Abstract
The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.
Highlights
People’s material life has been satisfied with the progress and development of society
The purpose of this exploration is to provide crucial technical support for solving the challenges and problems encountered in the application of tourism personalized Location based services (LBS) in reality
The above figures reveal that the recommended hit rate of context-based personalized recommendation model (CPRM) and kNN algorithms shows an upward trend, indicating that the hit rate of the two algorithms increases with the increase of the number of recommended items
Summary
People’s material life has been satisfied with the progress and development of society. The overall rhythm of life and work becomes increasingly faster, followed by the pressure from all aspects (Chen, 2019). Excessive social pressure will seriously affect people’s mental health and work efficiency. People will be eager to meet the needs of spiritual life after work (Bussing et al, 2018). People often spend a lot of time looking for information of tourist attractions they are interested in in in various search engines. Mobile positioning technology (MPT), global positioning system (GPS), geographic information system (GIS) and Internet technology (IT) have been popularized all over the world (Yuan et al, 2020). It is mainly use to obtain user’s location information through a mobile communication network or GPS, providing users with location-related information services
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