  Music streaming services heavily rely on their recommendation engines to
continuously provide content to their consumers. Sequential recommendation
consequently has seen considerable attention in current literature, where state
of the art approaches focus on self-attentive models leveraging contextual
information such as long and short-term user history and item features;
however, most of these studies focus on long-form content domains (retail,
movie, etc.) rather than short-form, such as music. Additionally, many do not
explore incorporating negative session-level feedback during training. In this
study, we investigate the use of transformer-based self-attentive architectures
to learn implicit session-level information for sequential music
recommendation. We additionally propose a contrastive learning task to
incorporate negative feedback (e.g skipped tracks) to promote positive hits and
penalize negative hits. This task is formulated as a simple loss term that can
be incorporated into a variety of deep learning architectures for sequential
recommendation. Our experiments show that this results in consistent
performance gains over the baseline architectures ignoring negative user
feedback.
