TY - GEN
T1 - Online learning to rank for sequential music recommendation
AU - Pereira, Bruno L.
AU - Ueda, Alberto
AU - Penha, Gustavo
AU - Santos, Rodrygo L.T.
AU - Ziviani, Nivio
PY - 2019/9/10
Y1 - 2019/9/10
N2 - The prominent success of music streaming services has brought increasingly complex challenges for music recommendation. In particular, in a streaming setting, songs are consumed sequentially within a listening session, which should cater not only for the user's historical preferences, but also for eventual preference drifts, triggered by a sudden change in the user's context. In this paper, we propose a novel online learning to rank approach for music recommendation aimed to continuously learn from the user's listening feedback. In contrast to existing online learning approaches for music recommendation, we leverage implicit feedback as the only signal of the user's preference. Moreover, to adapt rapidly to preference drifts over millions of songs, we represent each song in a lower dimensional feature space and explore multiple directions in this space as duels of candidate recommendation models. Our thorough evaluation using listening sessions from Last.fm demonstrates the efectiveness of our approach at learning faster and better compared to state-of-the-art online learning approaches.
AB - The prominent success of music streaming services has brought increasingly complex challenges for music recommendation. In particular, in a streaming setting, songs are consumed sequentially within a listening session, which should cater not only for the user's historical preferences, but also for eventual preference drifts, triggered by a sudden change in the user's context. In this paper, we propose a novel online learning to rank approach for music recommendation aimed to continuously learn from the user's listening feedback. In contrast to existing online learning approaches for music recommendation, we leverage implicit feedback as the only signal of the user's preference. Moreover, to adapt rapidly to preference drifts over millions of songs, we represent each song in a lower dimensional feature space and explore multiple directions in this space as duels of candidate recommendation models. Our thorough evaluation using listening sessions from Last.fm demonstrates the efectiveness of our approach at learning faster and better compared to state-of-the-art online learning approaches.
KW - Implicit feedback
KW - Music recommendation
KW - Online learning to rank
UR - http://www.scopus.com/inward/record.url?scp=85073390491&partnerID=8YFLogxK
U2 - 10.1145/3298689.3347019
DO - 10.1145/3298689.3347019
M3 - Conference contribution
T3 - RecSys 2019 - 13th ACM Conference on Recommender Systems
SP - 237
EP - 245
BT - RecSys 2019 - 13th ACM Conference on Recommender Systems
PB - Association for Computing Machinery (ACM)
T2 - 13th ACM Conference on Recommender Systems, RecSys 2019
Y2 - 16 September 2019 through 20 September 2019
ER -