# Analysis of the Influence of Hybrid Personalized Recommendation Algorithm on the Employment Tendency of Hotel Management Major

Authors: Li Liang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

As the pillar industry of tourism, the hotel industry plays an important role in the present and future development of tourism. Therefore, the industry’s demand for hotel management professionals is increasing. In this paper, the mixed personalized recommendation algorithm is used to carry out descriptive statistical analysis on the results, and the characteristics of the employment tendency of college students majoring in hotel management are preliminarily understood. In addition, the influence of human capital and social capital on college students’ employment tendency is discussed. The results show that, through experiments, the hybrid personalized recommendation algorithm proposed in this paper is compared with two traditional collaborative filtering recommendation algorithms based on users, Pearson similarity and cosine similarity. The algorithm in this paper has smaller MAE, which shows that the algorithm proposed in this paper has higher recommendation quality. Through this research, it is helpful to have a more comprehensive understanding of the actual situation and characteristics of the employment tendency of hotel management major college students, and it also provides the policy basis for the government and relevant departments to make employment decisions.


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## Body

## 1. Introduction

The willingness and proportion of hotel students to choose their own profession is not optimistic all the time, and the students’ cognition and change of hotel profession have an important influence on career choice [1, 2]. The employment tendency of hotel management college students “non-hotel employment” and “learning for non-use” is a waste of professional advantage resources accumulated by the university in recent years. For universities, it is a loss of educational resources. For hotels, it is very likely to increase their professional training costs. For the society, it is not conducive to the rational allocation of human resources [3]. Studies have found that [4, 5], after internship in hotels, college students’ love for the hotel industry declines, and their confidence in choosing to work in hotels decreases. Therefore, for the employment-oriented universities, on the one hand, it is necessary to understand the characteristics of hotel talent demand, train students in a targeted way, so that students can be competent in hotel service and management, and win the favor of the hotel. On the other hand, we should fully understand students’ hotel employment willingness and analyze the influencing factors of students’ choice of hotel employment, so as to carry out targeted vocational awareness education and ultimately improve students’ hotel employment rate.The principle of recommendation is that the system calculates the items through algorithms and then recommends the items to users who may be interested in it. The effectiveness of the recommendation system depends on the algorithm used by the system [6]. Personalized recommendation system is a personal customized recommendation based on each user’s own historical data, which makes every user participate in the whole system construction. The recommendation system has been widely cited in music websites, news websites, and many other websites [7]. The recommendation system is used to recommend students’ employment. The system can establish a student employment interest model by mining students’ employment intentions, career interests, school performance, and other information. Combining with the employment data of previous graduates, it can recommend suitable employment units for graduates, guide them to make effective employment preparations, reduce the waste of time and energy, and improve the employment rate and satisfaction [8]. Therefore, combining the relevant theoretical knowledge of recommendation system with the characteristics of college students’ employment and constantly studying and perfecting the theory and application of college students’ employment recommendation system have great development prospects.There are also two unreasonable types of managers in the hotel industry. One is that they are skilled in operation but have a low degree, have been promoted after working at the grass-roots level for a long time, and have rich work experience, but they are hard to manage because of the lack of professional management knowledge. The other is a management degree, but not a hotel major. It is unfamiliar to the hotel industry with strong application, so it is difficult to go deep into the front line, effectively manage the hotel, and improve the economic benefits of the hotel [9]. For individuals, knowing their employment tendency and making the right career choices on this basis can make individuals give full play to employers, understand students’ employment tendency, and then put them in the most suitable employment positions and use the allocation function of human resources to help individuals achieve personal growth and self-development and benefit others. This paper tries to understand the characteristics of college students’ employment tendency and the main factors that affect college students’ job selection through the questionnaire of college students’ employment tendency further developed on the basis of predecessors. Based on the hybrid personalized recommendation algorithm, the theoretical system of college students’ employment tendency and its educational guidance is preliminarily established, and then the theory of career planning and development in human resource management is supplemented.


