Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.

Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms

Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Through experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect.

Dynamic Modeling and Analysis of Multidimensional Hybrid Recommendation Algorithm in Tourism Itinerary Planning under the Background of Big Data

Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists’ tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction.

Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

The rapid development of the tourism industry and the Internet era has led to an increasingly severe problem of travel information overload, and travel recommendation methods are essential to solving the information overload problem. Traditional recommendation algorithms only target common travel scenarios during the daytime, combining the ratings and necessary attributes between users and items to calculate similarity for a recommendation. Still, the research on night travel recommendations is one of the few scenarios that needs to be explored urgently. This paper, based on histogram equalization, achieves better experimental results on image enhancement, combines convolutional neural network technology with night image and text comment feature extraction technology, and evaluates the resulting error with mean absolute error (MAE). This paper presents the first night travel recommendation system. It compares it with the traditional collaborative filtering method, and the model proposed in this paper can effectively reduce the prediction error.