# Cold-start Playlist Recommendation with Multitask Learning

## TL;DR

This paper introduces a multitask learning approach for cold-start playlist recommendation, effectively handling new playlists, users, and songs by leveraging playlist data and ranking loss minimization.

## Contribution

It proposes a novel multitask learning method that addresses all three cold-start scenarios using bipartite ranking and classification loss approximation.

## Key findings

- Effective in cold-start playlist recommendation
- Performs well on real playlist datasets
- Utilizes bipartite ranking for improved accuracy

## Abstract

Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users' existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation.