Improving Prompt-based News Recommendation with Individual Template and Customized Answer
Year: 2024
Type: conference-paper
Abstract: Prompt learning plays a key role in aligning the task of news recommendation (NR) with the Pre-trained Language Models (PLMs). However, current prompt-based NR methods utilize fixed templates and answer words, ignoring the personalization of user's demand and the diversity between news topics. To this end, we propose an Automatic Prompt based NR (AutoPNR) scheme, which automatically generates individual templates for users according to their potential interests, and customized answer words w.r.t. the topics of candidate news. Concretely, such an individual template utilizes several specific tokens to encode a user's interest extracted from her/his reading history, while a pair of customized answer words are retrieved from a large vocabulary (often existing alongside PLMs) based on the topic of candidate news. Through extensive experiments on the real-world datasets, we show that our AutoPNR works well with different PLMs, and considerably outperforms state-of-the-art NR techniques.
Authors: Yijiang Li, Jun Wu
Institutions: Beijing Jiaotong University, Open University of China
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403577470