兼顾风格与内容的个性化标题生成框架
Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
- 利用大模型提取标题的内容与风格特征
- 通过对比学习融合用户长短期兴趣，提升个性化效果
- 适合关注用户偏好建模与新闻生成的研究者
个性化新闻标题生成旨在为用户提供吸引眼球且符合偏好的标题。现有方法多关注用户的内容偏好，却忽略了风格偏好同样是用户全景兴趣的重要组成部分，导致个性化效果不佳。为此，本文提出新颖的风格-内容感知个性化标题生成框架（SCAPE）。SCAPE借助大语言模型协作，从标题中提取内容与风格特征，并通过基于对比学习的分层融合网络，自适应整合用户的长期与短期兴趣。将全景兴趣融入生成过程后，SCAPE在真实数据集PENS上的实验表明，其性能显著优于基线方法。
原文摘要 · Abstract (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.
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