With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.
Location-based service (LBS) is a kind of value-added service provided by the combination of mobile communication network and satellite positioning system. It obtains the location information of mobile terminal such as latitude and longitude coordinate data through a set of positioning technology and provides it to the communication system, mobile users, and related users to realize various location-related services in military and transportation. As a new mobile computing service in recent years, 80% of the world’s information has time and location tags, and location services have developed to the big data stage [
The precise marketing information pushed by LBS location service can effectively tap the potential consumer demand and make a scientific and reasonable network marketing strategy based on this, which can further improve the ability of e-commerce enterprises to tap the target customers and potential customers. According to the 2017 China Mobile e-commerce Industry Research Report, the transaction scale of China’s e-commerce market reached 20.2 trillion yuan in 2016, an increase of 23.6% compared with the same period of the same period of the year. China’s e-commerce market is developing steadily. Among them, the online shopping has a good momentum of development, up from 23.3% in 2015. Huge market potential tempts all walks of life. In 2016, online shopping and B2B e-commerce of small and medium-sized enterprises and enterprises above scale still dominate the Chinese e-commerce market, while online tourism and local life service O2O emerge as bamboo shoots, accounting for 3% and 1.6% of the market, respectively. From 2015 to 2016, the proportion of online tourism market in China’s tourism market has greatly increased, the process of product informatization has accelerated, the penetration rate has further improved, the mobile online tourism market has developed rapidly, and consumer understanding and demand and experience of tourism are changing imperceptibly and pursuing higher quality of tourism. With the further development of “Internet+” information technology, the tourism industry has huge room for development, and online travel penetration will also gradually increase.
According to the Statistical Bulletin on National Economic and Social Development of 2016 issued by the State Statistical Bureau, in the whole year of 2016, the number of domestic tourists’ trips reached 4.4 billion, an increase of 11.2% over the previous year, and the income of domestic tourism increased by 15.2% to 39390 billion yuan. The number of inbound tourists reached 138.44 million, an increase of 3.5%, and international tourism revenue increased by 5.6% to $120 billion. The number of domestic residents in China has reached 135 million 130 thousand, an increase of 5.7% [
Based on this background, in the mobile e-commerce environment, based on LBS location service, research and analysis of user’s mobile behavior trajectory can extract valuable user’s mobile behavior features from a large number of mixed dynamic data and integrate the mobile behavior and consumer behavior of tourism users. Based on LBS location, services will integrate the mobile behavior and consumer behavior of tourism users, then excavate the marketing value of consumers, and timely achieve the marketing objectives of enterprises on the appropriate media, so that mobile e-commerce marketing becomes more accurate and effective. Through the research of this subject, the interests of enterprises, consumers, and media can be maximized at the same time, providing personalized products and services for mobile e-commerce, improving consumer loyalty and core competitiveness of mobile e-commerce and bringing higher profits for e-commerce enterprises [
The data of mobile terminal users’ historical consumption behavior and location movement process are recorded and stored according to the time series, forming the user’s mobile behavior trajectory data, which can be collected by multiple device terminals. The user’s mobile behavior trajectory data contain a lot of useful information. Mobile behavior trajectory can express the behavior activities of mobile users in the real world. These activities imply user’s interests, hobbies, experiences, and behavior patterns [
From the view of consulting a large number of documents, there are more papers on location services than on mobile marketing. However, most of the previous articles on location services focused on the application of natural science, such as surveying and mapping technology, network development, geographic information, and so on. In recent years, the number of cross-research articles on management science, medicine, and agriculture combined with location-based services has begun to increase, most of which are the combination of location-based services and related industries to study the application or technology development of specific industries. For example, the combination of location services and logistics technology can track the journey of packages. Combining location services with electronic maps can provide catering, entertainment, discounts, and other information within a certain range according to the location of users. Combining location service with utility technology can quickly find information such as tap water, gas explosion, and so on. The main keywords of literature research include location service technology, location service system, location service terminal, location service strategy, mobile location service, and so on. Based on location, the services industry is currently considered one of the most dynamic industries. With the rapid development of mobile Internet and Internet of Things technology, more debris time has been transferred to mobile phones, tablets, and smart products [
