MUFM: A Mamba-Enhanced Feedback Model for Micro Video Popularity Prediction
This work addresses the challenge of understanding and predicting virality for micro-videos, which is crucial for recommendation algorithms and platform applications, though it appears incremental as it builds on existing feedback and event modeling approaches.
The paper tackles the problem of predicting micro-video popularity by modeling long-term dependencies in user feedback and dynamic event interactions using a Mamba Hawkes process, achieving a 23.2% improvement over state-of-the-art methods on a large-scale multi-modal dataset.
The surge in micro-videos is transforming the concept of popularity. As researchers delve into vast multi-modal datasets, there is a growing interest in understanding the origins of this popularity and the forces driving its rapid expansion. Recent studies suggest that the virality of short videos is not only tied to their inherent multi-modal content but is also heavily influenced by the strength of platform recommendations driven by audience feedback. In this paper, we introduce a framework for capturing long-term dependencies in user feedback and dynamic event interactions, based on the Mamba Hawkes process. Our experiments on the large-scale open-source multi-modal dataset show that our model significantly outperforms state-of-the-art approaches across various metrics by 23.2%. We believe our model's capability to map the relationships within user feedback behavior sequences will not only contribute to the evolution of next-generation recommendation algorithms and platform applications but also enhance our understanding of micro video dissemination and its broader societal impact.