# Personalized Recommendation of Rural Tourism Based on Traffic Classification and User Data Analysis

Authors: Wei Bi; Guangming Wang; Meijuan Zhang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Recently, With the development of global agricultural industrialization, modern agriculture has the functions of improving the quality of the ecological environment and providing people with the functions of sightseeing, leisure, and vacation. How to take the user as the center and combine the user’s personalized characteristics to offer the rural tourism products they need has become a research problem with real-world application value and challenge. In this context, this study develops an intelligent recommendation model by extensively analyzing the contents of rural tourism information platforms and product recommendation factors, as well as a rough set algorithm and traffic classification. To minimize the attributes of rural tourism product information and extract the core attribute, an attribute reduction approach based on a different matrix is implemented. Moreover, user interest similarity is computed and ranked to recommend rural tourist products. In addition, a personalized tourism attraction recommendation model is presented based on geographic area and period. The model achieved the highest average accuracy of 0.87%. The relevant experimental test results reveal that the system can provide accurate recommendations and services for rural tourism products.


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## Body

## 1. Introduction

With the acceleration of industrialization and urbanization and the negative effects brought about by it, urban residents will yearn for a peaceful rural life and a beautiful rural environment, and rural tourism has emerged to meet this demand [1]. Rural tourism is a tourism activity that integrates viewing, inspection, learning, participation, entertainment, shopping, and vacation with agricultural cultural landscape, agricultural ecological environment, and traditional ethnic customs as resources [2]. Rural tourism, as an emerging business in rural areas, provides farmers with a new source of income, creates jobs, and encourages rural industrial structure adjustment. The advent of rural tourism has aided in the rehabilitation of the rural economy and the realization of the integrated development of the urban and rural economies and societies [3].Rural communities around the world have faced falling economic activity, restructuring of traditional agricultural sectors, population aging and out-migration of higher-educated youth, and small-town and village viability for decades. The adoption of tourism as an alternative strategy of attaining economic and social rehabilitation has resulted from the search for rural regeneration.Numerous research studies have been conducted on the concept of rural tourism. Up to now, scholars have many opinions on its definition, but they have yet to reach a consensus [4, 5]. The first is that the European Union and the OECD have defined the concept of rural tourism [6]. They believe that tourism activities in rural regions are rural tourism and that the scale of such activities is often limited, and that locality is the foundation for rural tourism development [7]. Barke et al. [8] believe that the concept of rural tourism is very broad. Due to this feature, rural tourism can be summarized as tourism activities that take place in rural areas. This activity can be in any form and has been recognized by everyone. The author in [9] proposed that tourism activities carried out in remote rural areas relying on regional traditional culture and unique customs are called rural tourism. Bramwell and Lane [10] thought that rural tourism is a multilevel tourism activity. Tourism activities are not only based on agriculture but also include nature tourism and ecotourism. They also include fishing, sports, adventure, and health tourism, educational tourism, and some region-specific folk-related tourism activities. Hajibabaet al. [11] examined the application of the rural tourism industry in information systems from the perspective of social sciences in terms of tourism and travel decision making. According to Zhang [12], rural tourism in China has advanced rapidly in the twenty-first century, and it is now at a period of flourishing development. Liu et al. [13] investigated the roles of the Chinese central and local governments in the growth of rural tourism in China and analyzed the secondary data from government records as well as primary data from interviews with local government personnel and inhabitants in a Chinese rural destination. The author in [14] examined rural tourism and concluded that the demand for rural tourism continues to rise. To avoid industrialization and urbanization, a rising number of people seek out natural scenery and authentic folk customs in the quiet and serene countryside, supporting tourism development in rural areas.How to take the user as the center and combine the user’s personalized characteristics to recommend the rural tourism products they need has become a research topic with application value and challenge. However, the current user’s personalized interest characteristics and the attributes of rural tourism products are still relatively vague, which makes it difficult for us to accurately recommend rural tourism products. In this context, this study designed an intelligent recommendation model by examining the contents of rural tourism information platforms, product recommendation criteria, a rough set algorithm, and traffic categorization. An attribute reduction approach based on a difference matrix is used to reduce the attributes of rural tourism product information and extract the core attribute.The rest of the paper is organized as follows. Section2 provides an overview of the related rural tourism technologies. Section 3 describes the proposed rural tourism model. Section 4 illustrates different results, and the conclusion is presented in Section 5.


