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

Authors: Xuejuan Wang
Journal: Journal of Sensors
 (2022)

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## Abstract

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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## Body

## 1. Introduction

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. A good tourist attraction recommendation service can recommend scenic spots that meet their interests and preferences, so as to improve the efficiency of users’ decision of scenic spots and also improve the user’s travel satisfaction. The types of attractions are easy to distinguish, and users of similar types will show very similar preferences for tourist attractions. Therefore, an intuitive idea to implement attraction recommendation is to make full use of user preferences and attractions based on the “similarity” between user groups and attractions, and the intrinsic association of attractions implements the recommendation model. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper takes advantage of the characteristics of microblog data, such as personalized expression, strong current situation, and the intelligent prediction function of machine learning, and proposes a new model based on microblog data. The scenic spot recommendation method based on blog data and machine learning realizes accurate and personalized recommendation of tourist attractions.In the planning of travel itinerary, it is compared with the elements of accommodation, transportation, and restaurants [2]. As the ultimate goal of tourists’ travel itinerary, scenic spots are undoubtedly the most important part of itinerary planning; therefore, the choice of tourist attractions will greatly affect tourists’ travel satisfaction and obtain the required scenic spot information from travel websites; at the time, tourists mainly rely on information retrieval and recommendation systems. A typical example of information retrieval is a search engine, that is, a travel website returns the attractions related to the keywords according to the keywords provided by tourists [3]. The recommendation system is a travel website that pushes relevant attractions based on the prediction of tourists’ preferences. In the face of the massive amount of scenic spot information on the website, it is often difficult for tourists or it takes a lot of time and energy to obtain the required information, which leads to the problem of information overload. Although search engines can alleviate the problem of information overload to a certain extent, tourists are required to choose appropriate keywords to describe their needs; however, some studies have found that a large number of tourists cannot clearly express their travel needs. Therefore, since tourists cannot accurately input keywords, search engines are powerless to alleviate the problem of information overload for tourists when choosing scenic spots [4].Different from search engines, the recommendation system does not require the active input of tourists; through the analysis of historical data of tourists, it realizes the mining of tourists’ preferences, so as to provide tourists with personalized scenic spot recommendation services, which not only meets the needs of tourists but also improves the tourists’ preference, and website loyalty has now become an important part of major travel websites [5]. It is a new type of machine learning and a new type of deep learning. Through scientific information organization and presentation, it allows tourists to easily and quickly obtain tourism information, helping tourists to better arrange tourism plans and form tourism decisions. The brand-new service experience brought by smart tourism can be felt during the decision-making process of tourism planning and tourism planning.


