Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Abstract

Abstract With the rapid development of China’s economy and the increase in tourism consumption, the number of people in traveling in domestic tourism has increased rapidly each year, and more travelers choose privately customized travel routes, so reasonable travel route is generated based on the actual users’ needs has become a hot research spot in the current industry and academia. However, as far as practical application is concerned, the planning of travel routes is a comprehensive and complex task. Reasonable travel routes include comprehensive features such as reasonable travel cities, travel time, transportation methods, and itinerary arrangements. At present, the traditional method is basically that the customer manager can manually plan the suitable travel route for the user through collecting the user’s needs, and then modify and adjust by communicating with the customer. The problem that this brings is that the customer manager needs to compare information such as users’ needs, travel price, travel time, travel transportation, and scenic spot arrangements when planning numerous travel routes. Obviously, the traditional methods have significant disadvantages such as low efficiency and long time-consuming. Bring a great burden to the staff and it is incompatible with the development of the current industry. In order to solve the above problems, we put the historical travel routes collected as data sets in the paper, and a travel route recommendation and generation algorithm based on LDA and collaborative filtering is designed. Reasonable city recommendation list and playing time are the basis and focus of route planning. The paper is based on the many shortcomings in the traditional travel route planning method, and takes the city’s recommendation and time planning as the main focuses on work. In this work, different recommendation algorithms were designed, including a recommendation algorithm based on Latent Dirichlet Allocation (LDA) and collaborative filtering. By analyzing the performance of the recommendation algorithm on the data sets, the recommendation algorithm is improved and optimized. The LDA algorithm based on KDE (Kernel Density Estimation) and classification, the collaborative filtering algorithm based on KDE and classification. The final experimental results show that the optimal city list and travel time generated by the recommended algorithm are more reasonable and satisfy the actual use of the user.