## 2. Related Work

Literature [10] holds that intention refers to the intention and tendency that an individual has consciously planned to pursue or not pursue a specific goal to some extent. Literature [11] also puts forward that intention is the antecedent of behavior. Literature [12] verified that internship job satisfaction, internship job stress, and students’ intention to choose jobs in hotel industry were significantly positively correlated and negatively correlated and discussed the regression model of students’ intention to choose jobs in hotel industry. In [13] through research, it is found that the arrangement of internship in schools, the intervention of tutors, the humanistic care of hotels, and the contribution of students’ job value all affect students’ professional employment willingness to varying degrees. Literature [14] holds that there is a significant positive correlation between internship satisfaction and students’ intention to choose jobs in hotel industry. Literature [15] points out that freshmen’s occupational cognition is characterized by vague cognition, paranoid job selection, blind optimism, overdependence, and negative taboo. Literature [16] holds that students in different majors have different job-hunting values, and the change and stability of this value orientation are also different in different grades, which shows the influence of majors and grades on job-hunting values. This paper summarizes the career value orientation and education of Chinese youth and analyzes and studies the structure, types, characteristics, and influencing factors of career values. Literature [17] holds that social factors, family factors, school factors, and their own factors, respectively, constitute the external and internal factors that affect contemporary college students’ view of job selection. Literature [18] found that there are five main factors affecting college students’ job selection in this area, namely, regional factors, salary factors, career development factors, ownership factors, and career cognition factors.As an important means of information filtering, recommendation system is one of the most effective ways to solve the problem of information overload. In the face of massive information, recommendation system can help users to filter information and locate the information they want accurately, which greatly reduces the cost of information acquisition and meets the individual needs of users. In [19] through open management of users’ web bookmarks, vector space method and social filtering are used to generate information recommendation. Subsequently, literature [20] also proposed a recommendation algorithm based on multiagent hybrid intelligent filtering. Literature [21] lists recommendation engine, cloud computing, and search engine as the important strategic planning and development direction of Internet in the future. Literature [22] puts forward the student employment recommendation based on the project recommendation algorithm and displays the recommendation structure with visualization technology. Literature [23] proposes design and implementation of employment recommendation system for college students based on context awareness. Literature [24] takes the data set of users’ rating standards for movies as public resources and provides them to scholars and experts in recommendation systems around the world. Literature [25] proposed a new user recommendation algorithm based on Mahout to solve the problems of difficulty and inefficiency in recommending new users without historical scores or behavior data in the background of big data and implemented distributed computing in the framework of Map Reduce to improve the ability of the algorithm to handle data scale. Literature [26] puts forward the direction that the recommendation system should break through in the current era of big data and makes use of the advantages of big data processing platform to process and utilize a large number of user feedback data and social network information, so as to further improve the quality of the recommendation list of the recommendation system and the satisfaction degree of users to the recommendation system. Literature [27] proposes an improved collaborative filtering recommendation algorithm, which combines factors such as the category of items and the user’s interest degree, calculates the category distance between items by constructing a matrix of item categories, and measures the user’s interest degree in a novel way.


