Time highlighted multi-interest network for session-based news recommendation

Recommending news articles to anonymous users is a common yet challenging task. In recent years, several studies have focused on personalized recommendation based on user multi-interests in anonymous session scenarios. However, these studies neglect to utilize important temporal information within sessions when modeling user multi-interests, such as user browsing duration and news publication time. To address the issue, we propose a time highlighted multi-interest network for session-based news recommendation (TMN-SNR), aiming to explore users’ interests in various aspects of news and their degrees of interest by incorporating temporal information from news sessions, thereby comprehensively and precisely portraying user multi-interests. Specifically, we integrate the diverse content of news and news timeliness, considering the varying durations of users’ news browsing and news publication times, to achieve a comprehensive representation of user multiple interests. We also conduct comparative analyses of the impact of different multi-interest recommendation strategies on news recommendation performance. Furthermore, we propose a negative sampling method that integrates news publication time proximity and content similarity to enhance the representativeness of negative samples and improve model training performance. Experiments based on three real-world datasets and comparisons with baseline model performances prove that our method exhibits higher accuracy.