Can I read LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation on EtoBox?

LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation by Salganik, Rebecca; Liu, Xiaohao; Ma, Yunshan; Kang, Jian; Chua, Tat-Seng is a scholarly article available to read on EtoBox.

What is LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation about?

As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the success of music streaming. Currently, many existing playlist continuation approaches rely on collaborative filtering methods to perform recommendation. However, such methods will struggle to recommend songs that lack interaction data, an issue known as the cold-start problem. Current approaches to this challenge design complex mechanisms for extracting relational signals from sparse collaborative data and integrating them into content representations. However, these approaches leave content representation learning out of scope and utilize frozen, pre-trained content models that may not be aligned with the distribution or format of a specific musical setting. Furthermore, even the musical state-of-the-art content modules are either (1) incompatible with the cold-start setting or (2) unable to effectively integrate cross-modal and relational signals. In this paper, we introduce LARP, a multi-modal cold-start playlist continuation model, to

Author

Salganik, Rebecca; Liu, Xiaohao; Ma, Yunshan; Kang, Jian; Chua, Tat-Seng

Published

2024

Language

EN