## 2. Related Technologies

### 2.1. Rural Tourism

Rural revitalization is not only a strategy to promote sustainable rural development in developing countries but also an inevitable trend towards global urbanization. Tourism decision making can be defined as a process in which travelers understand and evaluate potential destination destinations through various channels and methods based on clear value demands and select tourism destinations through analysis and comparison. Huybers et al. [15] constructed a decision-making model of tourism consumers and analyzed the influence of tourism destination image on tourists’ choices. In addition, it lists a variety of factors that affect tourists’ decision-making behavior and summarizes and categorizes these influencing factors into two factors, namely, guiding factors and driving factors. In this process, tourists’ perception of cost performance is extremely important, and most tourists expect to maximize satisfaction based on cost minimization [16]. Rural tourism decision-making is that in order to meet their own travel purposes, tourists use a variety of methods to collect relevant tourism information in various aspects of rural tourism destinations and then organize and compare them so as to choose a plan that meets their own travel purposes and motivations. [17].


### 2.2. Personalized Recommendations

Residents’ travel decisions are affected by multiple internal (economic income, leisure time, etc.) and external (media publicity) factors. Tourism decision-making behavior generally follows the principle of benefit maximization, that is, travelers always choose to maximize their travel benefits (cultivating sentiments, broadening their horizons, maintaining good feelings, etc.) with minimal capital consumption and time consumption before traveling [18]. It is found that the research on the intelligent recommendation of tourism information in academic circles began at the beginning of this century [19]. Figure 1 describes the basic flow of user personalization model creation.Figure 1
Schematic diagram of the general model of personalized recommendation system.The main implementation process is to obtain hidden valuable data through data mining techniques such as association rule mode, classification mode, regression mode, and clustering mode [20, 21]. Use the methods of induction, classification, extraction, etc. to analyze the information of different scenic spots and then push it to different users in a targeted manner to meet their individual needs. Commonly used recommendation algorithms include rule-based filtering, content-based filtering, collaborative filtering, and hybrid filtering [22]. Rule-based filtering is a technique for narrowing down a flow of documents to only those that deal with specific themes and highlighting parts of the texts that were used to choose the documents in rural tourism. A content-based recommender system aims to predict a user’s features or behavior based on the features of an item to which he or she responds favorably. In collaborative filtering, the features of the goods are not required for collaboration. A feature vector or embedding describes each user and item. The hybrid filter is used to combine the three filtering mechanisms [22].


## 2.1. Rural Tourism

Rural revitalization is not only a strategy to promote sustainable rural development in developing countries but also an inevitable trend towards global urbanization. Tourism decision making can be defined as a process in which travelers understand and evaluate potential destination destinations through various channels and methods based on clear value demands and select tourism destinations through analysis and comparison. Huybers et al. [15] constructed a decision-making model of tourism consumers and analyzed the influence of tourism destination image on tourists’ choices. In addition, it lists a variety of factors that affect tourists’ decision-making behavior and summarizes and categorizes these influencing factors into two factors, namely, guiding factors and driving factors. In this process, tourists’ perception of cost performance is extremely important, and most tourists expect to maximize satisfaction based on cost minimization [16]. Rural tourism decision-making is that in order to meet their own travel purposes, tourists use a variety of methods to collect relevant tourism information in various aspects of rural tourism destinations and then organize and compare them so as to choose a plan that meets their own travel purposes and motivations. [17].


## 2.2. Personalized Recommendations

Residents’ travel decisions are affected by multiple internal (economic income, leisure time, etc.) and external (media publicity) factors. Tourism decision-making behavior generally follows the principle of benefit maximization, that is, travelers always choose to maximize their travel benefits (cultivating sentiments, broadening their horizons, maintaining good feelings, etc.) with minimal capital consumption and time consumption before traveling [18]. It is found that the research on the intelligent recommendation of tourism information in academic circles began at the beginning of this century [19]. Figure 1 describes the basic flow of user personalization model creation.Figure 1
Schematic diagram of the general model of personalized recommendation system.The main implementation process is to obtain hidden valuable data through data mining techniques such as association rule mode, classification mode, regression mode, and clustering mode [20, 21]. Use the methods of induction, classification, extraction, etc. to analyze the information of different scenic spots and then push it to different users in a targeted manner to meet their individual needs. Commonly used recommendation algorithms include rule-based filtering, content-based filtering, collaborative filtering, and hybrid filtering [22]. Rule-based filtering is a technique for narrowing down a flow of documents to only those that deal with specific themes and highlighting parts of the texts that were used to choose the documents in rural tourism. A content-based recommender system aims to predict a user’s features or behavior based on the features of an item to which he or she responds favorably. In collaborative filtering, the features of the goods are not required for collaboration. A feature vector or embedding describes each user and item. The hybrid filter is used to combine the three filtering mechanisms [22].