## 2. Literature Review

Kumar et al. said that with the development of technology and the performance breakthrough of computer hardware equipment, big data analysis methods have become a handy tool for researchers [6]. Based on the paper of Al-Garadi et al., the application of data mining to the tourism market is an important technological breakthrough in the field of tourism research, in which tourism route planning is an indispensable part of smart tourism research and tourism recommendation system development [7]. Relying on the vigorous development of deep learning and the Internet of Things and its supporting technologies and hardware, the mining and prediction accuracy of tourists’ interests through models has been greatly improved, which will bring great importance to the rapid development and progress of smart tourism and tourism economy; it also provides a core competitiveness for the development of local tourism. The core of the system is tourists, so the starting point of the system and algorithm design should be in line with the interests and motivations of tourists. Tourists’ satisfaction with the route planned by the system will directly affect tourists’ subjective evaluation of the tourist city attractions and thus indirectly affect the tourists. Tourists make travel plans and affect users’ stickiness to the system. In order to further optimize the efficiency of route planning, this paper proposes several optimization strategies, which greatly accelerate the speed of route finding through fast pruning. Through a large number of experiments, applying the method proposed in this paper can effectively perform scenic spot extraction and travel route planning and compare the effects of multiple optimization strategies. On the basis of taking tourists as the center, the optimal route is planned around the interest, time, budget, experience, and other factors of tourists; a personalized travel route can bring tourists the best travel experience.Kumoro and Hasanah said that at present, by studying several professional travel websites in China, it is found that the current recommendation method is mainly based on the recommendation of a single tourist attraction [5]. If tourists want to make overall planning for the travel route, there are mainly two ways; the first one is that tourists can obtain letters through social platforms such as strategies, magazines, and friend recommendations and then make travel plans according to their own time, budget, etc. The second way is to design the routes according to the most popular tourist cities, the most visited cities, and the highest star ratings, by the travel platform (usually including travel agencies and travel websites, etc.), but these routes are fixed. It is the same for all mass tourists and ordinary consumers. In the past, this kind of planning met the basic needs of mass tourists, but in today’s highly developed information, tourists are more eager to obtain a personalized travel experience. At the same time, this planning method also ignores some scenic spots with high quality, which is not conducive to the development of these scenic spots. In addition, it is impossible for travel agencies to designate exclusive travel routes for every ordinary tourist. Therefore, not all the scenic spots in the routes formulated by the travel platform are of interest to tourists. For these attractions, tourists can only passively accept them, and these methods have different drawbacks. At the same time, if tourists choose the method to plan the route by themselves, they may develop an inappropriate route due to information asymmetry, unreliable information, and other factors; therefore, there are some shortcomings in these two methods.Hu et al. said that according to the problem background and data type, the graph convolutional network in deep learning and IoT technology is used to capture useful information in user and scenic spot data, in order to expect better scenic spot recommendation effect, and provide users with intelligent recommendation services that can meet their interests and preferences [8]. The recommended comprehensive processing module removes the tourist attractions that the target user has traveled and forms a final recommendation set to recommend to the target user. At this point, the module design is completed. The test results show that, for the same target user, compared with the traditional recommendation model, tourism-based recommendation, there is some new information in the recommended results of the scenic spot recommendation model, and the recommended content is more comprehensive, and the recommendation model is better than the traditional recommendation model.
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Radio frequency identification technologyRadio frequency identification technology, also known as RFID technology, emerged in the 1990s and is an automatic identification technology. Radio frequency identification technology is based on the basic principle of radio frequency signal and its spatial combination and transmission characteristics, which can realize the automatic identification of stationary and moving objects. Radio frequency identification technology does not require mechanical contact or optical contact between the identification system and the identification target but identifies specific targets and completes the reading and writing of related data through radio signals. From the perspective of the operation mode of radio frequency identification technology, the basic components of radio frequency identification system mainly include the following three types, and tags are mainly used for communication with radio frequency antennas, which are divided into two categories: active and passive. The tags include coupling elements, chips, and built-in antennas. Each tag has a unique electronic code, which is attached to the object and identifies the target object. RFID readers can read the target object’s data stored in the tag. The reader is the information control and processing center of the RFID system, it is composed of a radio frequency module and a digital signal processing unit, the main function is to read and write the tag and read and write the data information in the tag, and it can be fixed. It can also be hand-carried. The application of radio frequency identification technology is inseparable from the spatial propagation of radio frequency signals and wireless communication connections. The function of the antenna is to realize the spatial propagation of frequency signals between the tag and the reader and establish a wireless communication connection, which is the medium between the tag and the reader. Not only that, the identification range of the RFID system is affected by the antenna design parameters, and the higher the antenna performance, the greater the identification range of the RFID technology, which requires the antenna to have superior impedance matching characteristics. The application range of RFID technology is extremely wide, and it can be used to track and manage almost all physical objects, such as electronic tickets in scenic spots, and automatic road tolls [9].
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Sensing technologySensing technology includes two parts: sensors and wireless sensor networks; first, as far as sensors are concerned, a sensor is an information detection device that can not only detect information but also transmit the detected information in electrical signals or other forms. In the Internet of Things, sensors are mainly responsible for information collection, which is a prerequisite for automatic detection and automatic control and occupies a fundamental position. The application value of sensors is significant, and with the continuous upgrading of technical means, the application fields of sensors are also expanding, becoming important equipment in industrial production, energy development, aerospace, and national defense. Secondly, as far as the wireless sensor network is concerned, the wireless sensor network is a multihop self-organizing network formed by wireless communication, which is composed of many micro sensor nodes in a specific area. As a brand-new information acquisition platform, the wireless sensor network can dynamically monitor and collect the information of the detected objects in the area and send the acquired information to each gateway node. In addition to micro sensor nodes, the constituent elements of wireless sensor networks also include receivers and transmitters, communication satellites, and task management nodes. Micro sensor node is not only the constituent unit of wireless sensor network, but also a completed sensor unit, including sensor unit, processing unit, communication unit, and power supply, and it has the advantages of rapid development and strong invulnerability. It plays an important role in the construction of smart scenic spots [10].