## 3. Research Method

### 3.1. Analysis of Characteristics of Employment Tendency

The purpose of setting up hotel management major in colleges and universities in China is to encourage the cultivation of senior professionals engaged in hotel management to meet the needs of the industry. However, the actual situation is that the number of students who voluntarily apply for the hotel management major is very small every year. Therefore, it is a subject worthy of our serious study to analyze the employment tendency of hotel management majors, explore the factors that affect their employment tendency, and put forward active coping strategies to effectively reverse this situation.Employment tendency can be discussed from two aspects of outlook on life and values. From the perspective of outlook on life, the choice of occupation is a level of individual’s view on life. Therefore, this view on occupation is one of the elements of outlook on life. From the point of view of values, values are expressed as people’s basic views when they know and evaluate whether everything in their lives can meet their own needs. Then, as an important carrier for people to form certain social relations, occupation is particularly important to meet the needs of the subject.Specifically, under certain historical conditions, it is the basic value position, value attitude, and basic value tendency of the job-choosing subject when dealing with various value contradictions, value conflicts, and value relationships based on their own values. The value tendency of choosing a job includes such factors as value goal, regional choice, industry choice, etc., which reflects the subject’s understanding level of the objective thing of career and his own needs and interests.If interns have expectations for their future career in the hotel industry before the internship, they will be relatively positive towards the hotel internship, which will also positively affect the interns’ work and attitude, thus affecting their satisfaction with the hotel internship and their future career direction. Good “hardware” conditions directly affect employees’ satisfaction and career choices. At the same time, it should also be noted that, for college students who have just entered internship positions, if they can get better guidance from their superiors and help from colleagues, they will be able to integrate into their job roles more quickly, stimulate their work enthusiasm, identify with the management system and culture of enterprises, and enhance their willingness to stay.First of all, by observing and interviewing graduates majoring in hotel management, we can understand some hot issues about employment that they are most concerned about in the process of employment and talk about more hot topics. We also conducted in-depth interviews with some students to understand their evaluation of some influencing factors and the reasons for ranking. Secondly, refer to the empirical research literature on college students’ employment, list the frequently occurring and representative questions in the questionnaire of these literature materials, and screen these questions based on your own personal experience. Thirdly, preliminarily design the first draft of the questionnaire according to these selected questions and discuss and exchange opinions with teachers and classmates on the preliminarily designed questionnaire and modify the questionnaire. Finally, according to the results of the presurvey, modify the questionnaire until it is finally determined.By finding the problem, we, the school and the government, can solve it in a targeted way. As the supplementary content does not involve the two core concepts of this paper: human capital and social capital, but involves the employment tendency, the questionnaire adds these two contents. The questions of the questionnaire are closed, and the single choice is the main choice. All data were statistically analyzed by SPSS16 software.


### 3.2. Recommendation Model of Students’ Employment Characteristics

The university employment market is characterized by relatively stable recruitment units and employment groups, with little change in every unit that leaves school for recruitment. Generally speaking, the units that recruit students in the same school and department will continue to recruit every year, and the number of new enterprises that come to recruit is small. Therefore, fresh graduates can refer to the employment choices of students who have basically the same conditions as themselves. Every year, the school will leave a lot of employment data. Mining the employment data of previous graduates can provide employment guidance for fresh graduates.Personalized recommendation refers to recommending resources that users may like through the calculation of recommendation algorithm according to the user’s identity information, historical records, and item information. Because the school has accumulated a large amount of personal data of students every year, including performance in school, employment destination, career mapping information, etc., we adopt a collaborative filtering algorithm based on users, which can calculate the similarity of students through the preference data of students’ scores and performance in employment, find the graduates who are similar to the recent graduates, and recommend the employment units of the most similar graduates to the recent graduates.Based on collaborative filtering of items, when predicting users’ ratings, the sparseness of rating data is mainly manifested in that users rarely have ratings for itemsi and j at the same time. On this basis, we need to find the union of users who have ratings for items i and j, respectively.(1)U=Ui∪Uj.U represents the collection of users, Ui represents all users who score item i, and Uj represents all users who score item j. Filled prediction scores are made for items in the user set that are not scored for item i,j respectively.If student A is similar to student C, then the unit that student C likes may also be liked by student A. The unit that student C chooses for employment is the employment reference for student A. Because of the similarity between student C and student A, student A has a greater chance to enter the employment unit of student C, as shown in Figure1, which is similar to the employment recommendation principle of students.Figure 1
Schematic diagram of employment recommendation for similar students.Let then-dimensional feature vectors of student u,v be U=Xu1,Xu2,…,Xun and V=Xv1,Xv2,…,Xvn, respectively, and measure the distance between students according to Euclidean distance; then the feature distance of student U,V is as follows:(2)duv=∑knxuk−xvk2.The similarity of students’ characteristics can be obtained through the transformation of distance and similarity coefficient; see the following formula(3)Simuv=11+duv=11+∑knxuk−xvk2.However, the influence of students’ characteristics on employment is not the same. Some characteristics have great influence, while others have little influence. In the past student employment recommendation system, the problem of students’ characteristic weight has not been considered. In this paper, when constructing the employment recommendation model, students’ characteristic weight is added, which makes cluster analysis more in line with the reality. The similarity of students’ characteristics is obtained through the transformation of distance and similarity coefficient:(4)Simuv=11+duv=11+∑knθkxuk−xvk2.If the vertices of the selected edge appear in the same connected component, it means that the loop is formed. At this time, the vertices forming the loop are taken out separately as a set and deleted from the initial vertex set. Then repeat the above method in the remaining vertices until all vertices are assigned to connected components. In this way, the distance between students in each connected subgraph is the smallest, and its similarity is the largest.