## 3. Rural Tourism Model Based on Personalized Recommendation

Implementing personalized recommendations for users is the ultimate goal of intelligent recommendation systems, and the construction of a good user ontology model is the premise of implementing intelligent recommendations [23]. The intelligent recommendation can only be implemented once a good user model has been built. In this study, the user ontology model can acquire relevant data about the user’s interests in rural tourism and then combine that data with the user’s personal information to create a model that our computer can identify.


### 3.1. Construction of User Ontology Model of Rural Tourism Platform

User personalized interest information is the data source for user model establishment, and the format and quality of the acquired data directly affect the quality of user model construction [24]. The display feedback technology mainly records the user’s preference through the user’s evaluation of the corresponding product and this technology is mainly realized through the user’s evaluation and praise of the product, which requires the user to actively participate and give positive evaluation. However, this process takes up a lot of the user’s time. It is difficult to get the user’s personal interest information when the user’s participation is low. Some of the values in the user’s personal information are continuous, such as the user’s age and income status. To compare and calculate the similarity of users, it is necessary to discretize these continuous data. Under the premise of simplifying the information system and not changing the consistency of the system, the focus of data discretization is to retain as much original information as possible. Usually, researchers use a definite quantitative description when performing discretization processing. Using the breakpoint value as a demarcation point may result in significant differences in data on both sides of the breakpoint, which is not in line with reality; the quantitative description cannot adequately describe the abstract processing of the data, and it does not use promotion. Figure 2 shows the basic flow of user personalization model development.Figure 2
The basic process of creating a user personalization model.The user model contains two types of information, one of which is static, such as the user's personal basic information, and the other is dynamic, which changes over time [25]. In this study, we employed the dynamic model. In this model, the user’s interest changes with the changes of the environment and psychological factors. The commonly used dynamic user model update methods are the forgetting function method and the time window method. The forgetting function method is similar to the forgetting law of memory [26, 27]. If there is no external stimulus, the user’s interest in a certain thing will decrease with the passage of time. In this study, we consider the user interest model under two conditions of user interest and access frequency.


### 3.2. Rural Tourism Recommendation Based on Rough Sets

With the rapid growth in the amount of information available on the Internet, people are unable to rapidly find the information they are looking for in the vast information ocean, resulting in information overload [28, 29]. One solution to the problem of information overload is to provide users with information, products, and other services based on their needs, personalized information, and personal interest traits [30]. The recommendation system generally consists of three parts including the user modeling part, recommendation object modeling part, and recommendation algorithm part. The specific process of the recommendation system to achieve recommendation is as follows:Match the personalized information in the user model with the corresponding feature information of the model of the object to be recommended.Use algorithm screening to recommend the information that the screened-out users may like to users.(1)Ikj=∑i=1mwikrij.Attribute reduction is a very important concept in rough set theory and plays an important role in the study of rough sets. Rough set theory can be used to classify data that is imprecise or noisy to find structural links [24]. It applies to characteristics with discrete values. As a result, continuous-valued properties must be discretized before being used. At present, scholars at home and abroad have defined various reductions of rough sets, and based on these research studies, various attribute reduction algorithms have been obtained.(2)upj=11+Ipj−1−12,Δwik=2ηrijwkpukj21−∑i=1mwikrij∑i=1mwikrij3δpj.For an information system IS, there are usually multiple reductions.(3)Δwkp=2ηukj2ukj1−∑k=1lwkpukj∑k=1lwkpukj3Mupj−upj.The search for all reductions is an NP-hard problem (nondeterministic polynomial-time hardness). NP-hard problems are a group of problems called NP-hardness. How to improve the algorithm for obtaining multiple reductions in IS or to obtain better reduction algorithms has also become a research hotspot in rough set theory.(4)Ipj=∑k=1lwkpukj.There are two important problems to be solved in attribute reduction, one is the evaluation of the importance of attributes, and the other is the search method of the information system. The importance evaluation determines the goal of deoptimizing the information system, and the search for the information system determines the optimal search method.(5)ukj=11+Ikj−1−12,Ej=12upj−Mupj2.Through attribute reduction of the discretized decision table, condition attributes with strong classification ability are obtained. Based on the definition of rough decision confidence, it can be known that if the classified text is placed in a confidence interval, the judgment structure for classifying a text can be obtained. Continuously calculate the confidence level of the text to be classified for the decision table and unify it to get a complete text classification result.


