  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. The PGC data is captured by a teacher network to guide the
matching of uploader-selected UGC data of the student network by KL-based
knowledge transfer. In addition, uploaders' personal preferences of music
genres are identified as confounders that spuriously correlate music embeddings
and background music selections, resulting in the learned recommender system to
over-recommend music from the majority groups. To resolve such confounders in
the UGC data of the student network, backdoor adjustment is utilized to
deconfound the spurious correlation between music embeddings and prediction
scores. We further utilize Monte Carlo (MC) estimator with batch-level average
as the approximations to avoid integrating the entire confounder space
calculated by the adjustment. Extensive experiments on the TT-150k-genre
dataset demonstrate the effectiveness of the proposed method towards the
selection bias. The code is publicly available on:
\url{https://github.com/jing-1/DebCM}.
