# Recommendation Model of Tourist Attractions Based on Deep Learning

Authors: Xinquan Cheng; Wenlong Su
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation.


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## Body

## 1. Introduction

With the rapid growth of information technology, the need for deepening of the information technology industry and information technology has become increasingly strong [1]. In the data age, which is overloaded with data, users can not quickly find the data they are interested in [2]. Therefore, the tourism consulting industry has been designed to meet the needs of consumers and provide customer satisfaction. One-person travel advertising has been widely used because it integrates referrals into the tourism industry, allowing consumers to make informed personal decisions, and provide their favorite and most accurate products. Research costs.The design recommendations are effective based on an analysis of user data behavior and provide users with the quality of the model as they see fit [3]. Recommendations can be divided by data: user behavior data, data usage data, content data, and social network data. In recent years, many scientists have proposed hybrid technology-approved and restricted-based technology to improve the technology performance of feedback and to ensure the use of multiple restrictions.As personal tourism referral technology has become a hotspot for research and industry research, the author evaluates referral technology, including information on the behavior of consumers, consumer information, content, and social network information years [4]. At the same time, the latest advances in related activities have been studied, highlighting the use of technology that can improve performance and limitations-based approval high-performance technology that meets various options [5]. Currently, the most widely used training models in the field of communication include multilayer sensors, automatic encoders, repeat neural networks, circulatory neural networks, and interactive interactions alk different versions [6]. The framework for the approval by in-depth research is shown in Figure 1.Figure 1
Framework of a recommender system based on deep learning.


## 2. Literature Review

In recent years, with the rapid development of information technology such as the Internet, big data, and cloud computing, people have expanded to big data in the environment around [7]. Big data contains a lot of information and knowledge, which allows people to access big data in a short amount of time [8]. At the same time, however, the negative impact of cracked data is that “broadcast data” is problematic because it is difficult for users to get still content. Importance of hard data when encountering large data. In terms of data filtering technology, the conventional system solution of data overload by providing users with personal quality content has become an important technology in various areas of application and is focused on research.Technically, there are two consensus points: a combination filter based on consensus and a consensus based concept. The first is based on the relationship of user impact, and the latter focuses on the appropriate ranking by the characteristics of the content. In recent years, through in-depth research, the system has become increasingly important to many scientists’ ability to study and represent the negative effects of consumer products and provide essential products of consumers and products [9]. Although in-depth training works very well to facilitate the study of the secrets of users and products, and to advance the level of research and application of recommendations, it is difficult to obtain get enough information of users to choose from in a variety of different, low starting cold and Other problems, the problem still exists. At the same time, model approval determines everything on its own in determining consumer preferences, and it is not possible to model product relationships in chronological order. because he could not determine the status of his system.Learning-improvement strategies have led to advances in game and robotics management, new breakthroughs in science in the age of intelligence, and new ways to explore research in a commentary. Reinforcement learning combined with deep learning methods has the ability to process large-scale data and discover and extract low-level features, so as to achieve specific goals more accurately. As an Interactive Recommendation (IR) method, the recommendation model based on reinforcement learning can update the recommendation strategy by interacting with users in real time and obtaining real feedback from users, compared with traditional static methods, it is more in line with realistic recommendation scenarios [10]. At the same time, since reinforcement learning problems are usually normalized as Markov Decision Process (MDP), such models have the natural characteristics of modeling user behavior sequences, which can fully characterize the sequence features and capture users’ dynamic preferences [11]. In addition, the setting of the exploration mechanism can enable the agent to fully explore the state and action space, which improves the diversity of recommendation results to a certain extent; finally, since this type of model often maximizes the cumulative revenue of the recommendation system, that is, the long-term feedback of users, updating the recommendation strategy as an optimization goal can improve the long-term satisfaction of users to a certain extent [12].From the current point of view, the research application of reinforcement learning in recommender systems has become one of the research focuses in recent years. However, there is still a lack of corresponding review summaries. The applications of deep reinforcement learning in search, recommender systems, and advertising are reviewed, but the review is relatively small in terms of recommendation system related research. In addition, as far as we know, there is no Chinese literature that systematically recommends the system as the research object, and comprehensively and systematically summarizes and analyzes the implementation method based on reinforcement learning [13].The recommendation, based on the DRL value function, uses a deep neural network to estimate the Q-value function, and the purpose of the optimization is to modify the neural network regularly from gradient to complete all the gifts and find good ideas. The DRL framework based on the agreed function values is shown in Figure2.Figure 2
Recommendation framework based on value iterative DRL.Since reinforcement learning can dynamically obtain user behavior information, incorporating the latest preference information in real time, more and more reinforcement learning is currently being used in news, e-commerce, medical and other fields. Among them, tourism is one of the entertainment items involved in people’s life, and there are few research studies on the recommendation of tourist attractions [14]. The inverse reinforcement learning is applied to the recommendation of tourist attractions, using the user’s past selection order of attractions and the current scene context to understand the user’s preferences and establishes a preference learning model that takes into account the timing of commodity consumption, then further use the inverse reinforcement learning method for tourist attraction recommendation.The classification of recommendation models based on reinforcement learning is shown in Table1.Table 1
Classification of recommendation models based on reinforcement learning.
TypeTraditional reinforcement learning recommendationDeep reinforcement learning recommendationMAB-based recommendationMDP-based recommendationRecommendation based on value function DRLRecommendation based on policy gradient DRLEnterActions/States and actionsState and action/StateState and action/StateState and action/StateOutputReward valueQ valueQ valueProbability of taking an actionFeatureMaximize step rewardMaximize total rewardMaximize total rewardMaximize total rewardLiterature modelMusicCN-BanditConstrained PSRLDRNLEAPDeepPageDCdriftMulti＿WithRobustCRSDeepChainCoLinBic-RLDEERSTop-k CorrectedRCRCorr-BanditBayes-UCB-CNFeedRecREINFORCEMARDPGMF-Banditε-SVR-CGAUMLSICVL-Rece-TSbanditDPG-FBESLATEQTPGRMaHRLDMCBDJ-MCPseudo Dyna-QIRecGANHRL-RecPOMDP-RecAPGValue-awareSRL-RNNKERLWeb-banditIRL-basedUDQNLIRDKGRLGCQNNRRSRecommendations for tourism always ignore information about tourists, characteristics of tourists, and tourists, and make predictions based on a survey of tourists and tourist destinations. The advantages of tour recommendations can be improved by identifying features and descriptive information only using a collapsible neural network. In order to differentiate between different features in the video, the author proposes an approved multimodal algorithm based on a multimodal in-depth study to obtain a wide range of data added by video. A combination of neural frequencies is used to provide users and video recordings, and long-term and short-term memory is used to generate historical data to provide users movies correctly. The battery and finally improve the accuracy of the instructions. The author has developed models of travel recommendations as the rupture of the neural network interferes with excessive travel information [15].