## 3.1. Construction of User Ontology Model of Rural Tourism Platform

User personalized interest information is the data source for user model establishment, and the format and quality of the acquired data directly affect the quality of user model construction [24]. The display feedback technology mainly records the user’s preference through the user’s evaluation of the corresponding product and this technology is mainly realized through the user’s evaluation and praise of the product, which requires the user to actively participate and give positive evaluation. However, this process takes up a lot of the user’s time. It is difficult to get the user’s personal interest information when the user’s participation is low. Some of the values in the user’s personal information are continuous, such as the user’s age and income status. To compare and calculate the similarity of users, it is necessary to discretize these continuous data. Under the premise of simplifying the information system and not changing the consistency of the system, the focus of data discretization is to retain as much original information as possible. Usually, researchers use a definite quantitative description when performing discretization processing. Using the breakpoint value as a demarcation point may result in significant differences in data on both sides of the breakpoint, which is not in line with reality; the quantitative description cannot adequately describe the abstract processing of the data, and it does not use promotion. Figure 2 shows the basic flow of user personalization model development.Figure 2
The basic process of creating a user personalization model.The user model contains two types of information, one of which is static, such as the user's personal basic information, and the other is dynamic, which changes over time [25]. In this study, we employed the dynamic model. In this model, the user’s interest changes with the changes of the environment and psychological factors. The commonly used dynamic user model update methods are the forgetting function method and the time window method. The forgetting function method is similar to the forgetting law of memory [26, 27]. If there is no external stimulus, the user’s interest in a certain thing will decrease with the passage of time. In this study, we consider the user interest model under two conditions of user interest and access frequency.


## 3.2. Rural Tourism Recommendation Based on Rough Sets

With the rapid growth in the amount of information available on the Internet, people are unable to rapidly find the information they are looking for in the vast information ocean, resulting in information overload [28, 29]. One solution to the problem of information overload is to provide users with information, products, and other services based on their needs, personalized information, and personal interest traits [30]. The recommendation system generally consists of three parts including the user modeling part, recommendation object modeling part, and recommendation algorithm part. The specific process of the recommendation system to achieve recommendation is as follows:Match the personalized information in the user model with the corresponding feature information of the model of the object to be recommended.Use algorithm screening to recommend the information that the screened-out users may like to users.(1)Ikj=∑i=1mwikrij.Attribute reduction is a very important concept in rough set theory and plays an important role in the study of rough sets. Rough set theory can be used to classify data that is imprecise or noisy to find structural links [24]. It applies to characteristics with discrete values. As a result, continuous-valued properties must be discretized before being used. At present, scholars at home and abroad have defined various reductions of rough sets, and based on these research studies, various attribute reduction algorithms have been obtained.(2)upj=11+Ipj−1−12,Δwik=2ηrijwkpukj21−∑i=1mwikrij∑i=1mwikrij3δpj.For an information system IS, there are usually multiple reductions.(3)Δwkp=2ηukj2ukj1−∑k=1lwkpukj∑k=1lwkpukj3Mupj−upj.The search for all reductions is an NP-hard problem (nondeterministic polynomial-time hardness). NP-hard problems are a group of problems called NP-hardness. How to improve the algorithm for obtaining multiple reductions in IS or to obtain better reduction algorithms has also become a research hotspot in rough set theory.(4)Ipj=∑k=1lwkpukj.There are two important problems to be solved in attribute reduction, one is the evaluation of the importance of attributes, and the other is the search method of the information system. The importance evaluation determines the goal of deoptimizing the information system, and the search for the information system determines the optimal search method.(5)ukj=11+Ikj−1−12,Ej=12upj−Mupj2.Through attribute reduction of the discretized decision table, condition attributes with strong classification ability are obtained. Based on the definition of rough decision confidence, it can be known that if the classified text is placed in a confidence interval, the judgment structure for classifying a text can be obtained. Continuously calculate the confidence level of the text to be classified for the decision table and unify it to get a complete text classification result.


