Making News Familiar: News Recommendation from Daily Scenery

Although news recommendations tailored to users’ browsing habits are common in news distribution services, issues must be addressed regarding broadening the range of users’ interests. This study focuses on user input images as a means that has not yet been fully investigated to provide new insights to users. Specifically, we assume the input of familiar images of the user’s daily scenery and verify whether the system can arouse interest in topics of which the user was unaware by recommending news articles related to the objects in the images. The implemented system extracts object names from the input images using a vision & language model and displays the search results of news articles. Through user experiments, the implemented system could recommend news articles that satisfied the requirements of serendipity (relevance, novelty, and unexpectedness in this study) at an average rate of 0.12, suggesting that news recommendation using user input images is useful.