûts. Notre recherche s' est focalisée sur l'étude des …

Music is ubiquitous in today's world—almost everyone enjoys listening to music. With the
rise of streaming platforms, listeners now have access to more music than ever before. While …