### 3.3. Hybrid Personalized Recommendation Model

In the information age, there are no more than active and passive ways to obtain data, but in this information overload age, it is extremely important for information filtering. Recommendation algorithm can well find the information that people crave, and collaborative filtering recommendation algorithm brings personalized service to people [18]. Single recommendation algorithm has its own disadvantages, and hybrid recommendation algorithm will avoid the disadvantages of single algorithm and make the recommendation effect better [20].Each recommendation technology has its advantages and disadvantages, and its effects are different in different tasks. For example, the neighborhood-based collaborative filtering algorithm only considers user behavior, which has the disadvantages of sparse data and cold start, while the content-based recommendation depends on the characteristics of items. All the basic algorithms can be further improved by mixing with other methods. Combine other recommendation technologies in the operation process of a single recommendation algorithm to improve the performance of the algorithm. Common hybrid models include the fusion of collaborative filtering and content-based recommendation, using similarity in content to search for similar neighbors instead of similar neighbors calculated by scoring data, and finally generating recommendation list.The hybrid personalized recommendation algorithm designed in this paper takes the result of user preference as the input of the algorithm and mixes three algorithms (collaborative filtering algorithm, matrix decomposition algorithm, and graph-based diffusion algorithm) in parallel. The three algorithms generate recommendation candidate sets, respectively, and then mix and filter the candidate sets to get the final recommendation list. The specific design of the model is shown in Figure2.Figure 2
Design of hybrid personalized recommendation algorithm.If there are multiple recommendation methods mixed, the recommendation results will continue to be input into the next algorithm, and the candidate results will be selected step by step during the sending process, and finally a relatively accurate recommendation result will be obtained.In the waterfall mixed model system, the recommendation algorithm with fast operation time and low discrimination granularity is often put in front and then transferred to the advanced algorithm in turn. The former algorithm can be used to filter out many items that are basically not recommended, while the latter complex algorithm can handle less data that are more likely to be recommended, thus saving system resources.For the lifting tree model, assuming there is aK tree, the predicted result is(5)y^i=∑k=1Kfkxi.Its objective function is defined as(6)obj=∑i=1mlyi,y^i+∑k=1KΩfk,where l is the loss function of the training set, and Ωfk is the complexity of the tree, which can include the number of leaf nodes of the tree, the depth of the tree, the l2 norm of leaf nodes, etc.After obtaining the nearest neighbor setU of the target user u, the following formula can be used to predict the score value of the target user u for item i [19], which is recorded as(7)Pu,i=Ru¯+∑uk∈Usimu,uk×Ruk,i−Ruk¯∑uk∈Usimu,uk.In the formula,Ruk,i represents the nonempty score of user uk on item i, and Ruk¯ is the average score of the intersection of user uk and user’s item scores. Ru¯ is the average score of user u in all project collections.According to the current actual scene, under different circumstances, we choose to use different recommendation algorithms and switch between them, and the recommendation system runs a variety of recommendation algorithms at the same time. Each algorithm will get some results. Then, the results obtained by different algorithms are sorted and combined according to a certain rule and finally displayed to users. This processing method makes the items recommended to users more comprehensive and rich in categories. At the same time, attaching different recommendation reasons to different items can often bring more surprises to users.