## 4. Rural Tourism Recommendation Case Verification

### 4.1. Technical Performance Test

This study randomly selects 500 users who have browsed and ordered the corresponding rural tourism products from the platform database. To validate the performance of the recommendation model based on a rough set, the model was extensively evaluated. The model achieved the highest average accuracy of 0.87%. The specific recommendation result accuracy distribution is shown in Table1.Table 1
The distribution of the accuracy of the recommended results.
Recommendation accuracyNo. of usersProportion (%)0.6–0.781.60.7–0.84590.8–0.928957.8≥0.917635.2In the process of recommending products that may be of interest to users through their browsing records and personal information on rural tourism platforms, the reduced key attributes play a decisive role. According to the user’s 30 records of information, the attributes of the user’s personalized interest ontology are reduced, and the key attributes of season, cost, special food, special culture, and scenic spot type are finally obtained. In this study, the weights of five key attributes in the personalized interest model of the two users extracted are shown in Table2.Table 2
Weights of five key attributes in the personalized interest model.
UserSeasonCostSpeciality cuisineCharacteristic cultureScenic spot typeSum of weightsA_max0.0940.2870.1200.1120.1310.744B_max0.1210.0230.1890.1290.1350.597The advantages of user-based collaborative filtering recommendations are obvious. First, it classifies users according to their evaluations of items. Such classification is easy to understand and reasonable so that the recommendation results obtained by mutual assistance among similar users are more accurate. Secondly, through the recommendation of similar users of the same kind, it can bring cross-type strange discoveries to the current users, dig out the potential new interests of the target users, and expand the user’s horizons.


### 4.2. Technical Performance Comparison

To compare the performance of the proposed method and other methods, we carried out a comparative experiment on the time effect of attribute reduction in the decision table. We compared the results of cosine similarity, related similarity, and modified cosine similarity. Figure3 shows the comparison of similarity calculation methods in traditional recommendation algorithms.Figure 3
Comparison of similarity calculation methods in traditional recommendation algorithms.Through comparative experiments, it can be found that on the same platform and for the same dataset, the mean absolute error (MAE) of the three similarity calculations decreases to become stable as the number of nearest neighbors gradually increases. When the number of neighbors is less than 80, the cosine similarity is the largest, and then the deviation of the cosine similarity becomes the largest, when the number of nearest neighbors increases, so it is inferred to apply the similarity calculation method to the recommendation algorithm. It will improve the recommendation accuracy. Therefore, in subsequent experiments, the Pearson correlation similarity algorithm is used to calculate the similarity between items in the user cluster to find the nearest neighbors of the target user. The Pearson coefficient of correlation is a measure of linear correlation between two sets of data. Figure4 shows the analysis results of the time effect of attribute reduction in the decision table.Figure 4
Analysis of time effect of attribute reduction in the decision table.As can be seen in Figure4, for the same decision attribute text, the reduction time is also different for different text numbers. With the continuous increase of the number of samples, the reduction time of the algorithm for the same sample gradually increases. When the number of samples is 300, the proposed algorithm has little difference in the reduction time, and the time is about 20–40S. However, when the number of samples is 800, the gap between the proposed algorithm and other algorithms gradually increases. The reduction time of the RDAR algorithm, ME algorithm, and MDAR algorithm is about 80S, while the reduction time of the proposed algorithm is only 50S. As the number continues to increase, the time advantage is gradually reflected.Finally, in a user-based system filtering system, all users can benefit from the feedback evaluation of neighbor users; as long as each user contributes to the system, the system can maintain the best performance, which is role consistency. The role-consistent performance promotes the healthy development of collaborative filtering systems, enabling the system to maintain effective recommendations.