## 3. Methods

### 3.1. Related Work

In the past, recommendations for tourists were rarely considered in the recommendations for tourists, but these recommendations contain the notion of tourism, which is the basis of reason important for others to measure. The authors combine the connections of neural networks and cofiltering methods to obtain and distribute features through the connections of neural networks and estimate user rating using the coax filtering method-filtering. Among them, decomposing the role in the agreement would be to alleviate the problem of data thinning of the disrupted neural network sharing method, which can preserve the shared knowledge and personal experience advantage of the shared filter method.


#### 3.1.1. Cotest approval algorithm

For the most commonly used recommendations, the key concept of integrated filters is to calculate the compatibility of users or products to complete the agreement [4]. A consumer sharing algorithm filter algorithm counts the similarities of users, creates proximity user groups, and then provides product satisfaction to the user group close to each other; the product-based cofilter approval algorithm takes into account the similarity of the products; the standard-based cofilter approval algorithm combines some smart models, train, and test data so that the user can get the desired information better when making the recommendations.Considering the consistency is important for finding potential customers or products nearby. The most common methods for calculating consistency are the Pearson method and the cosine method. Among them, Pearson’s similarity is necessary for computing data and determining different users in more detail, so model (1) is shown as follows:(1)sima,b=∑i∈Ia,bRa,i−Ra¯Rb,i−Rb¯∑i∈Ia,bRa,i−Ra¯2∑i∈Ia,bRb,i−Rb¯2.In formula (1), sim (a, b) represents the similarity between users a and b; Ia,b are the common user characteristics of users a and b; Ra,i is the ith eigenvalue of user a, Rb,i is the ith eigenvalue of user b; R¯a and R¯b are the average feature scores of users a and b, respectively.The author divides users and objects into K groups and adopts the new dynamic evolution group algorithm for similar groups. Integrated filter systems are used to estimate each group of K, and it is recommended that the target user be estimated according to the level of demand from the group. The use of offline experiments using an integrated integration filter algorithm improves the authenticity of group websites.


