ENTERPRISE AI ANALYSIS
A Multidimensional Parameter Optimization Model for Internet-Famous Scenic Spot Popularity Prediction
To solve the problems of multi-source heterogeneous data, complex feature structure, inadequate model fitting, and poor prediction accuracy of internet famous sceneries when predicting the popularity of cultural tourism attractions, a paper is put forward to propose a parameter space multiple dimensions optimization algorithm to predict the popularity of internet famous scenic spots. First, missing values imputation, feature selection, and sample synthesis of the initial check-in data are carried out. The four machine learning models called the XGBoost, AdaBoost, LightGBM, and CatBoost are used as base learners in a stacked learning architecture. The base models are merged with a Stacking method in order to create a powerful predictive system. Lastly, Bayesian optimization algorithm is used to hyper-tune each of the base models, to obtain optimal global performance and to improve the predictive accuracy. Cross-validation experiments on real-world check-in data confirm that the offered approach produces more promising results as compared to the classical (e.g., AdaBoost, CatBoost, KNN, XGBoost) models based on the measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean square percentage error (MSPE), and R 2. The findings provide evidence of the excellent fitting ability and forecasting power of the suggested model, which means that it has a significant beneficial effect in the matter of tourism popularity forecasting.
Executive Impact
This research introduces a novel multi-dimensional parameter optimization model to predict the popularity of internet-famous scenic spots, addressing challenges like heterogeneous data and complex feature interactions. The model integrates XGBoost, AdaBoost, LightGBM, and CatBoost within a stacked learning framework, optimized using Bayesian methods. Experimental results show superior performance against traditional models, making it a valuable tool for tourism planners and destination managers to forecast and manage viral popularity trends effectively.
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MAE Reduction
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R² Score
Advanced
Prediction Accuracy
Deep Analysis & Enterprise Applications
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The proposed methodology blends four state-of-the-art ensemble learning algorithms—XGBoost, AdaBoost, LightGBM, and CatBoost—as base learners in a stacked learning architecture. Bayesian optimization is then employed to fine-tune the hyperparameters, achieving optimal global performance and predictive accuracy. Data preprocessing steps include missing value imputation, feature selection, sample synthesis, and stratified train-test splitting.
Cross-validation experiments on real-world check-in data demonstrated that the proposed approach produces more promising results compared to classical models. The model achieved a MAE of 0.1045 and an R² of 0.8234, indicating excellent fitting ability and forecasting power. This superior performance is attributed to the multi-dimensional parameter space optimization and the robust ensemble learning approach.
The model offers significant benefits for tourism stakeholders, destination managers, local government authorities, and tourism companies. It provides an intelligent tool for monitoring and trend analysis, enabling better planning for social media campaigns, mitigating risks associated with sudden drops in popularity, and developing effective marketing strategies for internet-famous scenic spots. The interpretability of the model also allows stakeholders to understand the reasoning behind predictions.
0.1045
Lowest MAE achieved by Proposed Model
Enterprise Process Flow
Data Input (Check-in Data)
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Preprocessing (Missing Values, Feature Selection, Synthesis)
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Base Learners (XGBoost, AdaBoost, LightGBM, CatBoost)
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Stacking & Bayesian Optimization
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Popularity Prediction Output
| Model | MAE | MSE | RMSE | MAPE | MSPE | R² |
|---|---|---|---|---|---|---|
| AdaBoost | 0.1281 | 0.0671 | 0.1983 | 0.5289 | 0.7968 | 0.7890 |
| CatBoost | 0.1877 | 0.0735 | 0.2712 | 0.5011 | 0.8091 | 0.8034 |
| KNN | 0.1152 | 0.0395 | 0.1987 | 0.4538 | 0.8935 | 0.7819 |
| XGBoost | 0.1816 | 0.0664 | 0.2577 | 0.5501 | 0.7923 | 0.7769 |
| Proposed Model | 0.1045 | 0.0543 | 0.1829 | 0.4489 | 0.8013 | 0.8234 |
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Impact on Tourism Management
The rapid rise and fall of 'internet celebrity attractions' pose significant challenges for tourism planners and destination managers. This model provides a solution by enabling accurate, real-time popularity forecasting. For example, a local government authority can use this tool to anticipate a surge in visitors to a newly viral spot like the 'Zibo Barbecue' phenomenon, allowing them to allocate resources effectively, manage crowds, and plan for sustainable long-term engagement. Conversely, it can also predict potential drops, enabling proactive interventions to maintain interest.
The ability to predict both surges and declines allows for proactive resource allocation and strategic marketing, transforming reactive management into predictive foresight.
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Our Implementation Roadmap
A clear path to integrating predictive AI for tourism popularity into your operations.
Phase 1: Data Integration & Model Customization
We gather and integrate your specific tourism data (check-in, social media, geo-spatial). Our engineers customize the multi-dimensional parameter optimization model to your unique context, ensuring optimal feature engineering and ensemble configuration.
Phase 2: Training, Validation & Hyperparameter Tuning
The model is rigorously trained on your historical data. We perform extensive cross-validation and use Bayesian optimization to fine-tune hyperparameters, ensuring peak predictive accuracy and robust performance across various scenarios.
Phase 3: Deployment & Stakeholder Training
Deployment of the predictive system into your existing infrastructure. We provide comprehensive training to your tourism planners, marketing teams, and urban management staff on how to interpret predictions and leverage insights for strategic decision-making.
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