  With the increasing availability of videos, how to edit them and present the
most interesting parts to users, i.e., video highlight, has become an urgent
need with many broad applications. As users'visual preferences are subjective
and vary from person to person, previous generalized video highlight extraction
models fail to tailor to users' unique preferences. In this paper, we study the
problem of personalized video highlight recommendation with rich visual
content. By dividing each video into non-overlapping segments, we formulate the
problem as a personalized segment recommendation task with many new segments in
the test stage. The key challenges of this problem lie in: the cold-start users
with limited video highlight records in the training data and new segments
without any user ratings at the test stage. In this paper, we propose an
inductive Graph based Transfer learning framework for personalized video
highlight Recommendation (TransGRec). TransGRec is composed of two parts: a
graph neural network followed by an item embedding transfer network.
Specifically, the graph neural network part exploits the higher-order proximity
between users and segments to alleviate the user cold-start problem. The
transfer network is designed to approximate the learned item embeddings from
graph neural networks by taking each item's visual content as input, in order
to tackle the new segment problem in the test phase. We design two detailed
implementations of the transfer learning optimization function, and we show how
the two parts of TransGRec can be efficiently optimized with different transfer
learning optimization functions. Extensive experimental results on a real-world
dataset clearly show the effectiveness of our proposed model.