## 3.1. Analysis of Characteristics of Employment Tendency

The purpose of setting up hotel management major in colleges and universities in China is to encourage the cultivation of senior professionals engaged in hotel management to meet the needs of the industry. However, the actual situation is that the number of students who voluntarily apply for the hotel management major is very small every year. Therefore, it is a subject worthy of our serious study to analyze the employment tendency of hotel management majors, explore the factors that affect their employment tendency, and put forward active coping strategies to effectively reverse this situation.Employment tendency can be discussed from two aspects of outlook on life and values. From the perspective of outlook on life, the choice of occupation is a level of individual’s view on life. Therefore, this view on occupation is one of the elements of outlook on life. From the point of view of values, values are expressed as people’s basic views when they know and evaluate whether everything in their lives can meet their own needs. Then, as an important carrier for people to form certain social relations, occupation is particularly important to meet the needs of the subject.Specifically, under certain historical conditions, it is the basic value position, value attitude, and basic value tendency of the job-choosing subject when dealing with various value contradictions, value conflicts, and value relationships based on their own values. The value tendency of choosing a job includes such factors as value goal, regional choice, industry choice, etc., which reflects the subject’s understanding level of the objective thing of career and his own needs and interests.If interns have expectations for their future career in the hotel industry before the internship, they will be relatively positive towards the hotel internship, which will also positively affect the interns’ work and attitude, thus affecting their satisfaction with the hotel internship and their future career direction. Good “hardware” conditions directly affect employees’ satisfaction and career choices. At the same time, it should also be noted that, for college students who have just entered internship positions, if they can get better guidance from their superiors and help from colleagues, they will be able to integrate into their job roles more quickly, stimulate their work enthusiasm, identify with the management system and culture of enterprises, and enhance their willingness to stay.First of all, by observing and interviewing graduates majoring in hotel management, we can understand some hot issues about employment that they are most concerned about in the process of employment and talk about more hot topics. We also conducted in-depth interviews with some students to understand their evaluation of some influencing factors and the reasons for ranking. Secondly, refer to the empirical research literature on college students’ employment, list the frequently occurring and representative questions in the questionnaire of these literature materials, and screen these questions based on your own personal experience. Thirdly, preliminarily design the first draft of the questionnaire according to these selected questions and discuss and exchange opinions with teachers and classmates on the preliminarily designed questionnaire and modify the questionnaire. Finally, according to the results of the presurvey, modify the questionnaire until it is finally determined.By finding the problem, we, the school and the government, can solve it in a targeted way. As the supplementary content does not involve the two core concepts of this paper: human capital and social capital, but involves the employment tendency, the questionnaire adds these two contents. The questions of the questionnaire are closed, and the single choice is the main choice. All data were statistically analyzed by SPSS16 software.


## 3.2. Recommendation Model of Students’ Employment Characteristics

The university employment market is characterized by relatively stable recruitment units and employment groups, with little change in every unit that leaves school for recruitment. Generally speaking, the units that recruit students in the same school and department will continue to recruit every year, and the number of new enterprises that come to recruit is small. Therefore, fresh graduates can refer to the employment choices of students who have basically the same conditions as themselves. Every year, the school will leave a lot of employment data. Mining the employment data of previous graduates can provide employment guidance for fresh graduates.Personalized recommendation refers to recommending resources that users may like through the calculation of recommendation algorithm according to the user’s identity information, historical records, and item information. Because the school has accumulated a large amount of personal data of students every year, including performance in school, employment destination, career mapping information, etc., we adopt a collaborative filtering algorithm based on users, which can calculate the similarity of students through the preference data of students’ scores and performance in employment, find the graduates who are similar to the recent graduates, and recommend the employment units of the most similar graduates to the recent graduates.Based on collaborative filtering of items, when predicting users’ ratings, the sparseness of rating data is mainly manifested in that users rarely have ratings for itemsi and j at the same time. On this basis, we need to find the union of users who have ratings for items i and j, respectively.(1)U=Ui∪Uj.U represents the collection of users, Ui represents all users who score item i, and Uj represents all users who score item j. Filled prediction scores are made for items in the user set that are not scored for item i,j respectively.If student A is similar to student C, then the unit that student C likes may also be liked by student A. The unit that student C chooses for employment is the employment reference for student A. Because of the similarity between student C and student A, student A has a greater chance to enter the employment unit of student C, as shown in Figure1, which is similar to the employment recommendation principle of students.Figure 1
Schematic diagram of employment recommendation for similar students.Let then-dimensional feature vectors of student u,v be U=Xu1,Xu2,…,Xun and V=Xv1,Xv2,…,Xvn, respectively, and measure the distance between students according to Euclidean distance; then the feature distance of student U,V is as follows:(2)duv=∑knxuk−xvk2.The similarity of students’ characteristics can be obtained through the transformation of distance and similarity coefficient; see the following formula(3)Simuv=11+duv=11+∑knxuk−xvk2.However, the influence of students’ characteristics on employment is not the same. Some characteristics have great influence, while others have little influence. In the past student employment recommendation system, the problem of students’ characteristic weight has not been considered. In this paper, when constructing the employment recommendation model, students’ characteristic weight is added, which makes cluster analysis more in line with the reality. The similarity of students’ characteristics is obtained through the transformation of distance and similarity coefficient:(4)Simuv=11+duv=11+∑knθkxuk−xvk2.If the vertices of the selected edge appear in the same connected component, it means that the loop is formed. At this time, the vertices forming the loop are taken out separately as a set and deleted from the initial vertex set. Then repeat the above method in the remaining vertices until all vertices are assigned to connected components. In this way, the distance between students in each connected subgraph is the smallest, and its similarity is the largest.