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Network and communication technologyThe Internet of Things technology is the product of the development of network information technology. Network and communication technology is the most basic content of the Internet of Things technology, and it is also the precondition for the emergence of the Internet of Things technology. Network and communication technology includes two parts: network technology and computer communication technology. First of all, in terms of network technology, network technology is the combination of computer technology and communication technology, which can connect computers in different areas through communication equipment and lines, so as to achieve the goals of information transmission and resource sharing. The network system is the external manifestation of network technology, including the communication network and network resources. The main equipment includes network management, network bridges, routers, switches, repeaters, and hubs. Barrier-free communication can be achieved between each node in the network system; secondly, in terms of computer communication technology, computer communication refers to the process of information exchange and transmission between devices and computers and between computers. From the perspective of the development process of communication technology, it is mainly divided into three stages: the era of analog communication, the era of digital communication, and the era of data traffic. The Internet of Things technology was born in the era of data communication, and the so-called data communication refers to the data signal sent by the information source as the carrier. The specific exchange methods are divided into three types: message exchange, circuit exchange, and packet exchange. The information sharing and transmission between computers mainly rely on network protocols, and the network protocols are not fixed and need to be selected according to the actual situation [11].
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Data mining and fusion technologyData mining technology is the fusion of artificial intelligence and database technology, also known as knowledge discovery in database, and the main function is to dig out an information resource that can meet people’s data usage needs from massive, random, and noisy data. The combination of questionnaire survey and automatic capture is used to collect user information, user ratings, and other tourism data, and stratified sampling of the data is used to generate a “smart tourism” data set containing user travel preference information. Based on this dataset, the user ratings are preprocessed, and a collaborative filtering algorithm is carried out based on user clustering to calculate the similarity between the target user and the cluster center. Combined with the travel preference information generated by the stratified sampling model, a mixed recommendation list is output. Data mining includes three stages of data preparation, data mining, and result expression, which can extract valuable information from the database. Data mining is realized through data analysis, and with the help of the analysis of massive data, valuable information can be mined from it. Data analysis methods mainly include association analysis, classification analysis, cluster analysis, anomaly analysis, evolution analysis, and specific group analysis. Each analysis method can dig out a type of valuable information; for example, with the help of correlation analysis, the relevant information in the massive information can be presented; for example, with the help of evolution analysis, the evolution process of a set of data can be displayed. Data mining technology has greatly improved the application value of data information and has very significant value in many fields such as market planning, business decision-making, and financial forecasting. Information fusion is the development trend of data mining technology, and data mining and fusion technology, one of the key technologies of the Internet of Things, is the product of the connection between data mining and information fusion. Data mining and fusion technology is based on the information resources obtained by data mining, uses existing knowledge and experience to process information in different fields, and uses this information to identify, estimate, and judge targets [12].
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Convolutional neural network modelIn deep learning, convolutional neural networks (CNN or ConvNet) are a class of deep neural networks [13]. Convolutional neural network is a shared weight architecture based on convolution kernel, and features were used to scan hidden layers. Its network structure usually consists of multiple convolutional layers and fully connected layers, and a pooling layer is usually inserted into the convolutional layers. The convolution operation can be viewed as a process in which one function performs a linear transformation on another function to map to a new value. We can think of it as a sliding window function applied to the matrix; as shown in Figure 1 below, the convolution kernel slides on the feature matrix by a specified step size, multiplies the convolution kernel matrix value and the feature matrix value in turn, and then finds and gets a full convolution. Use the convolution kernel to slide on the feature matrix to get different feature layers. The pooling layer reduces the amount of data computation by combining the outputs of a layer of neurons, the usual pooling types are max pooling or average pooling, and a smaller feature matrix is obtained through pooling and then connected to the next layer. Fully connected layer is an important part of convolutional neural network (CNN), the convolutional neural network process starts with convolution and pooling, decomposes the matrix into features, and then analyzes them independently, the result of the process will be passed into a fully connected neural network structure, and the optimal result from this structure is the final classification decision. Finally, the fully connected layer is connected to the output layer, and finally, the probability prediction values of different classifications are obtained to form an interest vector. The number of layers of the convolutional network can be increased according to requirements and accuracy.
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Activation functionFigure 1
Principle of convolutional neural network.The activation function is also an important concept in convolutional neural networks, so it is explained as a single concept [14]. Biologically, whether a neuron is activated depends on whether the signal value is greater than a certain threshold. In the artificial neural network, the activation function is to judge whether the feature strength of a certain area reaches a threshold. The activation function determines the output of the deep learning model, its accuracy, and the computational efficiency of training the model. Commonly used activation functions help to normalize the output of each neuron to a range of 1 to 0 or -1 to 1. If the trigger condition is not met, the function indicates that the convolution kernel has no features extracted in this area, or the features are very weak: common activation sigmoid function, tanh function, the popular ReLU function, leaky ReLU function in recent years, and Softmax function.


