Debiased Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Understand

Micro-video background music recommendation is a complicated task where the matching degree between videos and uploader-selected background music is a major issue.

• However, the selection of the user-generated content (UGC) is biased caused by knowledge limitations and historical preferences among music of each uploader.
• In this paper, we propose a Debiased Cross-Modal (DebCM) matching model to alleviate the influence of such selection bias.
• Specifically, we design a teacher-student network to utilize the matching of segments of music videos, which is professional-generated content (PGC) with specialized music-matching techniques, to better alleviate the bias caused by insufficient knowledge of users.