#### 3.1.2. Metabolic neural network

The Convolutional Neural Network (CNN) is one of the current sources of in-depth research, and its use in image processing has evolved and has been widely used in text interpretation [16]. In the case of the anterior neural network, the basic structure of CNN is similar to that of other neural networks, with the entry process, the bottom layer, and the release process as the data source. The CNN secret code includes 3 layers of rotation, integration, and integration. Among these, the convolution process is the most important, resulting in the calculation of the rotation of the convolution core, which contains the signal input and extractor characteristics, and the data processing in the deep case; the pool consolidation layer filters the extracted material by aggregating, reducing the residual material, and preserving the data of the core material [17]; a single layer combines all the function data completed in the top layer and sent to the output process. The CNN model can be seen in Figure 3.Figure 3
Convolutional neural network structure.Use the nonlinear features of the convolutional layer and the pooling layer in the convolutional neural network to filter the microblog text content, ensuring the security of search information. Combining the outputs of deep convolutional neural networks and weighted feature extraction, implement correlation extraction between user data and music.


### 3.2. Construction of Travel Recommendation Model

#### 3.2.1. Problem description

The goal of the author’s study is to develop travel recommendations based on a user-friendly integration algorithm, which is used to deepen the experience deep neural network because it is difficult to solve the inadequacy of deep data separate and interpret tourism information. Visitors commenting on the tourism industry have different views on the tourism process, which is divided into pros and cons. Integrating important data of travel users, behavioral data, and data points to solve the problem of data alone. The specific categories of consumer data are as follows:(1)
The basic informationPi of the user includes: name (ID), age (age), gender (sex), location (place), and other attributes, of which the name (ID) plays a unique role in identifying and does not participate in the analysis of the data, that is, Pi = {namei, agei,sexi, placei}(2)
Behavior informationSi is divided into demand (demand), interest preference (prefer), and consumption situation (consume), namely, Si = {demandi, preferi,sexi, consumei}(a)
Demand information refers to the user’s food, housing, transportation, and travel methods. Travel methods include family travel, close friend travel, solo travel, couple/couple travel, local travel, surrounding travel, and off-site travel (referring to far away from the city where you are located)(b)
Interest preferences include what kind of environment, what style of attractions, and what activities do you like(c)
Consumption situation refers to the user’s price preference, such as travel mode, ticket price, and accommodation price(3)
Contextual informationQi includes the time (time), the user’s location (location), the user’s session and interaction information (interact) on the network platform, and the session and interaction information refers to the user’s visit time and the number of visits to the scenic spot, favorite records, etc., Qi = {timei, locationi,interacti}.


