Exploiting Sequential Music Preferences via Optimisation-Based Sequencing
October 26, 2023 11:38 amWe study the problem of constructing personalized playlists for users of music streaming services, in our case Spotify.
We study the problem of constructing personalized playlists for users of music streaming services, in our case Spotify.
TL;DR: On online platforms such as Spotify, recommender systems are increasingly tasked with improving users’ long-term satisfaction...
Reinforcement learning (RL) is an established tool for sequential decision making. In this work, we apply RL to solve an automatic music playlist generation problem...
A new approach to calibrating recommendations to user interests. Users’ interests are multi-faceted and representing different aspects of users’ interest in their recommendations is an important factor for recommender systems....
TL;DR: Survival analysis provides a framework to reason about time-to-event data; at Spotify, for example, we use it to understand and predict the way users might engage with Spotify in the future. In this work, we bring temporal-difference learning, a central idea in reinforcement learning, to survival analysis. We develop a new algorithm that trains a survival model from sequential data by leveraging a temporal consistency condition, and show that it outperforms direct regression on observed outcomes......
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