## 2 Related Work
### Multi-Context Information-Based Approaches  
Multi-context information-based approaches [6; 7; 8; 9] aim to learn POI representations and user preferences from multi-context information, such as geographical information and temporal information. [PERSON] et al. [10] incorporated Temporal Recency (TR) for visit timings and Spatial Proximity for location-based recommendations. They utilize a modified Long Short-term Memory (LSTM) model to capture temporal information and geographical proximity through orthogonal mapping to represent spatial information. [PERSON] et al. [11] introduce a Spatio-Temporal Intention Learning Self-Attention Network (STILSAN) that includes a preference-intention module to capture long-term preferences and revisit intentions, and a spatial encoder module to learn POI spatial features through spatial clustering and proximity analysis. [PERSON] et al. [12] propose a novel method, Point-of-Interest Recommendation based on Bidirectional Self-Attention Mechanism by Fusing Spatio-Temporal Preference (BSA-ST-Rec), which leverages a bidirectional self-attention mechanism to integrate spatio-temporal preferences for enhanced POI recommendations. [PERSON] et al. [13] developed the Spatio-Temporal context Aggregated Hierarchical Transformer (STAHT), which employs stacked hierarchical encoders to recursively encode the spatio-temporal context and identify subsequences of varying granularities, capturing the dynamic preferences from user chc-ins. [PERSON] et al. [14] propose a spatial-temporal and text representation learning approach that utilizes the Transformer architecture to extract long-term dependencies in check-in sequences, effectively capturing both spatial and temporal dynamics. [PERSON] et al. [15] introduce the Global Spatio-Temporal Aware Graph Neural Network (GSTAGNN), a novel model designed to capture and leverage global spatio-temporal relationships by examining user trajectories from a comprehensive perspective, including both spatial and temporal dimensions. [PERSON] et al. [16] propose the Social- and Spatial-Temporal-Aware Next Point-of-Interest (SSTANPOI) method, which employs two feature encoders based on the self-attention mechanism and gated recurrent units to hierarchically model users' check-in sequences, considering both social and spatial-temporal contexts. [PERSON] et al. [17] propose a novel framework named Mining Preferences from Geographical and Interactive Correlations (MPGI) that develops a specialized layer to capture geographical distances and interactive correlations between all POI pairs. This approach aims to leverage the spatial and relational aspects of POIs to improve the accuracy of recommendations. [PERSON] et al. [6] developed the Spatio-Temporal Heterogeneous Information Network (STORE) model, which simultaneously extracts geographical and temporal effects along with other contextual features, such as POI types and users' social relations. By integrating these diverse features, STORE provides a comprehensive representation of the user-POI interaction landscape. [PERSON] et al. [18] propose a recommendation framework that transforms heterogeneous nodes, including users and POIs, into a unified representation space. This addresses the issues of noise and node similarity, allowing the framework to better differentiate similar behavior nodes, resulting in more accurate recommendations. [PERSON] et al. [7] developed a unified Spatio-Temporal Neural Network (STNN) framework, which leverages users' check-in records and social ties to recommend personalized POIs. The proposed method jointly models user-POI relations, sequential patterns, geographical influence, and social ties in a heterogeneous graph to learn user and POI representations. [PERSON] et al. [19] propose a multi-task and multi-head attention transformer-based model that not only recommends the next POIs to target users but also predicts queuing times by considering user mobility behaviors.