#### 3.2.2. Model building

The travel guide authors focus on three aspects of basic information, behavioral information, and the content of information in user data and send vectorized travel user review data matrix to the circulatory network for training to get a good and negative view of the product, performance data are then used for similar calculations [18]. The structure of the tourism permit is shown in Figure 4.Figure 4
Recommendation model structure.The basic information of tourist users is expressed as follows:(2)P=α1age+α2sex+α3place.The behavior information of tourist users is expressed as follows:(3)S=β1demand+β2prefer+β3consume.The context information of tourist users is expressed as follows:(4)Q=γ1time+γ2location+γ3interact.Therefore, the travel recommendation feature information is expressed as follows:(5)TR=αP+βS+γQ,where α is the weight of basic information, α = [α1,α2,α3]; β is the weight of behavior information, β = [β1, β2,β3], γ is the weight of context information, γ  = [γ1, γ2, γ3].The unstructured data mentioned above are processed, in which the age below 18 is recorded as 1, the age of 18 to 25 is recorded as 2, the age of 26 to 35 is recorded as 3, and the age of 36 to 45 is recorded as 4, 46 to 55 years old is recorded as 5, 56 years old and above is recorded as 6; the gender is recorded as 0 for male and 1 for female; the same location is recorded as 1, and the difference is recorded as 0; according to the order of the above-given travel modes, they are represented by 1∼7, respectively; consumption below 1000 yuan is recorded as 1, 1000∼2000 yuan is recorded as 2, 2001∼3000 yuan is recorded as 3, 3001∼4000 yuan is recorded as 4, 4001∼5000 yuan is recorded as 5, and over 5000 yuan is recorded as 6; the time is divided into spring, summer, autumn, and winter, the months from March to May are recorded as 1, the months from June to August are recorded as 2, the months from September to November are recorded as 3, and the months from December to February are recorded as 4. Use formula (1) to calculate the similarity of features between users. Since different user features have different effects on user similarity, the calculation of user similarity needs to assign corresponding weights to different features, and find similar user groups through.(6)SIMa,b=αsimaP,bP+βsimaS,bS+γsimaQ,bQ.After the vector data is input into the convolutional neural network, the inner product is calculated with the convolution kernel in the convolution layer, the convolution layer contains multiple convolution kernels, and each attribute element that constitutes the convolution kernel has its own weight coefficient and offset when the size of the processed tourism vector data is smaller than the original tourism vector data, zero can be used to fill the sample boundary. The eigenvalues of the convolutional neural network can be calculated by the following equation:(7)ci=fw⋅veci+b.In formula (7), w is the convolution weight value; veci is the word vector; b is the bias term.Select the commonly used nonlinear sigmoid activation function to complete the convolution calculation and the adjustment of the weight offset, the activation function is(8)sigmoidx=11+e−x.The data after convolution calculation enters the pooling layer, and the maximum pooling method is used to obtain the result. There are no separate weight coefficients in the pooling layer. Assuming that the tourist user data obtained by the convolutional layer isU=u1,u2,…,uS, the pooling result P is(9)P=maxU=maxu1,u2,…,us.After the pooling result reaches the fully connected layer, through the ReLU activation function, the final tourist user implicit feature information TR and the adjusted weight coefficient and deviation are obtained, and then the result is output, the calculation formula (10) is as follows:(10)TR=ReLUw⋅veci+b.ReLUx=max0,x in formula (10).The convolutional neural network classifies the positive and negative of the reviews by whether there are direct classifiers that express emotional factors in the tourist reviews, according to the proportion of positive and negative word vectors, sort from high to low, and rate it. 100% of the positive words in the comments are scored as 5 points, 99% of the positive words are scored as 4.9 points, and so on, until 1 point, and then the predicted users are recommended according to the ranking order.


## 3.1. Related Work

In the past, recommendations for tourists were rarely considered in the recommendations for tourists, but these recommendations contain the notion of tourism, which is the basis of reason important for others to measure. The authors combine the connections of neural networks and cofiltering methods to obtain and distribute features through the connections of neural networks and estimate user rating using the coax filtering method-filtering. Among them, decomposing the role in the agreement would be to alleviate the problem of data thinning of the disrupted neural network sharing method, which can preserve the shared knowledge and personal experience advantage of the shared filter method.


### 3.1.1. Cotest approval algorithm

For the most commonly used recommendations, the key concept of integrated filters is to calculate the compatibility of users or products to complete the agreement [4]. A consumer sharing algorithm filter algorithm counts the similarities of users, creates proximity user groups, and then provides product satisfaction to the user group close to each other; the product-based cofilter approval algorithm takes into account the similarity of the products; the standard-based cofilter approval algorithm combines some smart models, train, and test data so that the user can get the desired information better when making the recommendations.Considering the consistency is important for finding potential customers or products nearby. The most common methods for calculating consistency are the Pearson method and the cosine method. Among them, Pearson’s similarity is necessary for computing data and determining different users in more detail, so model (1) is shown as follows:(1)sima,b=∑i∈Ia,bRa,i−Ra¯Rb,i−Rb¯∑i∈Ia,bRa,i−Ra¯2∑i∈Ia,bRb,i−Rb¯2.In formula (1), sim (a, b) represents the similarity between users a and b; Ia,b are the common user characteristics of users a and b; Ra,i is the ith eigenvalue of user a, Rb,i is the ith eigenvalue of user b; R¯a and R¯b are the average feature scores of users a and b, respectively.The author divides users and objects into K groups and adopts the new dynamic evolution group algorithm for similar groups. Integrated filter systems are used to estimate each group of K, and it is recommended that the target user be estimated according to the level of demand from the group. The use of offline experiments using an integrated integration filter algorithm improves the authenticity of group websites.


