  Personalized video highlight detection aims to shorten a long video to
interesting moments according to a user's preference, which has recently raised
the community's attention. Current methods regard the user's history as
holistic information to predict the user's preference but negating the inherent
diversity of the user's interests, resulting in vague preference
representation. In this paper, we propose a simple yet efficient preference
reasoning framework (PR-Net) to explicitly take the diverse interests into
account for frame-level highlight prediction. Specifically, distinct
user-specific preferences for each input query frame are produced, presented as
the similarity weighted sum of history highlights to the corresponding query
frame. Next, distinct comprehensive preferences are formed by the user-specific
preferences and a learnable generic preference for more overall highlight
measurement. Lastly, the degree of highlight and non-highlight for each query
frame is calculated as semantic similarity to its comprehensive and
non-highlight preferences, respectively. Besides, to alleviate the ambiguity
due to the incomplete annotation, a new bi-directional contrastive loss is
proposed to ensure a compact and differentiable metric space. In this way, our
method significantly outperforms state-of-the-art methods with a relative
improvement of 12% in mean accuracy precision.