## 3.3. Hybrid Personalized Recommendation Model

In the information age, there are no more than active and passive ways to obtain data, but in this information overload age, it is extremely important for information filtering. Recommendation algorithm can well find the information that people crave, and collaborative filtering recommendation algorithm brings personalized service to people [18]. Single recommendation algorithm has its own disadvantages, and hybrid recommendation algorithm will avoid the disadvantages of single algorithm and make the recommendation effect better [20].Each recommendation technology has its advantages and disadvantages, and its effects are different in different tasks. For example, the neighborhood-based collaborative filtering algorithm only considers user behavior, which has the disadvantages of sparse data and cold start, while the content-based recommendation depends on the characteristics of items. All the basic algorithms can be further improved by mixing with other methods. Combine other recommendation technologies in the operation process of a single recommendation algorithm to improve the performance of the algorithm. Common hybrid models include the fusion of collaborative filtering and content-based recommendation, using similarity in content to search for similar neighbors instead of similar neighbors calculated by scoring data, and finally generating recommendation list.The hybrid personalized recommendation algorithm designed in this paper takes the result of user preference as the input of the algorithm and mixes three algorithms (collaborative filtering algorithm, matrix decomposition algorithm, and graph-based diffusion algorithm) in parallel. The three algorithms generate recommendation candidate sets, respectively, and then mix and filter the candidate sets to get the final recommendation list. The specific design of the model is shown in Figure2.Figure 2
Design of hybrid personalized recommendation algorithm.If there are multiple recommendation methods mixed, the recommendation results will continue to be input into the next algorithm, and the candidate results will be selected step by step during the sending process, and finally a relatively accurate recommendation result will be obtained.In the waterfall mixed model system, the recommendation algorithm with fast operation time and low discrimination granularity is often put in front and then transferred to the advanced algorithm in turn. The former algorithm can be used to filter out many items that are basically not recommended, while the latter complex algorithm can handle less data that are more likely to be recommended, thus saving system resources.For the lifting tree model, assuming there is aK tree, the predicted result is(5)y^i=∑k=1Kfkxi.Its objective function is defined as(6)obj=∑i=1mlyi,y^i+∑k=1KΩfk,where l is the loss function of the training set, and Ωfk is the complexity of the tree, which can include the number of leaf nodes of the tree, the depth of the tree, the l2 norm of leaf nodes, etc.After obtaining the nearest neighbor setU of the target user u, the following formula can be used to predict the score value of the target user u for item i [19], which is recorded as(7)Pu,i=Ru¯+∑uk∈Usimu,uk×Ruk,i−Ruk¯∑uk∈Usimu,uk.In the formula,Ruk,i represents the nonempty score of user uk on item i, and Ruk¯ is the average score of the intersection of user uk and user’s item scores. Ru¯ is the average score of user u in all project collections.According to the current actual scene, under different circumstances, we choose to use different recommendation algorithms and switch between them, and the recommendation system runs a variety of recommendation algorithms at the same time. Each algorithm will get some results. Then, the results obtained by different algorithms are sorted and combined according to a certain rule and finally displayed to users. This processing method makes the items recommended to users more comprehensive and rich in categories. At the same time, attaching different recommendation reasons to different items can often bring more surprises to users.