### 3.1.2. Metabolic neural network

The Convolutional Neural Network (CNN) is one of the current sources of in-depth research, and its use in image processing has evolved and has been widely used in text interpretation [16]. In the case of the anterior neural network, the basic structure of CNN is similar to that of other neural networks, with the entry process, the bottom layer, and the release process as the data source. The CNN secret code includes 3 layers of rotation, integration, and integration. Among these, the convolution process is the most important, resulting in the calculation of the rotation of the convolution core, which contains the signal input and extractor characteristics, and the data processing in the deep case; the pool consolidation layer filters the extracted material by aggregating, reducing the residual material, and preserving the data of the core material [17]; a single layer combines all the function data completed in the top layer and sent to the output process. The CNN model can be seen in Figure 3.Figure 3
Convolutional neural network structure.Use the nonlinear features of the convolutional layer and the pooling layer in the convolutional neural network to filter the microblog text content, ensuring the security of search information. Combining the outputs of deep convolutional neural networks and weighted feature extraction, implement correlation extraction between user data and music.


## 3.1.1. Cotest approval algorithm

For the most commonly used recommendations, the key concept of integrated filters is to calculate the compatibility of users or products to complete the agreement [4]. A consumer sharing algorithm filter algorithm counts the similarities of users, creates proximity user groups, and then provides product satisfaction to the user group close to each other; the product-based cofilter approval algorithm takes into account the similarity of the products; the standard-based cofilter approval algorithm combines some smart models, train, and test data so that the user can get the desired information better when making the recommendations.Considering the consistency is important for finding potential customers or products nearby. The most common methods for calculating consistency are the Pearson method and the cosine method. Among them, Pearson’s similarity is necessary for computing data and determining different users in more detail, so model (1) is shown as follows:(1)sima,b=∑i∈Ia,bRa,i−Ra¯Rb,i−Rb¯∑i∈Ia,bRa,i−Ra¯2∑i∈Ia,bRb,i−Rb¯2.In formula (1), sim (a, b) represents the similarity between users a and b; Ia,b are the common user characteristics of users a and b; Ra,i is the ith eigenvalue of user a, Rb,i is the ith eigenvalue of user b; R¯a and R¯b are the average feature scores of users a and b, respectively.The author divides users and objects into K groups and adopts the new dynamic evolution group algorithm for similar groups. Integrated filter systems are used to estimate each group of K, and it is recommended that the target user be estimated according to the level of demand from the group. The use of offline experiments using an integrated integration filter algorithm improves the authenticity of group websites.


## 3.1.2. Metabolic neural network

The Convolutional Neural Network (CNN) is one of the current sources of in-depth research, and its use in image processing has evolved and has been widely used in text interpretation [16]. In the case of the anterior neural network, the basic structure of CNN is similar to that of other neural networks, with the entry process, the bottom layer, and the release process as the data source. The CNN secret code includes 3 layers of rotation, integration, and integration. Among these, the convolution process is the most important, resulting in the calculation of the rotation of the convolution core, which contains the signal input and extractor characteristics, and the data processing in the deep case; the pool consolidation layer filters the extracted material by aggregating, reducing the residual material, and preserving the data of the core material [17]; a single layer combines all the function data completed in the top layer and sent to the output process. The CNN model can be seen in Figure 3.Figure 3
Convolutional neural network structure.Use the nonlinear features of the convolutional layer and the pooling layer in the convolutional neural network to filter the microblog text content, ensuring the security of search information. Combining the outputs of deep convolutional neural networks and weighted feature extraction, implement correlation extraction between user data and music.


## 3.2. Construction of Travel Recommendation Model

### 3.2.1. Problem description

The goal of the author’s study is to develop travel recommendations based on a user-friendly integration algorithm, which is used to deepen the experience deep neural network because it is difficult to solve the inadequacy of deep data separate and interpret tourism information. Visitors commenting on the tourism industry have different views on the tourism process, which is divided into pros and cons. Integrating important data of travel users, behavioral data, and data points to solve the problem of data alone. The specific categories of consumer data are as follows:(1)
The basic informationPi of the user includes: name (ID), age (age), gender (sex), location (place), and other attributes, of which the name (ID) plays a unique role in identifying and does not participate in the analysis of the data, that is, Pi = {namei, agei,sexi, placei}(2)
Behavior informationSi is divided into demand (demand), interest preference (prefer), and consumption situation (consume), namely, Si = {demandi, preferi,sexi, consumei}(a)
Demand information refers to the user’s food, housing, transportation, and travel methods. Travel methods include family travel, close friend travel, solo travel, couple/couple travel, local travel, surrounding travel, and off-site travel (referring to far away from the city where you are located)(b)
Interest preferences include what kind of environment, what style of attractions, and what activities do you like(c)
Consumption situation refers to the user’s price preference, such as travel mode, ticket price, and accommodation price(3)
Contextual informationQi includes the time (time), the user’s location (location), the user’s session and interaction information (interact) on the network platform, and the session and interaction information refers to the user’s visit time and the number of visits to the scenic spot, favorite records, etc., Qi = {timei, locationi,interacti}.


