  Numerous stereoscopic 3D movies are released every year to theaters and
created large revenues. Despite the improvement in stereo capturing and 3D
video post-production technology, stereoscopic artifacts which cause viewer
discomfort continue to appear even in high-budget films. Existing automatic 3D
video quality measurement tools can detect distortions in stereoscopic images
or videos, but they fail to consider the viewer's subjective perception of
those artifacts, and how these distortions affect their choices. In this paper,
we introduce a novel recommendation system for stereoscopic 3D movies based on
a latent factor model that meticulously analyse the viewer's subjective ratings
and influence of 3D video distortions on their preferences. To the best of our
knowledge, this is a first-of-its-kind model that recommends 3D movies based on
stereo-film quality ratings accounting correlation between the viewer's visual
discomfort and stereoscopic-artifact perception. The proposed model is trained
and tested on benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 S3D video quality
assessment datasets. The experiments revealed that resulting
matrix-factorization based recommendation system is able to generalize
considerably better for the viewer's subjective ratings.
