全景兴趣：风格内容感知个性化标题生成

Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation

January 21, 2025

作者: Junhong Lian, Xiang Ao, Xinyu Liu, Yang Liu, Qing He

cs.AI

摘要

个性化新闻标题生成旨在为用户提供符合其偏好的引人注目标题。现有方法侧重于用户导向的内容偏好，但大多数方法忽视了多样化的风格偏好对用户全面兴趣的重要性，导致个性化效果不佳。鉴此，我们提出了一种新颖的风格-内容感知个性化标题生成（SCAPE）框架。SCAPE利用大型语言模型（LLM）协作从标题中提取内容和风格特征。它进一步通过对比学习为基础的分层融合网络，自适应地整合用户的长期和短期兴趣。通过将全面兴趣融入标题生成器，SCAPE在生成过程中反映用户的风格-内容偏好。在真实数据集PENS上进行的大量实验表明，SCAPE相对于基线方法具有优越性。

English

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.