### 3.2.2. Model building

The travel guide authors focus on three aspects of basic information, behavioral information, and the content of information in user data and send vectorized travel user review data matrix to the circulatory network for training to get a good and negative view of the product, performance data are then used for similar calculations [18]. The structure of the tourism permit is shown in Figure 4.Figure 4
Recommendation model structure.The basic information of tourist users is expressed as follows:(2)P=α1age+α2sex+α3place.The behavior information of tourist users is expressed as follows:(3)S=β1demand+β2prefer+β3consume.The context information of tourist users is expressed as follows:(4)Q=γ1time+γ2location+γ3interact.Therefore, the travel recommendation feature information is expressed as follows:(5)TR=αP+βS+γQ,where α is the weight of basic information, α = [α1,α2,α3]; β is the weight of behavior information, β = [β1, β2,β3], γ is the weight of context information, γ  = [γ1, γ2, γ3].The unstructured data mentioned above are processed, in which the age below 18 is recorded as 1, the age of 18 to 25 is recorded as 2, the age of 26 to 35 is recorded as 3, and the age of 36 to 45 is recorded as 4, 46 to 55 years old is recorded as 5, 56 years old and above is recorded as 6; the gender is recorded as 0 for male and 1 for female; the same location is recorded as 1, and the difference is recorded as 0; according to the order of the above-given travel modes, they are represented by 1∼7, respectively; consumption below 1000 yuan is recorded as 1, 1000∼2000 yuan is recorded as 2, 2001∼3000 yuan is recorded as 3, 3001∼4000 yuan is recorded as 4, 4001∼5000 yuan is recorded as 5, and over 5000 yuan is recorded as 6; the time is divided into spring, summer, autumn, and winter, the months from March to May are recorded as 1, the months from June to August are recorded as 2, the months from September to November are recorded as 3, and the months from December to February are recorded as 4. Use formula (1) to calculate the similarity of features between users. Since different user features have different effects on user similarity, the calculation of user similarity needs to assign corresponding weights to different features, and find similar user groups through.(6)SIMa,b=αsimaP,bP+βsimaS,bS+γsimaQ,bQ.After the vector data is input into the convolutional neural network, the inner product is calculated with the convolution kernel in the convolution layer, the convolution layer contains multiple convolution kernels, and each attribute element that constitutes the convolution kernel has its own weight coefficient and offset when the size of the processed tourism vector data is smaller than the original tourism vector data, zero can be used to fill the sample boundary. The eigenvalues of the convolutional neural network can be calculated by the following equation:(7)ci=fw⋅veci+b.In formula (7), w is the convolution weight value; veci is the word vector; b is the bias term.Select the commonly used nonlinear sigmoid activation function to complete the convolution calculation and the adjustment of the weight offset, the activation function is(8)sigmoidx=11+e−x.The data after convolution calculation enters the pooling layer, and the maximum pooling method is used to obtain the result. There are no separate weight coefficients in the pooling layer. Assuming that the tourist user data obtained by the convolutional layer isU=u1,u2,…,uS, the pooling result P is(9)P=maxU=maxu1,u2,…,us.After the pooling result reaches the fully connected layer, through the ReLU activation function, the final tourist user implicit feature information TR and the adjusted weight coefficient and deviation are obtained, and then the result is output, the calculation formula (10) is as follows:(10)TR=ReLUw⋅veci+b.ReLUx=max0,x in formula (10).The convolutional neural network classifies the positive and negative of the reviews by whether there are direct classifiers that express emotional factors in the tourist reviews, according to the proportion of positive and negative word vectors, sort from high to low, and rate it. 100% of the positive words in the comments are scored as 5 points, 99% of the positive words are scored as 4.9 points, and so on, until 1 point, and then the predicted users are recommended according to the ranking order.


