A News Recommendation Framework Utilizing GPT-Based Information Extension and Similarity-Based Augmentation

Personalized news recommendation has become an essential technology for online news services. For effective personalized news recommendation, it is ideal to utilize various information, such as the title and body text. However, some news services do not retain rich information such as the body text, and only titles may be available. In this paper, we propose a news recommendation framework that utilizes data augmentation with ChatGPT. By inputting our prompt and news title into ChatGPT, we extend the information in news articles to supplement the news content feature. In particular, we focus on two directions of title extension: (1) user direction and (2) content direction. In the proposed framework, ChatGPT infers and outputs (1) the target audience of the news article in the user direction, and (2) the categories of the news article in the content direction. In addition, to further enrich the extended information, we introduce a title-similarity-based augmentation module. Evaluation experiments on a real-world news service dataset show that the proposed framework outperforms conventional methods by up to 1.65% in AUC (area under the ROC curve). These findings highlight the importance of extending the content features of news articles from their titles and utilizing them for recommendation through various prompting strategies. Furthermore, experiments across multiple datasets confirmed that the title-similarity-based augmentation module works well in some cases, but does not always select articles that contribute to recommendations when titles contain little information or are excessively short. We have provided our code and GPT-generated data to enable other researchers to reproduce our findings. 1