# A Tour Recommendation System Considering Implicit and Dynamic Information
## 2 Literature Review
This approach employs a Graph Neural Network (GNN) to construct a user-POI interaction graph utilizing user rating data and a user social graph derived from social network connections. It then applies a spectral cluster-based algorithm to derive latent vectors for POIs in the location space and generates a recommendation list comprising the top-k POIs with the highest scores. Ref. [24] proposed a Graph Self-Supervised Behavior Pattern Learning (GSBPL) model for subsequent POI recommendations. The model introduces two graph data augmentation techniques to create augmented trajectory graphs, capturing implicit behavioral patterns. Ref. [25] introduced SSTGL, a novel method that integrates self-supervised learning with GNN-based POI recommendations. SSTGL utilizes spatiotemporal-aware strategies during data augmentation and pre-text tasks to enhance supervision by incorporating spatiotemporal prior knowledge. By merging self-supervised learning objectives with recommendation goals, SSTGL significantly boosts the performance of GNN-based POI recommendations. Ref. [26] presented novel Bi-level Graph Structure Learning (BiGSL) for the next-POI recommendation. BiGSL first learns a hierarchical graph structure to capture the fine-to-coarse connectivity between POIs and prototypes and then uses a pairwise learning module to infer relationships between POI pairs and prototype pairs dynamically. Based on the learned bi-level graphs, the model employs a multi-relational graph network that considers both POI- and prototype-level neighbors, resulting in improved POI representations. [PERSON]. [27] introduced a POI recommendation model for occasional groups called PROG-HGNN, which utilizes Hybrid Graph Neural Networks. PROG-HGNN first generates a representation of the occasional group using the Node Influence Indicator (INF) and Graph Attention Networks (GATs). It then learns POI representations reflecting members' interaction and transfer preferences through Signed Bipartite Graph Neural Networks (SBGNNs) and Session-based Graph Neural Networks (SRGNNs), respectively. Finally, PROG-HGNN recommends potential POIs based on the group's representation and the learned POI representations. Reinforcement learning (RL) has also been gaining traction in POI recommendation systems, as it allows for adaptive and dynamic decision-making that reflects users' evolving preferences. Unlike traditional methods, RL-based approaches can continuously learn from user interactions and optimize recommendations over time, leading to more personalized and efficient recommendations. [PERSON]. [28] introduced a top-N interactive recommender system using deep reinforcement learning. The recommendation process is framed as a Markov decision process (MDP), with interactions between the agent (recommender) and environment (user) modeled by a Recurrent Neural Network (RNN). Reinforcement learning is utilized to optimize the model, maximizing long-term recommendation accuracy. [PERSON]. [29] introduced the GRM-RTrip framework (Graph-based Representation Method for Reinforce Trip Recommendation). This framework learns POI representations from incoming and outgoing views to derive asymmetric POI-POI transition probabilities using POI-POI graph networks. It then integrates these representations into a user-POI graph network to assess user preferences. Ultimately, the authors model personalized trip recommendations as an MDP and employ a reinforcement learning algorithm to generate trips that maximize user travel experience. [PERSON]. [30] introduced MORL-Trip (Multi-Objective Reinforcement Learning for Trip Recommendation), which models personalized trip recommendations as an MDP within the Actor-Critic framework. It enhances state representation by incorporating sequential, geographic, and order information to capture user context from real-time locations. Additionally, MORL-Trip improves the Critic component by implementing a composite reward function that prioritizes three key objectives: accuracy, popularity, and diversity.