The problem of Point-Of-Interest (POI) recommendation, based on the user’s historical check-in records, determines whether a user checks in at specific POI. However, the user-POI data have a long-tail distribution phenomenon. To mitigate the sparsity of check-in data, it is a good idea to exploit the sufficient attributes of POI and recommend POIs in both geography wise and category wise. Generally, this problem can be treated as two specific tasks with feature combination, ignoring cross-task dependencies and feature disentanglement. To address the aforementioned problems, this paper proposes a novel joint framework named InteractPOI, enabling two-stage interaction bewteen geography-wise and category-wise POI recommendations. Specifically, this paper comprehensively considers the sequence effect and the neighbor effect both from geography wise and category wise. For the first-stage interaction, we design a disentangled graph embedding model to distinguish different influencing factors from geography wise and category wise. For the second-stage interaction, we integrate a gating mechanism for feature fusion with a complementary algorithm for interactive optimization. Extensive experiments on two datasets demonstrate the superiority of the proposed model.

A thorough understanding of the information dissemination process in Online Social Networks (OSNs) is crucial for enhancing user behavior analysis. While recent studies usually focus on assessing the emotional intensity of individual tweets or predicting their popularity, they frequently overlook how these tweets impact sentiment trends over time. The explosive and inflammatory nature of deliberate tweets is difficult to perceive by prediction or sentiment methods. To address this gap, we propose the multi-view Information Propagation State Awareness (IPSA) model, which aims to simultaneously assess and forecast both the popularity and sentiment strength throughout the information propagation process. Our approach begins by segmenting the information propagation into distinct time windows. Within each window, the IPSA model designs an encoder module to capture multi-view influence factors from structure, content, and time series data. Specifically, the encoder module includes a graph encoder layer based on graph attention networks to represent the backbone propagation structure formed by key nodes in the reply chain. Meanwhile, the sentiment encoder layer, utilizing an attention mechanism, extracts emotional factors present in the reply chain. Besides, we introduce a residual information prediction method that enhances the model’s precision in perceiving both popularity and sentiment intensity for each time window. Our comparative experiments, conducted on two datasets and benchmarked against State-of-the-Art (SOTA) methods, demonstrate that the IPSA model excels in predicting popularity and assessing future emotional trends in information propagation.