  When recommending or advertising items to users, an emerging trend is to
present each multimedia item with a key frame image (e.g., the poster of a
movie). As each multimedia item can be represented as multiple fine-grained
visual images (e.g., related images of the movie), personalized key frame
recommendation is necessary in these applications to attract users' unique
visual preferences. However, previous personalized key frame recommendation
models relied on users' fine-grained image behavior of multimedia items (e.g.,
user-image interaction behavior), which is often not available in real
scenarios. In this paper, we study the general problem of joint multimedia item
and key frame recommendation in the absence of the fine-grained user-image
behavior. We argue that the key challenge of this problem lies in discovering
users' visual profiles for key frame recommendation, as most recommendation
models would fail without any users' fine-grained image behavior. To tackle
this challenge, we leverage users' item behavior by projecting users (items) in
two latent spaces: a collaborative latent space and a visual latent space. We
further design a model to discern both the collaborative and visual dimensions
of users, and model how users make decisive item preferences from these two
spaces. As a result, the learned user visual profiles could be directly applied
for key frame recommendation. Finally, experimental results on a real-world
dataset clearly show the effectiveness of our proposed model on the two
recommendation tasks.