The characteristics of mobile marketing, such as precision, interaction, novelty, and effective delivery, are more and more concerned and recognized by various industries. This paper focuses on the main characteristics of the end-users of the tourism industry, such as frequent location movement, strong sense of sharing, and rich demand for services. First of all, the users of online travel must be mobile users who usually do not stay in a location for a long time, and a high probability of frequent location changes will produce a large number of location data. Moreover, in general, traveling users arrive at an unknown location or move in a series of unfamiliar geographical environments, which makes travel users’ demand for location-based services take precedence over personal privacy protection and enable them to obtain real-time user location information. These location data provide favorable conditions for our research. Secondly, the behavior of tourist users is quite different from that of ordinary people. In beautiful scenery and not very familiar environment, users will spontaneously produce self-awareness. Most people share location, photos, and moods through social platforms and micromessaging, and travel companies can access these social data to accurately portray users and provide accurate services for them. Third, travel users need high-quality services to obtain high-quality tourism experience. Scenic spots, accommodation, restaurants, transportation, and finance, including tour guides and their fellow travelers, are also important factors in achieving a high-quality tourism experience. These rich demands for services will generate enormous commercial value [
User’s mobile behavior trajectory is based on the path that users find frequently in the location mobile path generated by daily life. The location information generated by user’s daily behavior is acquired by GPS equipment sampling at a certain time interval, and the spatial position of moving object is represented by Euclidean space coordinates, discrete display in electronic map. Through moving sequence pattern mining, we can find the correlation among these discrete location information points and obtain the user’s moving behavior trajectory. This will provide effective support for precision marketing in mobile e-commerce [
Location information point: the position information points generated by the user’s movement can be obtained by receiving devices such as GPS of mobile terminals. Each position information point indicates a position that the user has arrived at. Suppose an independent location information point is represented as two tuple
Mobile behavior trajectory: mobile behavior trajectory can be obtained by GPS log. A mobile behavior trajectory consists of a sequence of position information points arranged in order of time attribute
Mobile behavior subtrajectory: represents the inclusion or inclusion relationship between two moving behavior trajectories. Suppose that
Support degree: the collection of all location information points of moving behavior trajectories constitute a database of mobile behavior trajectories.
Frequent Trajectories: when the support degree of the mobile behavior trajectory is greater than or equal to the minimum support threshold, the mobile behavior trajectory is called the frequent trajectory.
The user’s mobile trajectory records the user’s activity status in the real world, which can reflect the user’s behavior preferences and potential intentions to some extent. For example, if a user moves a lot every day, he may be an outdoor sports enthusiast. Through more fine-grained analysis, we can identify users’ occupations, taste habits, and so on from their frequent locations and restaurants. Therefore, mining hot spots and planning roads through multiuser mobile trajectory data sharing is an important research content of this paper.
User’s mobile behavior trajectory data refer to the sequence of changes in geographic location information caused by user’s own motion behavior in a certain time and space environment. These geographic location information points which change with time series can form a user’s mobile behavior trajectory data according to the order of occurrence time [
A trajectory formed by a change in position during the movement of a user can be sampled sequentially according to the change in position. It focuses on the information of location change when the user moves. The data obtained by this method have abundant semantic information and very detailed location change information. We can record the trajectory data of user’s mobile behavior based on position sampling by recording discrete variables. The trajectory of user’s mobile behavior can be represented by the sequence of sampling points with the change of moving object’s position, and it can be formally expressed as
The location
Trajectory can be divided into three segments according to the information of stopping point, boarding point, and alighting position, and the trajectory can be preserved according to different semantics and application segments. For example, in the prediction of travel time, it is necessary to delete the stopping point, which may be the vehicle parking or waiting for passengers, in order to measure the trajectory travel time more accurately. For some tasks that analyze the similarity between two users, it is often necessary to use the residence trajectory to reflect the user’s region of interest.
