WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that an augmented big data analytics model is proposed for the development of an innovative intelligent personalized travel recommendation system. WiMi incorporated user preferences, dynamic environments, desired activities, lifestyle experiences, and real-world problems (e.g., costs and distances) in order to identify and recommend the most appropriate set of destinations for travel. Such a system would be a dramatic improvement over the recommendation systems used in existing commercial systems. Existing systems focus primarily on tourist destinations offered in travel packages and fail to meet user-centered and context-driven requirements. In addition, it is difficult to obtain comprehensive and rich information about any travel destination from a single data source.
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WiMi developed a prototype of the system using an enhanced big data analytics model that takes into account five broad categories of data types, namely images, reviews, climate, social media and location. Optimized and personalized travel recommendations are achieved by leveraging destination-related information such as images of natural environments, reviews of various travel activities, climate data based on the history of weather reports, social media content of recent events and global news, as well as location information with geospatial distance measurements and user-centered travel constraints. The integration of these data sources provides the ability to gain a more comprehensive and accurate understanding of travel destinations.
The model uses intelligent analysis and state-of-the-art technologies to implement an optimized travel recommendation system. It is implemented based on key technologies such as deep learning, natural language processing(NLP), data mining and machine learning, social media analysis and geographic information system(GIS).
Deep Learning: WiMi’s augmented big data analytics-based intelligence model uses deep learning techniques to analyze and process image and review data. By training neural network models, it is possible to extract information about the natural environment of a destination from images, as well as the quality of tourist activities and user experience from reviews.
NLP: Natural Language Processing is applied to model review data for sentiment analysis and personalization. This enables the model to understand users’ attitudes and preferences towards tourism activities for better personalized recommendations.
Data Mining and Machine Learning: The model uses data mining and machine learning algorithms to analyze climate data to understand climate trends and suitable times for travel at destinations. These algorithms can also predict future weather conditions based on historical data for travelers.
Social media analysis: Social media data is analyzed through algorithms to understand the impact of recent events on travel destinations. This enables the model to provide timely information related to travel safety and hot events, helping travelers make informed decisions.
GIS: The management and analysis of geospatial data through the use of GIS technology. Taking into account geospatial distances and user-specific travel constraints, we are able to provide travelers with destination recommendations that are more in line with their needs and preferences.
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This travel recommendation system provides travelers with more accurate and personalized advice. By synthesizing various data sources, we are able to better understand the natural environment, tourism activities, climate trends, social media dynamics and global news of a destination. This enables travelers to consider all aspects of the decision-making process and make informed travel decisions based on their preferences and practical issues. The development of this travel recommendation system is based on advanced technology and intelligent analysis that offer several key benefits:
Personalized recommendations: Through in-depth analysis of user preferences and needs, the system is able to recommend the most suitable destinations for each traveler based on their unique interests and preferences. Personalized recommendations provide a more customized experience, enabling travelers to achieve greater satisfaction and enjoy the fun of travel.
Context-driven: The system not only considers the user’s personal preferences, but also takes dynamic environmental factors into account. For example, based on current climate conditions and recent social media developments, the system is able to provide travelers with more accurate destination recommendations. Such context-driven recommendations make travel decisions more relevant, increasing their reliability and usefulness.
Multi-source data integration: The system utilizes a variety of data sources, including images, reviews, climate, social media and location information. By integrating these data, the system is able to provide more comprehensive and multidimensional information about the destination, providing travelers with deeper understanding and decision support. This multi-source data integration approach greatly improves the information quality and reliability of the recommendation system.
Real-time updates: By monitoring and analyzing changes in data sources in real time, the system is able to provide timely feedback on the latest destination dynamics and information. This enables travelers to access the latest travel information and make decisions based on the latest situation. The real-time update feature improves the flexibility and adaptability of the system, enabling it to respond to the ever-changing tourism market and user needs.
User participation: The system also provides a user participation mechanism where travelers can evaluate and provide feedback on the recommendation results. Such a user participation mechanism helps to further optimize the recommendation algorithm and improve the accuracy and user satisfaction of the system.
WiMi’s travel recommendation system based on augmented big data analytical intelligence models provides travelers with more reliable and personalized suggestions. By utilizing advanced technologies such as deep learning, natural language processing, data mining, social media analysis and GIS, the system is able to synthesize a variety of data sources to provide travelers with the most suitable destination recommendations, taking into account user preferences, dynamic environments and practical issues. Such a system will drive the travel industry in the direction of greater intelligence and personalization, enhancing travelers’ experience and satisfaction, as well as bringing more business opportunities and competitive advantages to travel industry participants.
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