## 4.1. Technical Performance Test

This study randomly selects 500 users who have browsed and ordered the corresponding rural tourism products from the platform database. To validate the performance of the recommendation model based on a rough set, the model was extensively evaluated. The model achieved the highest average accuracy of 0.87%. The specific recommendation result accuracy distribution is shown in Table1.Table 1
The distribution of the accuracy of the recommended results.
Recommendation accuracyNo. of usersProportion (%)0.6–0.781.60.7–0.84590.8–0.928957.8≥0.917635.2In the process of recommending products that may be of interest to users through their browsing records and personal information on rural tourism platforms, the reduced key attributes play a decisive role. According to the user’s 30 records of information, the attributes of the user’s personalized interest ontology are reduced, and the key attributes of season, cost, special food, special culture, and scenic spot type are finally obtained. In this study, the weights of five key attributes in the personalized interest model of the two users extracted are shown in Table2.Table 2
Weights of five key attributes in the personalized interest model.
UserSeasonCostSpeciality cuisineCharacteristic cultureScenic spot typeSum of weightsA_max0.0940.2870.1200.1120.1310.744B_max0.1210.0230.1890.1290.1350.597The advantages of user-based collaborative filtering recommendations are obvious. First, it classifies users according to their evaluations of items. Such classification is easy to understand and reasonable so that the recommendation results obtained by mutual assistance among similar users are more accurate. Secondly, through the recommendation of similar users of the same kind, it can bring cross-type strange discoveries to the current users, dig out the potential new interests of the target users, and expand the user’s horizons.


## 4.2. Technical Performance Comparison

To compare the performance of the proposed method and other methods, we carried out a comparative experiment on the time effect of attribute reduction in the decision table. We compared the results of cosine similarity, related similarity, and modified cosine similarity. Figure3 shows the comparison of similarity calculation methods in traditional recommendation algorithms.Figure 3
Comparison of similarity calculation methods in traditional recommendation algorithms.Through comparative experiments, it can be found that on the same platform and for the same dataset, the mean absolute error (MAE) of the three similarity calculations decreases to become stable as the number of nearest neighbors gradually increases. When the number of neighbors is less than 80, the cosine similarity is the largest, and then the deviation of the cosine similarity becomes the largest, when the number of nearest neighbors increases, so it is inferred to apply the similarity calculation method to the recommendation algorithm. It will improve the recommendation accuracy. Therefore, in subsequent experiments, the Pearson correlation similarity algorithm is used to calculate the similarity between items in the user cluster to find the nearest neighbors of the target user. The Pearson coefficient of correlation is a measure of linear correlation between two sets of data. Figure4 shows the analysis results of the time effect of attribute reduction in the decision table.Figure 4
Analysis of time effect of attribute reduction in the decision table.As can be seen in Figure4, for the same decision attribute text, the reduction time is also different for different text numbers. With the continuous increase of the number of samples, the reduction time of the algorithm for the same sample gradually increases. When the number of samples is 300, the proposed algorithm has little difference in the reduction time, and the time is about 20–40S. However, when the number of samples is 800, the gap between the proposed algorithm and other algorithms gradually increases. The reduction time of the RDAR algorithm, ME algorithm, and MDAR algorithm is about 80S, while the reduction time of the proposed algorithm is only 50S. As the number continues to increase, the time advantage is gradually reflected.Finally, in a user-based system filtering system, all users can benefit from the feedback evaluation of neighbor users; as long as each user contributes to the system, the system can maintain the best performance, which is role consistency. The role-consistent performance promotes the healthy development of collaborative filtering systems, enabling the system to maintain effective recommendations.


## 5. Conclusion

The acceptance of tourism as an alternative means of achieving economic, agricultural, and social rehabilitation has resulted from the quest for rural regeneration. Modern agriculture can improve the quality of the natural environment and provide people with the functions of tourism, sightseeing, leisure, and vacation. In this study, an intelligent recommendation model is developed by systematically analyzing the characteristics of rural tourism information platforms and factors of product recommendation, combined with a rough set algorithm and traffic classification. An attribute reduction approach based on a difference matrix is used to reduce the attributes of rural tourism product information and extract the core attribute. To recommend rural tourist products, user interest similarity is also computed and ranked. A modified tourism attraction suggestion model based on geographic location and time period is also offered. The system can provide correct recommendations and services for rural tourism products, according to the appropriate experimental test findings. How to set a reasonable value to control the frequency of updating the real-time interest model without affecting the quality of the recommendation is a problem that needs to be explored in future work.



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