The change of mobile user’s behavior is sampled by definite time interval to form the trajectory data of user’s mobile behavior, which is called the trajectory of user’s mobile behavior sampled according to time. This kind of sampling focused on the change of location information points caused by the change of mobile user’s behavior at the same time interval, which has the characteristics of large data volume and wide range. The time-sampled trajectory data of user’s mobile behavior is formalized as follows:
The trajectory of mobile user’s mobile behavior, which is recorded by the system after the sensor event is triggered, is obtained by the event triggering [
The location
When the trajectory direction changes beyond the threshold value, we can mark the key points according to the direction changes and divide the trajectory into two segments.
According to the trajectory data of user’s movement behavior, the speed of completing the trajectory is calculated by time, and then the user’s behavior pattern is determined. Many problems still need to be considered, such as road congestion, construction, and even traffic accidents, which will affect the speed of user behavior. Vehicles travel much faster than people’s walking speed on normal roads, but in congested or abnormal roads, the speed difference between vehicles and people’s walking speed is not obvious. Therefore, the identification accuracy of trajectory velocity can only be less than 50% through time calculation [
How to realize the reasonable division of user movement behavior segments is the problem we want to study. As shown in Figure Because the trajectory data of user’s moving behavior produced by walking often produce direction change or reciprocating motion, we can divide the trajectory segments according to the change of the trajectory data direction of user’s moving behavior. In mobile scenes, people get off a bus, walk to another station to continue to take the bus process, and must pass through a section of walking, although the walking section is short, but still can show obvious direction changes. The trajectory data of mobile behavior produced by driving users do not change significantly in direction. This kind of characteristic is not affected by traffic conditions. We can train a classification model by the supervised learning method. For example, drivers do not change their direction as freely and frequently as pedestrians do, resulting in a straight line in the trajectory of the user’s movement behavior, and the direction of change is not obvious [ We can also judge user behavior patterns by the shape of user behavior trajectory data, especially the trajectory of user behavior generated by different user behavior patterns in a journey, which will have obvious morphological changes of trajectory.
Differences in movement behavior between (a) walking users and (b) driving users.
This paper studies the trajectory of user’s mobile behavior generated by online travel users during their mobile process. It contains a lot of information to express the personalized behavior of mobile users. We can use data mining methods such as classification, clustering, frequent itemsets, cycle discovery, and anomaly detection to mine and analyze the trajectory of tourism users’ mobile behavior.
Each user movement behavior trajectory can be regarded as an image data. Structural Similarity Index (SSIM) can effectively measure the similarity of two trajectories, and clustering based on the similarity index is more accurate than traditional clustering based on Euclidean distance index [
Each user movement behavior trajectory cannot be a straight line. As the precision of position coordinate recording is higher and higher, the direction of each track will change more and more, especially some subtle direction changes, and the angle of rotation can reflect the degree of change of the track direction. The division of track segments is determined according to the size of the track angle. However, if every corner is stored, it is not conducive to reduce the storage of the corner, and it is not conducive to extract it to divide the trajectory segments. Therefore, by storing the large turning point, we can discover and identify the changes of user behavior or abnormal conditions, which is also conducive to retaining the relatively stable local structure features of user trajectory segments.
We define the turning angle of user’s moving behavior trajectory as the turning angle caused by the change of direction of adjacent trajectory segments, which can reflect the movement trend of trajectory and the change of user’s behavior [
Corner of user’s mobile behavior trajectory.
As can be seen from Figure
According to the above formula, the formula for calculating the angle theta can be obtained (
This is the first step to partition the trajectory segments of user’s mobile behavior. Using formulas (
Step 1: one by one, scanning the location information point sequence in the user movement behavior track; Step 2: formula ( Step 3: formula ( Step 4: Set a threshold
Some trajectory fragments obtained by calculating rotation angle, setting threshold, and partitioning trajectory fragments can be expressed as a set of several feature attribute vectors. These feature attributes can comprehensively express the local features of a trajectory fragment and the global features of user’s moving behavior trajectory. In this section, the trajectory fragment is not simply the expression of coordinate information of the position information points, but extracts the speed, shape, position, rotation angle, acceleration, and other characteristic vectors from it. Using these eigenvectors, we can enhance the accuracy of analyzing the trajectory of user movement. We formally represent the trajectory fragment structure as follows:
Since the weights of feature vectors correspond to the eigenvectors of the trajectory segments, their values should be greater than or equal to zero, and the sum of their weights should be 1; we can generally assume that the weights of all feature vectors are equal probability, and we can take the average value of 0.25 as the weights. Similarly, we can adjust the weights of each feature vector according to the sensitivity of the feature vectors of the trajectory fragments in the actual scene. For example, when analyzing the position-sensitive trajectory fragments, we can focus on the position vectors, and the weights
According to the feature vector and its weight to complete the structural similarity comparison, mainly through the analysis of the differences between the feature vectors of the trajectory segments to complete the comparison [
The structural similarity comparison of trajectory fragments can express the structural differences of each trajectory fragment on the feature vectors. Therefore, the smaller the SSIM value of the trajectory fragments, the greater the SSIM value of the trajectory fragments. Moreover, the distance between the structural similarities of the trajectory fragments is symmetrical, that is,
According to structural similarity, the direction information, speed information, angle information, and position information are compared [ The direction vector comparison function
Comparison of track direction and rotation angle: (a) direction contrast; (b) corner contrast.
