Category-Aware Next Event Prediction Based on Temporal Influence

Accurate modeling of influence relationships between events is critical to achieving effective sequential recommendation. Representing event sequences as a Hawkes process is a widely adopted approach; however, it is suboptimal in practical applications because it overlooks the contextual information of events and assumes that all historical events exert a positive influence on current events. To address these limitations, we propose a category-aware next event prediction method. This method computes both the event-level and category-level influences of all historical events associated with the target user on candidate events and recommends the Top-k ones with the highest predicted event intensities. Specifically, it captures the temporal influence between events from the perspectives of event features and the features of the categories to which the events belong. Using embedding learning, the method derives feature vectors for both events and their corresponding categories, as well as their mutual influence relationships, thereby allowing more accurate prediction of the next event. Extensive experiments on real-world datasets demonstrate that the proposed method achieves superior performance across multiple evaluation metrics compared to conventional methods.