### 2.1. Tourist Attraction Recommendation Method Based on Graph Convolutional Network

This section will introduce the proposed personalized travel recommendation method based on graph convolutional network, and the overall structure includes four parts: embedding layer, information dissemination aggregation layer, information self-encoding layer, and prediction layer.(1)
Embedding layerLike the mainstream recommendation system, the latent vectorsxu and xi∈Rd of the Euclidean space are used to represent the user u and the scenic spot i, respectively, and d represents the dimension of the embedding vector, thus establishing a lookup table X as the initial value of users and attractions, which is randomly initialized through normal distribution, as shown in the following formula:
(1)Χ=xu1,xu2,⋯,xuN,xi1,xi2,⋯,xiN,whereN and M represent the number of users and attractions, respectively. In the traditional recommendation model, the initial embedding vectors of users and attractions are directly input into the interaction layer for end-to-end optimization, but users and attractions in real scenes are not isolated, and there is a certain connection between them. When the feedback records of users are sparse, simple splicing or nonlinear interaction cannot well extract the associations between users and users, users and attractions, and attractions and attractions. All users and attractions are regarded as each node in the graph, so that preference information is propagated in the user-attraction interaction graph, so that nodes not only focus on local neighbors but also obtain higher-order relationships between users and attractions through information diffusion, and contacts get effective collaborative information embedding into user and sight embedding vectors to alleviate the data sparsity problem.
(2)
PageRank-based graph convolutional network information propagation and aggregation layerIn the user-spot interaction, each user or scenic spot is not isolated but related to each other, and the user’s direct interactive scenic spot is defined as the user’s local neighbor, for example, scenic spoti1,i2,i4 in Figure 2 is the local neighbors of user u1, they reflect the user’s direct preference evidence, and the user’s potential preference is also affected by the local neighbors. Attraction i2,i4 is visited by user u2 to establish a connection, so u1 is the second-order neighbor of u2, and u2 reflects the second-order potential preference of user u1. However, the farther the neighbor nodes are from user u1, the smaller the preference and influence on user u1’s node is, which is a decay process, similar to water waves. The user’s preference information can be regarded as the diffusion of personalization on the interaction graph. Therefore, when the user has only a few sparse interactions, this connection and preference propagation relationship can be used to spread the user’s preference to higher-order neighborhoods to obtain the user’s higher-order latent preference, as shown in Figure 2.Figure 2
User-attraction interaction diagram.Existing research shows that graph convolution can effectively process data with graph structure, and some progress has also been made in the field of recommendation; using graph convolution to mine collaborative signals from graph structure to obtain user preferences can improve recommendation performance [15]. Studies have shown that GCN will oversmooth as the number of layers increases, all nodes will tend to a value, and as the number of layers increases, the amount of parameters will also increase exponentially. And some studies have shown that the number of GCN layers generally needs to reach 4-5 layers to cover all nodes in the graph, the traditional graph convolution method has very limited expandable neighborhoods, and the effect on the problem of user cold start is not ideal. Differentiated numerical similarity calculation methods are designed for different types of users to alleviate the deficiencies of traditional numerical similarity when faced with sparse data. Finally, numerical similarity and structural similarity are combined to form sparse cosine similarity. Inspired by the literature, we utilize a graph convolutional network with personalized PageRank propagation to model the process of user preference propagation on the user-spot interaction graph, the algorithm increases the opportunity to transmit back to the root node and does not have to be limited by the size of the node neighborhood, it can effectively aggregate an infinite number of neighborhoods to obtain user and scenic spot information into the node embedding vector, and there will be no node smoothing.Embedding vector for each user and attraction node, the information of its neighbor nodes and the embedded information of the node itself are combined through the neural network. For a user nodexu reaches the scenic spot node xi in a random walk, the preference information of node xu is also propagated to node xi, then node xi carries the preference information of user node xu, and node xu is adjusted through personalized PageRank propagation, such as the following formula:
(2)mxu=αIn−1−αA∧−1xu.m represents the message embedding (that is, the information to be propagated), α∈0,1 is the transmission probability, Iu,i is the influence score of node xi on node xu, the influence of node xi on node xu is equal to the influence of node xu on node xi, and this value is different for each root node. The preference influence reduction from the root node can be adjusted by α. The matrix-form propagation equations of the propagation rules of each layer of the graph convolution of personalized PageRank propagation are as follows:
(3)Z0=H=X,(4)Zl+1=1−αA^Zt+αH,whereZl∈RN+M×dl is the embedding vector of users and attractions obtained after I-step propagation. X0 is set to X during the initial message passing iteration; 1 represents an identity matrix, A~^=D~−1/2A~D~−1/2 is a symmetric normalized adjacency matrix with self-circular sum, and A represents the Laplacian matrix of the user-item graph, expressed as the following equation:
(5)A=0RRT0,whereR∈RN×M is the user-item interaction matrix, 0 is the matrix with all 0 s, A is the adjacency matrix, and D is the opposite angle matrix. Personalized PageRank Propagation Graph Convolutional Networks can efficiently use even infinitely many neighbor aggregation layers, so higher-order connectivity information can be explored by stacking more embedding propagation layers. As shown in Figure 3, a small number of user access records can be extended to all nodes to obtain the user’s potential preference information.
(3)
Information autoencoderFigure 3
The result of the influence of the number of propagation layersL on the Recall@10 indicator of cold-start users.For the embedding vector of each layer of users and scenic spots obtained through the graph convolutional network propagated by personalized PageRank, an autoencoder is used to filter the encoded information to obtain the effective embedding vector of each layer of users and scenic spots, and for the final model prediction, the calculation formula is shown in the following equation:(6)El=W1lZl.W1l∈Rd×d′ is a learnable parameter for each layer, which is used to extract useful information in the embedding vectors of users and attractions obtained by each propagation layer for message aggregation.


### 2.2. Experimental Setup

The author uses two datasets, MFW and Ubicomp, to verify the effectiveness of the proposed method, MFW is a self-built tourism dataset, and Ubicomp is a widely used public restaurant recommendation dataset; it is used to verify the universality of the algorithm, and the details are as follows:MFW: the dataset used for the experiment contains 9157 users who have visited at least two destinations and 1407 attractions that have been visited by at least 10 users, and we choose a destination that users recently visited as a test and travel records of other destinations visited as training. Users who visit less than 4 attractions in the training set are cold-start users, and other users are hot-start users [16].Ubicomp: this dataset includes New York City restaurant check-in and labeling data collected from Foursquare from October 2019 to February 2020, including 3112 users, 3298 sites, and 27149 access records. The users who visit less than 4 attractions in the training set are cold-start users, and other users are hot-start users [17]. The traditional neuron classification method based on geometric morphology relies on the extraction and selection of neuron spatial structure features, which will lose a lot of useful neuron classification information. The adaptive projection algorithm is used to convert three-dimensional neurons without extracting the neuron’s characteristics.


