Autoencoders for Next-Track-Recommendation

Thesis Type Master
Thesis Status
Student Michael Vötter
Thesis Supervisor
Research Field

In music recommender systems, playlist continuation is the task of continuing a user's playlist with a tting next track, often also referred to as next-track or sequential recommendation. This work investigates the suitability and applicability of autoencoders for the task of playlist continuation. We utilize autoencoders and hence, representation learning to continue playlists. Our approach is inspired by the usage of autoencoders to denoise images and we consider the missing next-track as a noisy input. Particularly, we design different autoencoders and deep autoencoders for this specifc task and investigate the effects of different layouts on the overall suitability of recommendations produced by the resulting recommender systems. To evaluate the suitability of recommendations produced by the proposed approach, we utilize the AotM-2011 and LFM-1b datasets. Based on those datasets, we show that it is possible to outperform a kNN baseline with our autoencoder approach.