If two similar trajectory fragments have the same direction and the angle The speed vector comparison function The angle vector comparison function
Figure For the position vector comparison function
At present, we collect and store the trajectories of tourism users’ mobile behavior, cluster the typical similar trajectories from these trajectory data, analyze the behavior patterns of user’s mobile behavior trajectories, and predict the personalized needs of tourism users based on structural characteristics. Clustering analysis is to divide user behavior trajectory into several groups with high cohesion and low coupling. It requires high similarity of user behavior trajectory in the same group, and low similarity of user behavior trajectory in different groups. The goal of clustering analysis is to find out the trajectory data with the same or similar behavior patterns from the trajectories of some users’ mobile behaviors, analyze the personal preferences, consumer demands, and behavior characteristics of the trajectories of tourism users’ mobile behaviors, and accurately determine the similarity between trajectories of users’ mobile behaviors. At the same time, the trajectories of users’ mobile behaviors with high similarity are gathered into one class [
Most of the online travel users are in the same scenic spot, similar routes to carry out activities, and most of the resulting mobile behavior trajectory data have local similarity and global dissimilarity. It is difficult to find the personalized characteristics of tourism users by analyzing the complex and large number of users’ mobile behavior trajectories and effectively extract users. The analysis of a part of the mobile behavior trajectory is more conducive to finding the information contained in it [
On the basis of obtaining the feature vector distance of user’s moving behavior trajectory segment, the trajectory segment with high similarity is analyzed, and then the clustering algorithm is used to complete the clustering of user’s moving behavior trajectory. By comparing the structural similarity between the trajectory segments and other trajectory segments which are not on the same trajectory, a number of
The steps of clustering algorithm based on structural similarity are given in Algorithm
Step 1: first calculate the corner Step 2: according to the corner threshold Step 3: calculate the distance between the trajectory feature vectors based on the weight of the trajectory segment feature vectors. Step 4: calculate the Step 5: the distance clustering segment is centred on the similarity track segment Step 6: initialize clustering ID and track segment clustering markers. Step 7: traverse the trajectory fragments, find the core clustering and set the clustering ID, and then add the pointers of these trajectory fragments to a new node in the index tree. Step 8: determine whether the set center of
From the analysis of the above algorithms, it can be seen that, in the trajectory segment clustering algorithm based on structural similarity, it is very important to determine the threshold value of
Through repeated verification of the algorithm, in the data analysis of trajectory of travel user’s movement behavior, the value of
By effectively identifying the location information points in the trajectory data of users’ mobile behavior, the feature vectors of the trajectory segments can be extracted, and the semantics of these location information points can be expressed as the route, the scenic spots, and the behavior patterns of an online travel user in the past period of time. By clustering and analyzing the trajectory fragments containing location information points, we can find that the traveling users have a longer time in a certain area, which can be interpreted as the tourist users have a higher degree of interest in a certain scenic spot. Semantic expression is a popular tourist spot with longer stay time for online travel users. In practical scenarios, many traveling users will visit the same or similar scenic spots. From the trajectory of users’ mobile behavior and the region of interest, traveling users with similar trajectory and the same region of interest can predict their similar preferences or similar behavior characteristics. These regions of interest frequently stayed by tourist users will appear as overlapping regions in the trajectory of user’s mobile behavior. If these overlapping regions are found, the popular scenic spots concerned by tourist users can be found and the users who like these scenic spots can be clustered. And then, dig out the other characteristics of these users to complete the personalized tourist attractions recommendation of similar tourists. We extract the feature parameters of these overlapping areas, such as overlap time and overlap times, which can reflect the similarity between the traveling users. It can identify the tourist attractions that the tourist users are interested in during the mobile process and recommend the most likely popular tourist inventory for other tourist users who have a higher similarity with their user’s mobile behavior trajectory, so as to tap the potential preferences of the tourist users [
In the process of analyzing mobile user behavior trajectory data with structured eigenvectors, it is not difficult to find that the moving speed of user behavior trajectory is not the same in different time periods, or it is slower in a certain time period, or it is faster in a certain time period. Figure
Mobile speed of user behavior trajectory.