## 2.1. Tourist Attraction Recommendation Method Based on Graph Convolutional Network

This section will introduce the proposed personalized travel recommendation method based on graph convolutional network, and the overall structure includes four parts: embedding layer, information dissemination aggregation layer, information self-encoding layer, and prediction layer.(1)
Embedding layerLike the mainstream recommendation system, the latent vectorsxu and xi∈Rd of the Euclidean space are used to represent the user u and the scenic spot i, respectively, and d represents the dimension of the embedding vector, thus establishing a lookup table X as the initial value of users and attractions, which is randomly initialized through normal distribution, as shown in the following formula:
(1)Χ=xu1,xu2,⋯,xuN,xi1,xi2,⋯,xiN,whereN and M represent the number of users and attractions, respectively. In the traditional recommendation model, the initial embedding vectors of users and attractions are directly input into the interaction layer for end-to-end optimization, but users and attractions in real scenes are not isolated, and there is a certain connection between them. When the feedback records of users are sparse, simple splicing or nonlinear interaction cannot well extract the associations between users and users, users and attractions, and attractions and attractions. All users and attractions are regarded as each node in the graph, so that preference information is propagated in the user-attraction interaction graph, so that nodes not only focus on local neighbors but also obtain higher-order relationships between users and attractions through information diffusion, and contacts get effective collaborative information embedding into user and sight embedding vectors to alleviate the data sparsity problem.
(2)
PageRank-based graph convolutional network information propagation and aggregation layerIn the user-spot interaction, each user or scenic spot is not isolated but related to each other, and the user’s direct interactive scenic spot is defined as the user’s local neighbor, for example, scenic spoti1,i2,i4 in Figure 2 is the local neighbors of user u1, they reflect the user’s direct preference evidence, and the user’s potential preference is also affected by the local neighbors. Attraction i2,i4 is visited by user u2 to establish a connection, so u1 is the second-order neighbor of u2, and u2 reflects the second-order potential preference of user u1. However, the farther the neighbor nodes are from user u1, the smaller the preference and influence on user u1’s node is, which is a decay process, similar to water waves. The user’s preference information can be regarded as the diffusion of personalization on the interaction graph. Therefore, when the user has only a few sparse interactions, this connection and preference propagation relationship can be used to spread the user’s preference to higher-order neighborhoods to obtain the user’s higher-order latent preference, as shown in Figure 2.Figure 2
User-attraction interaction diagram.Existing research shows that graph convolution can effectively process data with graph structure, and some progress has also been made in the field of recommendation; using graph convolution to mine collaborative signals from graph structure to obtain user preferences can improve recommendation performance [15]. Studies have shown that GCN will oversmooth as the number of layers increases, all nodes will tend to a value, and as the number of layers increases, the amount of parameters will also increase exponentially. And some studies have shown that the number of GCN layers generally needs to reach 4-5 layers to cover all nodes in the graph, the traditional graph convolution method has very limited expandable neighborhoods, and the effect on the problem of user cold start is not ideal. Differentiated numerical similarity calculation methods are designed for different types of users to alleviate the deficiencies of traditional numerical similarity when faced with sparse data. Finally, numerical similarity and structural similarity are combined to form sparse cosine similarity. Inspired by the literature, we utilize a graph convolutional network with personalized PageRank propagation to model the process of user preference propagation on the user-spot interaction graph, the algorithm increases the opportunity to transmit back to the root node and does not have to be limited by the size of the node neighborhood, it can effectively aggregate an infinite number of neighborhoods to obtain user and scenic spot information into the node embedding vector, and there will be no node smoothing.Embedding vector for each user and attraction node, the information of its neighbor nodes and the embedded information of the node itself are combined through the neural network. For a user nodexu reaches the scenic spot node xi in a random walk, the preference information of node xu is also propagated to node xi, then node xi carries the preference information of user node xu, and node xu is adjusted through personalized PageRank propagation, such as the following formula:
(2)mxu=αIn−1−αA∧−1xu.m represents the message embedding (that is, the information to be propagated), α∈0,1 is the transmission probability, Iu,i is the influence score of node xi on node xu, the influence of node xi on node xu is equal to the influence of node xu on node xi, and this value is different for each root node. The preference influence reduction from the root node can be adjusted by α. The matrix-form propagation equations of the propagation rules of each layer of the graph convolution of personalized PageRank propagation are as follows:
(3)Z0=H=X,(4)Zl+1=1−αA^Zt+αH,whereZl∈RN+M×dl is the embedding vector of users and attractions obtained after I-step propagation. X0 is set to X during the initial message passing iteration; 1 represents an identity matrix, A~^=D~−1/2A~D~−1/2 is a symmetric normalized adjacency matrix with self-circular sum, and A represents the Laplacian matrix of the user-item graph, expressed as the following equation:
(5)A=0RRT0,whereR∈RN×M is the user-item interaction matrix, 0 is the matrix with all 0 s, A is the adjacency matrix, and D is the opposite angle matrix. Personalized PageRank Propagation Graph Convolutional Networks can efficiently use even infinitely many neighbor aggregation layers, so higher-order connectivity information can be explored by stacking more embedding propagation layers. As shown in Figure 3, a small number of user access records can be extended to all nodes to obtain the user’s potential preference information.
(3)
Information autoencoderFigure 3
The result of the influence of the number of propagation layersL on the Recall@10 indicator of cold-start users.For the embedding vector of each layer of users and scenic spots obtained through the graph convolutional network propagated by personalized PageRank, an autoencoder is used to filter the encoded information to obtain the effective embedding vector of each layer of users and scenic spots, and for the final model prediction, the calculation formula is shown in the following equation:(6)El=W1lZl.W1l∈Rd×d′ is a learnable parameter for each layer, which is used to extract useful information in the embedding vectors of users and attractions obtained by each propagation layer for message aggregation.