## 4. Results Analysis and Discussion

College students’ human capital is the cornerstone of college students’ employment tendency. It is the knowledge and labor ability that college students form through various educational investments before employment and can bring various benefits to college students and society in the future. College students with career planning have determined their ideal job types very early, while others are at a loss about their future career and do not know what they will do in the future. Figure3 is a statistical analysis of the ideal job types of college students majoring in hotel management.Figure 3
The ideal job type survey statistics.As can be seen from Figure3, generally speaking, 77% of the students choose the three categories of state organs, state-owned enterprises, and foreign-funded enterprises. There are more girls than boys in the choice of state organs and private enterprises. There are 2.1% more girls than boys in this type of choice of state organs. There are more boys than girls in these three types of jobs: public institutions, state-owned enterprises, and foreign-funded enterprises. In the choice of institutions, boys outnumber girls by 1.3%.When choosing a job, some value the “benefits” such as economic income and welfare benefits, but some value the “fame” of social reputation, and some value personal interests, development prospects, and professional counterparts. The following is a statistical analysis of the most important factors in choosing a job for college students majoring in hotel management. The results are shown in Figure4.Figure 4
Investigation and statistics of employment factors.The influencing factors of employment can reflect the employment values of college graduates. The employment of college graduates is comprehensively influenced by many factors, including individuals, society, families, and employers. Because the object of this survey only includes graduates majoring in hotel management, the design of this question option only considers the graduates themselves.It can be seen that nearly half of college students pay the most attention to the development prospects when choosing a job; that is to say, college students think that the development prospects of their chosen unit and their own development prospects in this unit and industry are very important, and many people regard their chosen job as the cornerstone and development platform of their future career. It reflects that college students majoring in hotel management pursue self-realization, find the best combination of self-value and social value, and are eager to contribute to society while realizing self-value.Employment guidance is to learn vocational knowledge, understand the employment policy situation, and help students choose the way of further study or employment according to their own characteristics and specific conditions. It plays a very important role in personal employment tendency. The following is an analysis of college students majoring in hotel management’s views on employment guidance, and the statistical results are shown in Figure5.Figure 5
Importance of employment guidance.It can be seen that college students do not have a clear understanding of the current employment guidance work in universities. During the interview, some students answered that although the school has specialized institutions, it has not provided substantial help to college students in choosing jobs, and there is less education on college students’ values in choosing jobs. Some students reflected that the current guidance for graduates is generally organizing recruitment activities, issuing letters and announcements, etc., while others are rarely involved, which shows that the guidance for university career selection is still not perfect.The purpose of school employment guidance is to guide individuals to examine their career choices and life development from a broader perspective and, on this premise, evaluate various development resources owned by individuals, learn to choose and plan, and realize the most active and effective interaction between individuals and society by promoting the independent and orderly development of individuals. Only through the interaction and communication between the two sides can the employment of college students be more guaranteed.When choosing the attribute to predict the employment classification of students, it is necessary to choose the attribute that has influence on the classification target. When the information gain rate of an attribute is less than 0.01, it has no influence on the target classification. Therefore, when choosing the employment classification attribute, it is necessary to choose the attribute whose information gain rate is greater than 0.01. Therefore, the sum of information gains of all attributes can be taken as a whole to calculate the proportion of information gains, which is the weight of each feature attribute. The calculation results are shown in Figure6.Figure 6
The weight of each attribute information.It can be seen that when students are employed, the employment situation of different majors is different, and it is difficult for students to find jobs in different degrees, followed by students’ places of origin, which will affect students’ employment choices and recruitment choices of enterprises, while the political outlook has less influence on employment. From the data analysis, it can be seen that the weight results obtained by objective calculation are more in line with the actual situation, so it is a good result to calculate the proportion of employment influencing factors by the method of information gain rate.Using MATLAB, the feature data of all previous students are clustered into four categories, and the clustering center is constantly adjusted until the clustering center does not change, and the number of students in each category, the clustering center of each category, and the distance from each category to the clustering center are calculated. After continuously adjusting the cluster center, the final cluster center is obtained after calculation, as shown in Figure7.Figure 7