As shown in Figure
Two-dimensional trajectory analysis of user movement behavior.
For example, when a tourist visits a scenic spot, he or she forms a trajectory of the user’s movement behavior. Three troughs appear in the trajectory, indicating that the user may have experienced three scenic spots or rest areas, of which the first trough has a shorter experience. It shows that tourists spend less time visiting the first scenic spot, travel faster, continue to move forward at a faster speed after the tour, and spend a little more time watching the tide or taking pictures when they meet the scenic spot of interest. So tourists will slow down, move in a more fixed area, and travel at a slower speed, thus appearing the second trough period, after the tourists continue to move forward; when the formation of the third trajectory speed reached trough state, semantic expression may have two situations. The first is that the tourists reach a certain degree of fatigue or meet a rest area, stop and rest; the second is that the tourists arrive at a well-known scenic spot, gather more tourists, people will stay in a certain position, waiting for sightseeing and photography, moving slowly, and almost stop. The above two semantics can be distinguished by whether the location in the electronic map is a resting area or a scenic spot. However, in the actual tourist attractions, the situation may be more complicated. For example, a tourist is an outdoor sports enthusiast who has good physical strength and likes natural scenery. Because of his fast moving speed, there is little difference between the wave crest and trough of the waveform trajectory formed by the speed and distance. Although his tour speed is fast and his stay time is short, the location he stays in is still the area of interest. In this way, moving objects with similar frequencies in the velocity-distance waveform can be found not only in the known hot spots of the users, but also in the scenic spots that the potential users may be interested in, even in the preferences, occupations, and personality characteristics of the tourist users. It helps to gather tourists with similar preferences and similar personalities to achieve the confluence module [
Popular scenic spots refer to scenic spots with long staying time after arrival [
Because GPS receiving equipment receives satellite signals in vast and open areas with high intensity and good positioning effect, satellite signals in indoor areas will be shielded by the wall, resulting in weak positioning signal and reduced positioning accuracy [
Input parameters: user movement behavior trajectory Output parameters: collection of popular scenic spots HR. Step 1: for ( Step 2: Step 3: Step 4: HR = {}; C = {}; CO = false; Step 5: for ( Step 6: if ( Step 7: Step 8: if (not CO) then CO = true; Step 9: else Step 10: else if (CO) then Step 11: HR = {C}; CO = false; C = {} /∗search outdoor attractions area HRII∗/ Step 12: if ( Step 13: Step 14: if (not CO) then CO = true; Step 15: else if (CO) then Step 16: last Index = look Ahead (MT, Step 17: if (last Index ≤ Step 18: for Step 19: Step 20: Step 21: else if (time ( Step 22: HÈ = { Step 23: return HR;
Because the location information points sampled by GPS are affected by the factors of region, space, and weather, it is easy to have inaccurate positioning or interruption of positioning [
The outdoor and indoor scenic spots are divided according to the location information of popular scenic spots. The density-based hot spot discovery algorithm is used to retrieve two different types of user residence areas, outdoor and indoor, in the trajectory of user’s mobile behavior, and define them as hot spots [
With the combination of Bluetooth, WIFI, and other RF communication technologies and mobile terminal devices, mobile point-to-point communication environment has been derived, and many different research topics have also emerged. This research talks about the relationship between mobile attraction recommendation system and social software from the perspective of mobile social software and completes interaction through user comment sharing in mobile point-to-point environment. In this paper, an interactive system of tourism comment information sharing and social networking software is established, which includes three functions: recommendation, reunion, and comment. It is used to explore the interaction between users in mobile point-to-point environment. The preliminary test has been completed in this paper. The experimental results show that the recommendation, convergence, and comment functions of the system can provide precise services for users and provide a basis for further research on the wide application of user behavior trajectory in precise marketing.
