Research hotspots and trend analysis of text generation for explainable recommendation based on CiteSpace

Bibliometric analysis was applied to retrieve the international core journals on the subject of text generation for an explainable recommendation from 2000 to 2022. CiteSpace, a visualization-based analysis tool, was used to analyze the research status and recent development of text generation for explainable recommendations by mapping the co-occurrence of high-frequency keywords and citation bursts. The results show that in the past 22 years, the number of international articles on text generation for an explainable recommendation has been on the rise with more publications especially compared with that in China, calling for further exchange and collaboration among scholars and institutions over the world to facilitate the research progress. “Personalized” recommendation has been exerting its influence in text generation for explainable recommendations, which effectively gain the trust of users, and increase the persuasiveness and satisfaction of the recommendation system by providing recommendations with explainable texts. Text processing in Deep Learning has now been widely used for explainable recommendations and will throw its weight further in the future.