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