This paper focuses on the problem of information sharing and social interaction of tourism mobile recommendation system in mobile point-to-point environment. The system mainly includes three functions: recommendation, convergence, and information sharing. In the recommendation function section, we assume that users will leave comments and other information after visiting a scenic spot. When other users meet with them, they can exchange comments through RF communication technology. These comments are calculated by system algorithm to recommend scenic spots that meet users’ interests. In addition, users can also actively send requests to other people to join the information and find similar interests around users to visit a scenic spot. Of course, users can also actively share location, comments, traffic conditions, tourist density, and other related information.
In order to enable the interaction and sharing of information between remote users, the relay mode under mobile point-to-point can be adopted. Every user in the system plays the role of information transmission, that is to say, each user’s mobile terminal is a relay node for information transmission and constantly transfers the information they have mastered to the users at a long distance.
The mobile peer-to-peer environment mainly transmits information through the direct transmission between peer-to-peer users and the relay mode assisted by the third party. Using this feature, the system proposed in this paper mainly provides three services: recommendation, convergence, and review. First of all, the main purpose of recommendation service is to recommend scenic spots similar to user’s interests to users through user’s evaluation information, so that users can have a reference direction for the next destination in the journey, so that users can travel more smoothly. Secondly, the convergence service allows users to initiate a convening activity, gather other interested users around, visit the scenic spots together, or buy specialty goods together, through group buying to get a better price, or to strive for preferential services. Thirdly, evaluation services are divided into general information and specific information. General information is simply the transmission of personal information and specific information, so that the use of convergence services conveys the convening activities of the department of the offensive, through short messages, and the expression of personal information is incompatible; specific information is only for convening activities issued information. To provide the above services, the system architecture is presented in Figure
Mobile point-to-point tourism recommendation system architecture.
In the process of recommendation, the recommendation module first calculates Pearson correlation coefficient, calculates the first 20 items of scoring data which are most similar to users, and then runs the subsequent recommendation algorithm. Finally, the user’s predicted value of a certain scenic spot is obtained by weighted average of these scoring data and similarity, and five scenic spots with the highest predicted value are recommended to users for reference.
J2ME can be chosen as the development platform of the system, and Bluetooth technology is the basic wireless transmission technology commonly available in mobile terminals. Therefore, it is feasible to use Bluetooth technology as a transmission tool. In order to expand the scope of information transmission, WIFI wireless network can also be considered as a transmission medium, which can effectively solve the problem of short transmission distance and unstable signal of Bluetooth.
Advanced GPS devices enable people to record their location histories with GPS trajectories. The trajectory of users’ mobile behavior means to a certain extent that a person’s behavior and interests are related to their outdoor activities, so we can understand the users and their locations and their correlation according to these trajectories. This information enables accurate travel recommendations and helps people to understand a strange city efficiently and with high quality. By measuring the similarity of different user location histories, the similarity between users can be estimated and personalized friend recommendation can be realized. The user stereoscopic user portrait can be portrayed through the integration of user movement behavior trajectory and social information. This paper takes the trajectory data of tourism users’ mobile behavior as the research object and constructs the tourism precise marketing model. In the process of obtaining the trajectory of user movement, the characteristics of mobile user behavior track data are taken into account. The sensitivity of various features in the trajectory analysis process is adjusted by weight. The structured feature vectors and popular scenic spots discovery methods of user’s mobile behavior trajectory are fully studied by clustering and collaborative filtering techniques, which lay a foundation for constructing the application model of tourism precision marketing.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research work was supported by the Ministry of Education Humanities and Social Sciences Planning Fund Project (No. 18YJAZH128) and Research Project of Harbin University of Commerce (No. 18XN022).