## 2.2. Experimental Setup

The author uses two datasets, MFW and Ubicomp, to verify the effectiveness of the proposed method, MFW is a self-built tourism dataset, and Ubicomp is a widely used public restaurant recommendation dataset; it is used to verify the universality of the algorithm, and the details are as follows:MFW: the dataset used for the experiment contains 9157 users who have visited at least two destinations and 1407 attractions that have been visited by at least 10 users, and we choose a destination that users recently visited as a test and travel records of other destinations visited as training. Users who visit less than 4 attractions in the training set are cold-start users, and other users are hot-start users [16].Ubicomp: this dataset includes New York City restaurant check-in and labeling data collected from Foursquare from October 2019 to February 2020, including 3112 users, 3298 sites, and 27149 access records. The users who visit less than 4 attractions in the training set are cold-start users, and other users are hot-start users [17]. The traditional neuron classification method based on geometric morphology relies on the extraction and selection of neuron spatial structure features, which will lose a lot of useful neuron classification information. The adaptive projection algorithm is used to convert three-dimensional neurons without extracting the neuron’s characteristics.


## 3. Results and Analysis

### 3.1. Performance Comparison Experiment of Different Recommendation Methods on MFW Dataset

In order to verify the effectiveness of the model, we compare the proposed method with several mainstream recommendation methods, which are introduced below, and the BPR method optimizes the MF model using pairwise Bayesian personalized ranking loss [18]. The PTRMJL method is a personalized travel recommendation method based on multiview joint learning [19]. PTRMJL is a travel package recommendation method based on multiview attention mechanism. It learns the unified representation of travel packages with the help of deep learning technology and learns the user’s interest representation based on the long-term and short-term clickstream data of online travel users to generate recommendations. The method consists of tourism package encoding and user interest encoding. In the tourism package encoding module, a unified tourism package representation is learned by using word-level and view-level attention networks to select important words and views from the attributes of the tourism package. The GCMC method employs a GCN encoder to generate representation vectors for users and items, considering only the nearest neighbors [20]. The NGCF method propagates user and item embeddings on the user-item graph data structure and then comprehensively utilizes each layer of propagated embeddings combined with CF for recommendation [21]. In order to evaluate the performance of recommendations, we provide a list of Top-N recommendations for each user in the test set.Table1 presents the comparison of the two types of recommendation performance between the proposed method and other recommendation methods on the MFW dataset. It can be seen from the table that PTRGCN outperforms several other comparison methods in the user cold-start scenario of the MFW dataset. For hot-start users, the PTRMJL method has the best recommendation performance, and the method proposed by the author is second only to the PTRMJL method and the GCMG method, but it is 5.7% more accurate than the BPR method, 9.7% improvement in recall rate. In the recommendation for cold-start users, the method proposed by the author has the best performance in terms of precision rate, recall rate, and normalized loss cumulative gain, and the recall rate Recall@5 is 17.9% higher than BPR, and the accuracy is improved by 11.8%. The method proposed by the author has better travel recommendation performance for cold-start users than hot-start users, because the algorithm utilizes the deep potential connections between nodes; since hot-start users are directly connected to more scenic spots than cold-start users, there are many local neighbors, during the training process. Overfitting may lead to performance degradation; therefore, the method proposed by the author is more suitable for cold-start users, as shown in Table 1.Table 1
Comparison of algorithm recommendation performance for different types of users on MFW dataset.
TypeMethodRecall@5Precision@5NDCG@5Recall@10Precision@10NDCG@10Warm userBPRMF0.2390.2840.2670.3460.2430.367PERMJL0.3200.3510.4500.4780.2950.482NGCF0.2790.2840.3620.3900.2510.387GCMC0.3390.3410.4410.4690.2990.468PTRGCN0.3470.3390.4400.4590.2910.462Cold userBPRMF0.2140.2300.3730.3140.2050.304MRPTR0.2920.3330.3990.4420.2720.440NGCF0.2700.2590.3290.3820.2260.362GCMC0.3250.3060.3920.4440.2650.427PTRGCN0.3930.3570.4640.4960.2910.490


