Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based attention language model for automatic, personalized playlist generation that adapts to user preferences, producing coherent and diverse playlists efficiently.

Contribution

It presents a novel attention RNN language model optimized with reinforcement learning for flexible, personalized playlist creation, addressing limitations of manual curation.

Findings

Generates coherent playlists automatically

Adapts to user preferences for diversity and novelty

Outperforms baseline methods in personalization

Abstract

Songs can be well arranged by professional music curators to form a riveting playlist that creates engaging listening experiences. However, it is time-consuming for curators to timely rearrange these playlists for fitting trends in future. By exploiting the techniques of deep learning and reinforcement learning, in this paper, we consider music playlist generation as a language modeling problem and solve it by the proposed attention language model with policy gradient. We develop a systematic and interactive approach so that the resulting playlists can be tuned flexibly according to user preferences. Considering a playlist as a sequence of words, we first train our attention RNN language model on baseline recommended playlists. By optimizing suitable imposed reward functions, the model is thus refined for corresponding preferences. The experimental results demonstrate that our approach not only generates coherent playlists automatically but is also able to flexibly recommend personalized playlists for diversity, novelty and freshness.