Panoramic Interests: Stylistic-Content Aware Personalized Headline
  Generation
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.
679079a3a78e61ae7817a5f4
Junhong Lian
claimed_verified
2025-01-22T10:08:10.850Z
63a15c583c8841cfe2dbc5c6
https://cdn-avatars.huggingface.co/v1/production/uploads/1671519286473-noauth.jpeg
Lian Junhong
user
THEATLAS
679079a3a78e61ae7817a5f5
Xiang Ao
admin_assigned
2025-01-22T20:53:33.832Z
6462ccfc514ee1645bd27b9e
/avatars/38d77fa41da3af7e358a8ea65c17aa3c.svg
Xiang Ao
user
aoroseeee
679079a3a78e61ae7817a5f6
Xinyu Liu
679079a3a78e61ae7817a5f7
Yang Liu
679079a3a78e61ae7817a5f8
Qing He
Panoramic Interests: Stylistic-Content Aware Personalized Headline
  Generation
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.
The SCAPE framework enhances personalized headline generation by integrating both content and stylistic preferences using a contrastive learning-based hierarchical fusion network.
large language model LLM contrastive learning hierarchical fusion network personalized headline generation