DSDRec: Next POI recommendation using deep semantic extraction and diffusion model

Semantics play a crucial role in many AI tasks, yet the lack of high-quality textual data in LBSNs hampers deep semantic feature learning. Sparse user check-in records, characterized by significant spatial or temporal intervals (cut points), create challenges in understanding users' real mobile preferences. However, prior studies often rely on shallow semantic signals from independent textual properties and overlook these cut points when modeling spatio-temporal context. This work proposes a recommender model named DSDRec for the next POI recommendation. To alleviate the shortage of high-quality textual data in LBSNs, we take prompt engineering to freely obtain rich semantic texts (a.k.a., prompt sentences) from the discrete trajectory sequences and then use a pre-trained language model to acquire high-quality deep semantic features based on the functional prompt sentences. To mitigate the risk caused by the cut points, we propose an effective method, Spatio-Temporal Area Encoding, to depict the global spatial and temporal relationship of check-ins in trajectory. Besides, to study the potential of diffusion models in the next POI recommendation, we stacked an improved diffusion model to assist the recommender in learning more abstract interaction information. Comprehensive experiments conducted on two real-world datasets validate the competitive performance of DSDRec over previous SOTA approaches.