Place-Transition-Aware Tourism Recommendation Framework Integrating Dynamic Profiling and Path Behavior Reasoning

Tourism recommendation must capture evolving preferences and dependencies among successive activities. This study proposes a place-transition-aware tourism recommendation framework integrating dual-side dynamic profiling, Event Evolutionary Graph-based path behavior reasoning, and XGBoost re-ranking. Using travel notes, attraction reviews, and attribute data from Wuhan, China, the framework models dynamic user preferences and attraction states. Sequential visit transitions and co-visitation associations among tourism places are organized in Neo4j. Behavioral continuity is defined as the explicit sequential and co-visitation dependencies linking users’ previous attraction visits to subsequent candidate attractions. In this framework, a place transition denotes a topological behavioral relation between place-related events rather than a geographic route, road-network path, or coordinate-based distance relationship. A time-aware relation-weighted path score is combined with profile features in a 35-dimensional vector for candidate ranking. The framework achieved the highest Top-5 performance among the evaluated models, with an F1@5 of 0.2714. Its F1@5 was significantly higher than those of GRU4Rec, SASRec, and LightGCN, but not significantly different from BERT4Rec, which obtained slightly higher Top-10 results. The path score achieved a ROC–AUC of 0.7811 and increased Top-5 path coverage from 0.5997 to 0.6475. These findings indicate that dynamic profiling supports relevance discrimination, whereas path behavior reasoning improves behavioral coherence and interpretability. The conclusions are limited to the present moderate-scale offline dataset.