Location Identification and Personalized Recommendation of Tourist Attractions Based on Image Processing

Zhang, Qian; Liu, Yi; Liu, Lei; Lü, Shuang; Feng, Yuxue; Xiao, Yu

doi:10.18280/ts.380121

Cited by 12 publications

(15 citation statements)

References 25 publications

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“…In recent years, the tourism industry has developed rapidly, and the number of tourist attractions and tourism information on the Internet has become more and more numerous [1]. The process for users to decide on attractions is complicated and inefficient.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

Wang¹

2022

Journal of Sensors

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In order to provide tourists with better tourism services, a system method of personal information recommendation platform based on deep learning tourism is proposed. The system includes noise reduction autoencoder, feature extraction module, data preprocessing module, recommendation calculation module, expert evaluation module, recommendation result output module, customer feedback module, and storage module. The personal information recommendation platform system based on deep learning tourism of the present invention enables tourists to obtain tourism information conveniently and quickly through scientific information organization and presentation form and helps tourists to better arrange tourism plans and form tourism decisions. By effectively aggregating multiple neighborhoods of nodes, embedding high-order collaboration information into the node embedding vector, obtaining the potential preferences of users, solving the problems of user data sparse and cold start, and finally through experimental analysis, a research method is proposed. It is used to build the model of tourist attraction recommendation system. Experimental results show that the proposed method for cold-start user recommendation has the best performance in terms of accuracy, recall, and normalized loss cumulative gain, and it is 17.9% higher than BPR in recall rate Recall@5 and 11.8% higher in accuracy rate. It is proved that the system has a significant impact on the diversity and novelty of tourist attraction recommendation.

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Section: Introductionmentioning

confidence: 99%

Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

Wang¹

2022

Journal of Sensors

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“…People not only pursue general group sightseeing but also the demand for personalized tourism has increased greatly. Due to the diversity of tourist groups, under the restriction of funds and time, how to choose the scenic spots that tourists are interested in is often a problem that tourists or tourism marketing departments both pay attention to and need to solve urgently [1]. Usually, before traveling, tourists will search the tourist information through the Internet to get the detailed information of the scenic spots, but the traditional tourist information service has been unable to meet the needs of the public [2].…”

Section: Introductionmentioning

confidence: 99%

Tourism Destination Recommendation Based on Association Rule Algorithm

Lou

2022

Mobile Information Systems

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With the development of information technology and the arrival of intelligent era, the tourism industry has also changed from traditional information service to intelligence. In this study, the recommendation system is studied. By comparing the advantages and disadvantages of various recommendation methods, the association rule algorithm is selected as the recommendation method in this study. Then, the user’s operation behavior is obtained and the personalized tourism destination recommendation model is established. This model is based on the user’s access operation behavior data, and it introduces the user’s interest degree as a parameter to measure the operation behavior data, forming the basic data of data mining. The popularity of tourist routes and the change weight of longitudinal interest characteristics among users are introduced into the algorithm. The change weight of user’s lateral interest characteristics is added to the prediction of target users’ future interest. Compared with the classified recommendation method, users can easily locate the information of scenic spots they are interested in under the guidance of scenic spot recommendation. The results further verify that the research of this model has certain practical significance and practical value.

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“…However, a MapReduce job consists of several distinct phases, each of which will have di erent computational resource requirements. At the same time, there are dependencies between di erent stages [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Construction of Garden Landscape Design System Based on Multimodal Intelligent Computing and Deep Neural Network

Liu

2022

Computational Intelligence and Neuroscience

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The problem of module discrimination and identification in the field of landscape design is the focus of researchers. Based on multimodal intelligent computing, this paper constructs a landscape design system based on deep neural network. The article first uses a deep neural network to train multimodal garden landscape images, and then performs pooling and convolution operations on garden landscape images on the multimodal training model of convergence speed on the edge and solve the problem of low model accuracy. In the simulation process, the neural network module of MATLAB software is used to extract the spatiotemporal features of the dynamic garden landscape image from the three directions of the bottom block of the garden to achieve feature complementarity. This method only uses 15% of the features of the original feature set. The complexity of the recognition system also reduces the recognition error rate. The experimental results show that by adopting the design of feature series fusion, maximum value fusion, and multiplicative fusion in the score layer, the feature series fusion achieves a high accuracy rate under the multiplicative fusion of the three modalities, reaching 77.1%, and the test error is within 0.118, which effectively improves the multimodal characteristics of the integrated landscape and makes the modeling results more accurate.

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Location Identification and Personalized Recommendation of Tourist Attractions Based on Image Processing

Cited by 12 publications

References 25 publications

Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

Implementation of Personalized Information Recommendation Platform System Based on Deep Learning Tourism

Tourism Destination Recommendation Based on Association Rule Algorithm

Construction of Garden Landscape Design System Based on Multimodal Intelligent Computing and Deep Neural Network

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