Feature analysis for dynamic live streaming content recommendation considering the relationship between streamers and audience

Abstract

The content recommendation problem for live-streaming platforms presents several unique challenges not present in other media platforms. First, the recommended content is ad-hoc CGM content generated by the live streamer. Because it is dynamic, the audience is permanently restricted to choosing from the content being streamed when they visit the platform. In addition, the content is diverse and changes in real-time, influenced by the communication between the audience and the live streamer, so there is also the problem of obtaining the streamed content's features. Furthermore, it has been pointed out that audience behavior patterns on live-streaming platforms include exploring new streamers and exploiting known streamers. Understanding and appropriately capturing the dynamics of these viewer states is considered essential for a live-streaming content recommendation but has not been sufficiently studied. In this study, to investigate appropriate approaches to these problems specific to live-streaming platforms, we analyzed viewing behavior using user logs of a live-streaming platform.