  Live streaming services are becoming increasingly popular due to real-time
interactions and entertainment. Viewers can chat and send comments or virtual
gifts to express their preferences for the streamers. Accurately modeling the
gifting interaction not only enhances users' experience but also increases
streamers' revenue. Previous studies on live streaming gifting prediction treat
this task as a conventional recommendation problem, and model users'
preferences using categorical data and observed historical behaviors. However,
it is challenging to precisely describe the real-time content changes in live
streaming using limited categorical information. Moreover, due to the sparsity
of gifting behaviors, capturing the preferences and intentions of users is
quite difficult. In this work, we propose MMBee based on real-time Multi-Modal
Fusion and Behaviour Expansion to address these issues. Specifically, we first
present a Multi-modal Fusion Module with Learnable Query (MFQ) to perceive the
dynamic content of streaming segments and process complex multi-modal
interactions, including images, text comments and speech. To alleviate the
sparsity issue of gifting behaviors, we present a novel Graph-guided Interest
Expansion (GIE) approach that learns both user and streamer representations on
large-scale gifting graphs with multi-modal attributes. Comprehensive
experiment results show that MMBee achieves significant performance
improvements on both public datasets and Kuaishou real-world streaming datasets
and the effectiveness has been further validated through online A/B
experiments. MMBee has been deployed and is serving hundreds of millions of
users at Kuaishou.