Final clustering center.Whether the clustering effect is effective or not can also be judged according to the toe distance between classes and the distance between classes of each cluster. If each cluster is above one, it means that the clustering effect is not good, the clustering among the clustering centers is not big, the gap between the classes is not big, and the clustering effect is not achieved. On the contrary, if the clustering centers are scattered and differ greatly, it means that the differences between the classes are big and the clustering effect is good.Next, we compare the hybrid recommendation and similarity calculation methods of the two algorithms, Pearson similarity and cosine similarity, which are two traditional collaborative filtering recommendation algorithms based on users, and verify the MAE (mean absolute error) of the algorithms. The experimental results are shown in Figure8.Figure 8
Algorithm comparison.From the experimental results in Figure8, we can see that the MAE of our algorithm is obviously lower than that of the similarity calculation method for different neighbor numbers under the same training set and test set ratio. Pearson similarity and cosine similarity are two traditional collaborative filtering recommendation algorithms based on users, so our algorithm has better recommendation accuracy.In order to verify the influence of data set sparsity on the algorithm in this paper, and also to verify the stability of the algorithm, we selected three different sparsity scoring matrices, the sparsityS of which is S=0.913,S=0.908,S=0.847, respectively, and the experimental results are shown in Figure 9.Figure 9
MAE value of different algorithms underS.From the experimental results in Figure9, it can be seen that the value of MAE decreases with the decrease of sparsity S, which indicates that sparsity becomes lower and the recommendation accuracy of this algorithm is improving. At the same time, when the nearest neighbor number of attribute domain is 16, the recommendation error of this algorithm is the lowest.Interest is the most important factor that restrains the employment tendency of hotel management majors, while professional knowledge, professional practice, and professional counterpart are the factors that promote the employment tendency of hotel management majors. Therefore, by improving the professional level of college students majoring in hotel management, we can stimulate their interest in this major, thus promoting their employment tendency in this industry, using various teaching methods, such as scenario exercises, role-playing, problem discussion, etc., to stimulate the interest of hotel management majors in this major.In order to improve the treatment of college students to a certain extent, it is inevitable for college students to add their own educational cost when measuring their own value. The hotel can give college students some special care under appropriate and reasonable circumstances, such as establishing a job allowance system according to their academic qualifications and recognizing their academic qualifications, so as to show the difference between this special group and ordinary employees, show the hotel’s emphasis on college students, and meet their psychological needs for being valued.Different from other industries, the hotel industry pays more attention to operational skills. As a student in this industry, it is impossible to take a management position at the beginning of work. It is necessary to start from the grass-roots level and be psychologically prepared for hardship. It is believed that, with the adjustment of China’s economic structure and the accelerated development of the service industry, the social prejudice against the service industry will gradually decrease, and a more open concept of employment will be formed.


## 5. Conclusion

According to the survey, most college students majoring in hotel management are satisfied and recognized for their majors, because there are various courses. However, as far as future employment is concerned, less than 40% of the students are willing to work in hotels because of their professional counterparts. There is a correlation between human capital and employment tendency, and there is also a correlation between social capital and employment tendency. College students should constantly improve themselves and strive to improve the core competitiveness of employment and human capital. This method does not simply give recommendations according to the scoring matrix but uses the item attribute information and also considers the influence of time on users’ interests. At the same time, the problems of data sparsity and cold start are solved. Through experiments, it can be seen that this method has higher prediction and recommendation accuracy than the traditional similarity filtering method.The employment recommendation model proposed in this paper and the prototype of the employment recommendation system developed in this paper can provide some reference and help for college students’ employment recommendation. At the same time, it is instructive to promote the research and application of employment recommendation system for college students majoring in hotel management. However, the research and application of university employment system is a complex issue, which needs to be explored and tried continuously.



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*Source: 6886076-2022-06-30.xml*