### 3.2. Performance Comparison of Different Recommendation Methods on Ubicomp Dataset

In order to verify the universality of the proposed method, the author verified the effectiveness of the method on the Ubicomp dataset, and the experimental results are shown in Table2. From the experimental results, it can be seen that the PTRGCN method proposed by the author improves the accuracy of Precision@5 by 15.8%, the recall rate of Reach@10 by 7.4%, and the improvement of NDCG@10 by 14.5% for hot-start users. The recommendation performance accuracy rate, recall rate, and normalized ranking index of cold-start users are higher than those of warm-start users. For cold-start users, the accuracy rate of Precision@5 is increased by 13.1%, and the recall rate of Recall@10 is increased by 17.1%. Because cold-start user access records are sparse, methods such as BPR and GCMG are limited by data sparsity and have low accuracy. Therefore, the method proposed by the author effectively improves the recommendation performance for cold-start users on the basis of maintaining the recommendation performance of hot-start users, as shown in Table 2.Table 2
Algorithm recommendation performance comparison for different types of users on Ubicomp dataset.
TypeMethodRecall@5Precision@5NDCG@5Recall@10Precision@10NDCG@10Warm userBPRMF0.0020.0030.0030.0040.0030.004NGCF0.0530.1390.1720.0680.1070.142GCMC0.0700.0240.0700.0350.0580.063PTRGCN0.0620.1610.1760.0800.1260.149Cold userBPRMF0.0010.0020.0050.0010.0010.002NGCF0.1170.1180.1700.1430.0860.170GCMC0.0440.0430.0490.0620.0390.057PTRGCN0.1320.1310.1660.1720.1030.174


### 3.3. The Effect of the Number of Propagation Layers

The number of layers of graph propagation plays a key role in the method proposed in this chapter, and the method proposed by the author does not make nodes smooth as the number of layers deepens. The author discusses the impact of changing the depth of graph model propagation on recommendation performance, and Figures3 and 4 show the results of experiments on the dataset MFW. From the experimental results, it can be seen that the PTRGCN method achieves the best performance when the number of layers in the recommendation for cold-start users is L=4, and the performance when L=5 is similar to that when L=4, and the algorithm successfully avoids the oversmoothing problem; with the increase of the number of layers, the recommendation recall rate and accuracy rate of cold-start users increase, and the recommendation performance improves more significantly than that of hot-start users. Since hot-start users have more local neighbors to represent their preferences, and cold-start users have few access records, they need to mine users and scenic spots that have deep connections with users through the expansion of layers; therefore, cold-start users are more than hot-start users, and users are more sensitive to an increase in the number of layers. Cold-start users need higher-level expansion, so that users’ potential preferences can be propagated to more nodes to mine users’ deep-level preferences, as shown in Figures 3 and 4.Figure 4
The effect of the number of propagation layersL on the recommendation index of the hot-start user Recall@10.


## 3.1. Performance Comparison Experiment of Different Recommendation Methods on MFW Dataset

In order to verify the effectiveness of the model, we compare the proposed method with several mainstream recommendation methods, which are introduced below, and the BPR method optimizes the MF model using pairwise Bayesian personalized ranking loss [18]. The PTRMJL method is a personalized travel recommendation method based on multiview joint learning [19]. PTRMJL is a travel package recommendation method based on multiview attention mechanism. It learns the unified representation of travel packages with the help of deep learning technology and learns the user’s interest representation based on the long-term and short-term clickstream data of online travel users to generate recommendations. The method consists of tourism package encoding and user interest encoding. In the tourism package encoding module, a unified tourism package representation is learned by using word-level and view-level attention networks to select important words and views from the attributes of the tourism package. The GCMC method employs a GCN encoder to generate representation vectors for users and items, considering only the nearest neighbors [20]. The NGCF method propagates user and item embeddings on the user-item graph data structure and then comprehensively utilizes each layer of propagated embeddings combined with CF for recommendation [21]. In order to evaluate the performance of recommendations, we provide a list of Top-N recommendations for each user in the test set.Table1 presents the comparison of the two types of recommendation performance between the proposed method and other recommendation methods on the MFW dataset. It can be seen from the table that PTRGCN outperforms several other comparison methods in the user cold-start scenario of the MFW dataset. For hot-start users, the PTRMJL method has the best recommendation performance, and the method proposed by the author is second only to the PTRMJL method and the GCMG method, but it is 5.7% more accurate than the BPR method, 9.7% improvement in recall rate. In the recommendation for cold-start users, the method proposed by the author has the best performance in terms of precision rate, recall rate, and normalized loss cumulative gain, and the recall rate Recall@5 is 17.9% higher than BPR, and the accuracy is improved by 11.8%. The method proposed by the author has better travel recommendation performance for cold-start users than hot-start users, because the algorithm utilizes the deep potential connections between nodes; since hot-start users are directly connected to more scenic spots than cold-start users, there are many local neighbors, during the training process. Overfitting may lead to performance degradation; therefore, the method proposed by the author is more suitable for cold-start users, as shown in Table 1.Table 1
Comparison of algorithm recommendation performance for different types of users on MFW dataset.
TypeMethodRecall@5Precision@5NDCG@5Recall@10Precision@10NDCG@10Warm userBPRMF0.2390.2840.2670.3460.2430.367PERMJL0.3200.3510.4500.4780.2950.482NGCF0.2790.2840.3620.3900.2510.387GCMC0.3390.3410.4410.4690.2990.468PTRGCN0.3470.3390.4400.4590.2910.462Cold userBPRMF0.2140.2300.3730.3140.2050.304MRPTR0.2920.3330.3990.4420.2720.440NGCF0.2700.2590.3290.3820.2260.362GCMC0.3250.3060.3920.4440.2650.427PTRGCN0.3930.3570.4640.4960.2910.490


