Panoramic Interests: Stylistic-Content Aware Personalized Headline
  Generation
Authors: name: Junhong Lian, name: Xiang Ao, name: Xinyu Liu, name: Yang Liu, name: Qing He
Organization: Institute of Computing Technology, Chinese Academy of Sciences

Personalized news headline generation aims to provide users with
attention-grabbing headlines that are tailored to their preferences. Prevailing
methods focus on user-oriented content preferences, but most of them overlook
the fact that diverse stylistic preferences are integral to users' panoramic
interests, leading to suboptimal personalization. In view of this, we propose a
novel Stylistic-Content Aware Personalized Headline Generation (SCAPE)
framework. SCAPE extracts both content and stylistic features from headlines
with the aid of large language model (LLM) collaboration. It further adaptively
integrates users' long- and short-term interests through a contrastive
learning-based hierarchical fusion network. By incorporating the panoramic
interests into the headline generator, SCAPE reflects users' stylistic-content
preferences during the generation process. Extensive experiments on the
real-world dataset PENS demonstrate the superiority of SCAPE over baselines.