  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 effectively overcome
these limitations. LARP is a three-stage contrastive learning framework that
integrates both multi-modal and relational signals into its learned
representations. Our framework uses increasing stages of task-specific
abstraction: within-track (language-audio) contrastive loss, track-track
contrastive loss, and track-playlist contrastive loss. Experimental results on
two publicly available datasets demonstrate the efficacy of LARP over uni-modal
and multi-modal models for playlist continuation in a cold-start setting. Code
and dataset are released at: https://github.com/Rsalganik1123/LARP.
