# A Tour Recommendation System Considering Implicit and Dynamic Information
## 2 Literature Review
[15] introduced a next-POI recommendation strategy, DSPR, that concurrently evaluates user preferences and real-time demand. This strategy uses implicit vectors to represent users' immediate demands and preferences, applying an attention-based Recurrent Neural Network to automatically discern the significance of various demand aspects and preferences, thereby capturing the complex interactions among POIs. [PERSON]. [16] introduced a model that enhances POI recommendations by considering the strength of user relationships, which is determined by analyzing users' check-in behaviors. This model incorporates users' social connections into a spatiotemporal framework for POI recommendations. [PERSON]. [17] addressed the Tourist Trip Design Problem with Crowd Dynamics (TTDP-CD), aiming to devise dynamic and personalized tour routes. Utilizing mobile tracking data, the approach quantifies crowd dynamics to minimize perceived crowding, maximize destination value, and reduce total travel distance. [PERSON]. [18] presented a framework for generating recommendations for a sequence of landmarks to visit, including stay durations (LST), based on user-generated content from photo-sharing social media platforms. The empirical results demonstrated the superior performance of the proposed LST-LSTM predictor over the baseline L-LSTM predictor, which does not account for stay time, across all evaluated metrics. Ref. [19] developed a deep-learning-based POI recommendation model leveraging social networks and big data. The model processes input data collected via intelligent sensors to minimize computational demands. It employs a symmetric matrix decomposition method for static feature extraction, capturing geographical locations and POI categories in LBSN. Finally, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) adaptively selects relevant check-in activities from user history to identify changing preferences. Ref. [20] proposed a new framework called Multi-view Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation (MCGRec). This framework first uses a personalized PageRank (PPR) sampling method with super nodes to convert graph data into a grid-like feature matrix. Next, it applies a CNN to extract representations of POIs from this matrix. Finally, MCGRec introduces an innovative method for estimating user preferences by incorporating geographical and temporal factors. [PERSON]. [21] introduced DSDRec, a recommender model for next-POI recommendations. To address the lack of quality textual data in LBSNs, they used prompt engineering to generate rich semantic texts from trajectory sequences and employed a pre-trained language model for deep semantic features. To manage cut-point risks, they developed Spatiotemporal Area Encoding to capture global spatial and temporal relationships of check-ins. [PERSON]. [22] proposed the Spatiotemporal Consistency Federated Learning (SCFL) framework to enhance POI recommendation models' personalized performance in federated learning by leveraging trajectory spatiotemporal consistency. They developed the SCA trajectory optimization module for clients to uncover deeper behavioral patterns from sparse trajectories. Additionally, a hierarchical aggregation strategy using intermediate entities called Edges, which aggregates similar users, improving the model's ability to learn shared information. Graph Neural Networks (GNNs) have emerged as a powerful tool in recommendation systems, including POI recommendations, due to their ability to capture complex relationships between users, locations, and contextual data. GNNs can model interactions within heterogeneous networks, such as the relationships between users and POIs, as well as dependencies across time and geography. Ref. [23] developed a POI recommendation strategy that utilizes Location-Based Social Networks (LBSNs) and multi-graph fusion.