全景興趣：風格內容感知個性化標題生成

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

個性化新聞標題生成旨在為用戶提供符合其偏好的引人注目標題。目前的方法著重於用戶導向的內容偏好，但大多數忽略了多樣的風格偏好對用戶全面興趣的重要性，導致個性化效果不佳。鑑此，我們提出了一個新穎的風格-內容感知個性化標題生成（SCAPE）框架。SCAPE利用大型語言模型（LLM）協作從標題中提取內容和風格特徵。它進一步通過對比學習為基礎的階層融合網絡，適應性地整合用戶的長期和短期興趣。通過將全面興趣融入標題生成器中，SCAPE在生成過程中反映了用戶的風格-內容偏好。對真實世界數據集PENS進行的大量實驗證明了SCAPE相對於基準方法的優越性。

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.