## 3.2. Performance Comparison of Different Recommendation Methods on Ubicomp Dataset

In order to verify the universality of the proposed method, the author verified the effectiveness of the method on the Ubicomp dataset, and the experimental results are shown in Table2. From the experimental results, it can be seen that the PTRGCN method proposed by the author improves the accuracy of Precision@5 by 15.8%, the recall rate of Reach@10 by 7.4%, and the improvement of NDCG@10 by 14.5% for hot-start users. The recommendation performance accuracy rate, recall rate, and normalized ranking index of cold-start users are higher than those of warm-start users. For cold-start users, the accuracy rate of Precision@5 is increased by 13.1%, and the recall rate of Recall@10 is increased by 17.1%. Because cold-start user access records are sparse, methods such as BPR and GCMG are limited by data sparsity and have low accuracy. Therefore, the method proposed by the author effectively improves the recommendation performance for cold-start users on the basis of maintaining the recommendation performance of hot-start users, as shown in Table 2.Table 2
Algorithm recommendation performance comparison for different types of users on Ubicomp dataset.
TypeMethodRecall@5Precision@5NDCG@5Recall@10Precision@10NDCG@10Warm userBPRMF0.0020.0030.0030.0040.0030.004NGCF0.0530.1390.1720.0680.1070.142GCMC0.0700.0240.0700.0350.0580.063PTRGCN0.0620.1610.1760.0800.1260.149Cold userBPRMF0.0010.0020.0050.0010.0010.002NGCF0.1170.1180.1700.1430.0860.170GCMC0.0440.0430.0490.0620.0390.057PTRGCN0.1320.1310.1660.1720.1030.174


## 3.3. The Effect of the Number of Propagation Layers

The number of layers of graph propagation plays a key role in the method proposed in this chapter, and the method proposed by the author does not make nodes smooth as the number of layers deepens. The author discusses the impact of changing the depth of graph model propagation on recommendation performance, and Figures3 and 4 show the results of experiments on the dataset MFW. From the experimental results, it can be seen that the PTRGCN method achieves the best performance when the number of layers in the recommendation for cold-start users is L=4, and the performance when L=5 is similar to that when L=4, and the algorithm successfully avoids the oversmoothing problem; with the increase of the number of layers, the recommendation recall rate and accuracy rate of cold-start users increase, and the recommendation performance improves more significantly than that of hot-start users. Since hot-start users have more local neighbors to represent their preferences, and cold-start users have few access records, they need to mine users and scenic spots that have deep connections with users through the expansion of layers; therefore, cold-start users are more than hot-start users, and users are more sensitive to an increase in the number of layers. Cold-start users need higher-level expansion, so that users’ potential preferences can be propagated to more nodes to mine users’ deep-level preferences, as shown in Figures 3 and 4.Figure 4
The effect of the number of propagation layersL on the recommendation index of the hot-start user Recall@10.


## 4. Conclusion

The author proposes a tourist attraction recommendation method based on deep learning and Internet of Things research, using graph convolutional network architecture to provide tourist attraction recommendation mainly for cold-start users. From the experimental results, it can be seen that the PTRGCN method improves the accuracy by 15.8% on the hot-start user, on the Reach@10 by 7.4%, and on the NDCG@10 by 14.5%. The recommendation performance accuracy rate, recall rate, and normalized ranking index of cold-start users are higher than those of warm-start users. For cold-start users, the accuracy rate of Precision@5 is increased by 13.1%, and the recall rate of Recall@10 is increased by 17.1%. Because cold-start user access records are sparse, methods such as BPR and GCMG are limited by data sparsity and have low accuracy. Through experimental deduction, it is proved that deep learning and Internet of Things technology can effectively meet the needs of tourist attractions recommendation.



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*Source: 6221413-2022-08-26.xml*