## 3.2.1. Problem description

The goal of the author’s study is to develop travel recommendations based on a user-friendly integration algorithm, which is used to deepen the experience deep neural network because it is difficult to solve the inadequacy of deep data separate and interpret tourism information. Visitors commenting on the tourism industry have different views on the tourism process, which is divided into pros and cons. Integrating important data of travel users, behavioral data, and data points to solve the problem of data alone. The specific categories of consumer data are as follows:(1)
The basic informationPi of the user includes: name (ID), age (age), gender (sex), location (place), and other attributes, of which the name (ID) plays a unique role in identifying and does not participate in the analysis of the data, that is, Pi = {namei, agei,sexi, placei}(2)
Behavior informationSi is divided into demand (demand), interest preference (prefer), and consumption situation (consume), namely, Si = {demandi, preferi,sexi, consumei}(a)
Demand information refers to the user’s food, housing, transportation, and travel methods. Travel methods include family travel, close friend travel, solo travel, couple/couple travel, local travel, surrounding travel, and off-site travel (referring to far away from the city where you are located)(b)
Interest preferences include what kind of environment, what style of attractions, and what activities do you like(c)
Consumption situation refers to the user’s price preference, such as travel mode, ticket price, and accommodation price(3)
Contextual informationQi includes the time (time), the user’s location (location), the user’s session and interaction information (interact) on the network platform, and the session and interaction information refers to the user’s visit time and the number of visits to the scenic spot, favorite records, etc., Qi = {timei, locationi,interacti}.


## 3.2.2. Model building

The travel guide authors focus on three aspects of basic information, behavioral information, and the content of information in user data and send vectorized travel user review data matrix to the circulatory network for training to get a good and negative view of the product, performance data are then used for similar calculations [18]. The structure of the tourism permit is shown in Figure 4.Figure 4
Recommendation model structure.The basic information of tourist users is expressed as follows:(2)P=α1age+α2sex+α3place.The behavior information of tourist users is expressed as follows:(3)S=β1demand+β2prefer+β3consume.The context information of tourist users is expressed as follows:(4)Q=γ1time+γ2location+γ3interact.Therefore, the travel recommendation feature information is expressed as follows:(5)TR=αP+βS+γQ,where α is the weight of basic information, α = [α1,α2,α3]; β is the weight of behavior information, β = [β1, β2,β3], γ is the weight of context information, γ  = [γ1, γ2, γ3].The unstructured data mentioned above are processed, in which the age below 18 is recorded as 1, the age of 18 to 25 is recorded as 2, the age of 26 to 35 is recorded as 3, and the age of 36 to 45 is recorded as 4, 46 to 55 years old is recorded as 5, 56 years old and above is recorded as 6; the gender is recorded as 0 for male and 1 for female; the same location is recorded as 1, and the difference is recorded as 0; according to the order of the above-given travel modes, they are represented by 1∼7, respectively; consumption below 1000 yuan is recorded as 1, 1000∼2000 yuan is recorded as 2, 2001∼3000 yuan is recorded as 3, 3001∼4000 yuan is recorded as 4, 4001∼5000 yuan is recorded as 5, and over 5000 yuan is recorded as 6; the time is divided into spring, summer, autumn, and winter, the months from March to May are recorded as 1, the months from June to August are recorded as 2, the months from September to November are recorded as 3, and the months from December to February are recorded as 4. Use formula (1) to calculate the similarity of features between users. Since different user features have different effects on user similarity, the calculation of user similarity needs to assign corresponding weights to different features, and find similar user groups through.(6)SIMa,b=αsimaP,bP+βsimaS,bS+γsimaQ,bQ.After the vector data is input into the convolutional neural network, the inner product is calculated with the convolution kernel in the convolution layer, the convolution layer contains multiple convolution kernels, and each attribute element that constitutes the convolution kernel has its own weight coefficient and offset when the size of the processed tourism vector data is smaller than the original tourism vector data, zero can be used to fill the sample boundary. The eigenvalues of the convolutional neural network can be calculated by the following equation:(7)ci=fw⋅veci+b.In formula (7), w is the convolution weight value; veci is the word vector; b is the bias term.Select the commonly used nonlinear sigmoid activation function to complete the convolution calculation and the adjustment of the weight offset, the activation function is(8)sigmoidx=11+e−x.The data after convolution calculation enters the pooling layer, and the maximum pooling method is used to obtain the result. There are no separate weight coefficients in the pooling layer. Assuming that the tourist user data obtained by the convolutional layer isU=u1,u2,…,uS, the pooling result P is(9)P=maxU=maxu1,u2,…,us.After the pooling result reaches the fully connected layer, through the ReLU activation function, the final tourist user implicit feature information TR and the adjusted weight coefficient and deviation are obtained, and then the result is output, the calculation formula (10) is as follows:(10)TR=ReLUw⋅veci+b.ReLUx=max0,x in formula (10).The convolutional neural network classifies the positive and negative of the reviews by whether there are direct classifiers that express emotional factors in the tourist reviews, according to the proportion of positive and negative word vectors, sort from high to low, and rate it. 100% of the positive words in the comments are scored as 5 points, 99% of the positive words are scored as 4.9 points, and so on, until 1 point, and then the predicted users are recommended according to the ranking order.


