Live-streaming platforms broadcast user-generated video in real-time. Recommendation on these platforms shares similarities with traditional settings, such as a large volume of heterogeneous content and highly skewed interaction distributions. However, several challenges must be overcome to adapt recommendation algorithms to live-streaming platforms: first, content availability is dynamic which restricts users to choose from only a subset of items at any given time; during training and inference we must carefully handle this factor in order to properly account for such signals, where 'non-interactions' reflect availability as much as implicit preference. Streamers are also fundamentally different from 'items' in traditional settings: repeat consumption of specific channels plays a significant role, though the content itself is fundamentally ephemeral.

In this work, we study recommendation in this setting of a dynamically evolving set of available items. We propose LiveRec, a self-attentive model that personalizes item ranking based on both historical interactions and current availability. We also show that carefully modelling repeat consumption plays a significant role in model performance. To validate our approach, and to inspire further research on this setting, we release a dataset containing 475M user interactions on Twitch over a 43-day period. We evaluate our approach on a recommendation task and show our method to outperform various strong baselines in ranking the currently available content.