Background Music Recommendation on Short Video Sharing Platforms

Abstract

On short video sharing platforms, users often choose background music for their videos. In this paper, we study the problem of background music recommendation for short videos on short video sharing platforms. In our recommendation setting, the item (music) is not recommended directly to the user, but to the video created by the user. When making music recommendations for videos, we consider three important players: users, videos, and music. We define a unique background music recommendation problem and design a novel background music recommendation model to address the problem. We propose a model based on the deep learning framework to effectively address the distinctive three-way relationships among users, videos, and music. Our model considers not only of the conventional user–music alignment, but also the alignment between videos and music. To evaluate our model, we conduct comprehensive experiments on real-world data collected from one of the most popular short video sharing platforms. Our proposed model significantly outperforms other existing models in recommendation performance. The superiority of our proposed model remains consistent across various scenarios, including cold-start recommendations, data sets with varying density levels, and data sets spanning diverse video categories.