## 4. Results and Discussion

### 4.1. Experimental Data and Processing

The author records travel information to 32 scenic spots of grades 5A and 4A with specialized software and credit scores listed on the Ctrip website. First, it removes duplicate files, blank files, and inaccurate files from useless areas in the file; I data are then placed in a sound filter, i.e., indirect markings are removed; the quoted data is then filtered and segmented byPython’s jieba keywords and stopwords. Since the union neural network has some requirements for the length of the input vector, the length of the sentence must be combined after segmenting the word. The length of the data segmentation points is usually about 50, so sentences as long as 50 are selected, and the background or pattern can be used if the length is insufficient or too long. Integration of information affects all aspects of tourism and recreation; Then use word2vec to vector the file sharing of word segmentation; send vectorized multidimensional data to the neural network to decompose operations [19].


### 4.2. Experimental Results and Analysis

To better understand the benefits of a combination of neural network and cofiltering techniques, the author compares this model with a conventional cofiltering technique, which only measures the users and products, and the experimental results can be seen in the figure [20]. Figure 5 shows that the MAE cost of the collection method is lower than that of the filtration process, which shows the sensitivity of the data in the data of tourists, status information, and assumptions can improve the accuracy of the recommendations. As the value of the data in the rating increases, the value of MAE increases, but this shows that one preference decreases altogether, which means that the quality of the structure will deteriorate when data is large more certification.Figure 5
Comparison of experimental results.


## 4.1. Experimental Data and Processing

The author records travel information to 32 scenic spots of grades 5A and 4A with specialized software and credit scores listed on the Ctrip website. First, it removes duplicate files, blank files, and inaccurate files from useless areas in the file; I data are then placed in a sound filter, i.e., indirect markings are removed; the quoted data is then filtered and segmented byPython’s jieba keywords and stopwords. Since the union neural network has some requirements for the length of the input vector, the length of the sentence must be combined after segmenting the word. The length of the data segmentation points is usually about 50, so sentences as long as 50 are selected, and the background or pattern can be used if the length is insufficient or too long. Integration of information affects all aspects of tourism and recreation; Then use word2vec to vector the file sharing of word segmentation; send vectorized multidimensional data to the neural network to decompose operations [19].


## 4.2. Experimental Results and Analysis

To better understand the benefits of a combination of neural network and cofiltering techniques, the author compares this model with a conventional cofiltering technique, which only measures the users and products, and the experimental results can be seen in the figure [20]. Figure 5 shows that the MAE cost of the collection method is lower than that of the filtration process, which shows the sensitivity of the data in the data of tourists, status information, and assumptions can improve the accuracy of the recommendations. As the value of the data in the rating increases, the value of MAE increases, but this shows that one preference decreases altogether, which means that the quality of the structure will deteriorate when data is large more certification.Figure 5
Comparison of experimental results.


## 5. Conclusion

Given the inadequacy of natural travel information, the author presents models of travel information based on the interconnectedness of neural networks. The design uses a circulatory neural network to divide the positive and negative emotions that exist in the analysis by analyzing and studying tourist information, behavioral data, information status, and identification of tourists, and counting similar tourist groups and, finally, estimated scores the difference of fact. The model has been tested on data entry into the software, and the design can improve the effectiveness of the recommendations.



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*Source: 9080818-2022-08-28.xml*