CN107798557B - Electronic device, service place recommendation method based on LBS data and storage medium - Google Patents
Electronic device, service place recommendation method based on LBS data and storage medium Download PDFInfo
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Abstract
The invention discloses a service place recommendation method based on LBS data. The method comprises the following steps: acquiring LBS data corresponding to each user within preset time from a predetermined database, and performing cluster analysis on the acquired LBS data to respectively analyze at least one behavior track data corresponding to each user; analyzing the behavior track data of each user to obtain the similarity among the users; clustering the users based on the similarity among the users to obtain different user groups; analyzing LBS data of all users in the same user group to analyze preferred behavior tracks of all users in the user group, and sending service place recommendation instructions aiming at the behavior tracks of the users in the user group to a predetermined terminal based on the analyzed preferred behavior tracks of all the users. The method and the system can provide more accurate recommendation for the user and improve the accuracy of LBS data application.
Description
Technical Field
The invention relates to the field of internet data processing, in particular to an electronic device, a service place recommendation method based on LBS data and a storage medium.
Background
With the development of the internet, the interests of users are more and more extensive, and with the change of the environment and living standard of users, the demands of users are also changing. Therefore, how to better understand and analyze the behavior of the user becomes crucial to providing the user with services to his needs.
Currently, the application of Location Based Services (LBS) data is limited to analyzing and judging geographic information around a Location where a user is located according to the obtained current LBS data of the mobile terminal user, and recommending corresponding surrounding commodities to the user by combining the geographic information around the user. For example, find the current geographic location of the mobile phone user is a certain street in Shanghai city, then find the name and address of the service places such as hotels, cinemas, libraries, gas stations, etc. within 1 km of the current location of the mobile phone user in the fixed square km range of the street, and recommend to the user. The recommendation method is very convenient, but in terms of application, the requirements of the user cannot be completely met, and the recommended service place is not necessarily needed by the user urgently. Therefore, there are certain limitations.
Disclosure of Invention
In view of this, the invention provides an electronic device, a service location recommendation method based on LBS data, and a storage medium, which can analyze massive LBS data to obtain a behavior trajectory preferred by a user, and perform service location recommendation based on the behavior trajectory preferred by the user, thereby improving recommendation accuracy and improving limitation of application of the LBS data.
First, to achieve the above object, the present invention provides an electronic device, which includes a memory, a processor and a service location recommendation system based on LBS data stored on the memory and operable on the processor, wherein the service location recommendation system based on LBS data when executed by the processor implements the following steps:
A. if service place recommendation needs to be performed on each predetermined user, or if a service place recommendation request sent by a terminal of a predetermined user is received, acquiring LBS data corresponding to each predetermined user within a preset time from a predetermined database, and performing cluster analysis on the acquired LBS data of each user by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user;
B. analyzing the behavior trajectory data of each user according to a predetermined similarity analysis rule to obtain the similarity between each user;
C. based on the similarity among the users, clustering the users by using a predetermined second clustering algorithm to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups;
D. analyzing LBS data of all users in a user group to which each predetermined user belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of each predetermined user, and sending a recommendation instruction of a service place on the preferred behavior track of each predetermined user to a predetermined terminal, or analyzing LBS data of all users in the user group to which the user who requests the recommendation belongs by using the predetermined service place recommendation model, determining the preferred behavior track of the user, and sending the recommendation instruction of the service place on the preferred behavior track of the user to the predetermined terminal.
Further, the predetermined database comprises mobile positioning data acquired from positioning service systems of all mobile terminal users and provided service data related to positions, the LBS data comprises geographic position information data and various types of service data related to the geographic position information data, and the behavior trajectory data comprises travel type trajectory data and/or entertainment type trajectory data; the travel type track data includes a travel time and a travel identifier, and the entertainment type track data includes an entertainment time and an address identifier.
Further, the predetermined first clustering algorithm comprises a density-based clustering algorithm; the predetermined similarity analysis rule comprises a cosine included angle similarity method, a Euclidean distance measurement method or a Pearson correlation coefficient method; the predetermined second clustering algorithm includes a primitive-based objective function clustering algorithm, a density-based clustering algorithm, or a hierarchy-based clustering algorithm.
Further, the predetermined service place recommendation model is a collaborative filtering recommendation model.
Further, the LBS data-based service place recommendation system when executed by the processor further implements the steps of:
and B, tracking LBS data of the user receiving the recommendation instruction, analyzing the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior track of the user, and if the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior track of the user is less than or equal to a preset matching threshold value, repeatedly executing the step B and the step C.
In addition, in order to achieve the above object, the present invention further provides a service location recommendation method based on LBS data, the method comprising the steps of:
s1, if service place recommendation needs to be carried out on each predetermined user, or if a service place recommendation request sent by a predetermined user terminal is received, obtaining LBS data corresponding to each predetermined user within preset time from a predetermined database, and carrying out cluster analysis on the obtained LBS data of each user by using a predetermined first clustering algorithm so as to respectively analyze at least one behavior track data corresponding to each user;
s2, analyzing the behavior trajectory data of each user according to a predetermined similarity analysis rule to obtain the similarity between each user through analysis;
s3, clustering the users by using a predetermined second clustering algorithm based on the similarity among the users to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups;
s4, analyzing LBS data of all users in the user group to which each predetermined user belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of each predetermined user, and sending a recommendation instruction of the service place on the preferred behavior track of each predetermined user to a predetermined terminal, or analyzing LBS data of all users in the user group to which the user who requests the recommendation belongs by using the predetermined service place recommendation model, determining the preferred behavior track of the user, and sending the recommendation instruction of the service place on the preferred behavior track of the user to the predetermined terminal.
Further, the predetermined database comprises mobile positioning data acquired from positioning service systems of all mobile terminal users and provided service data related to positions, the LBS data comprises geographic position information data and various types of service data related to the geographic position information data, and the behavior trajectory data comprises travel type trajectory data and/or entertainment type trajectory data; the travel type track data includes a travel time and a travel identifier, and the entertainment type track data includes an entertainment time and an address identifier.
Further, the predetermined first clustering algorithm comprises a density-based clustering algorithm; the predetermined similarity analysis rule comprises a cosine included angle similarity method, a Euclidean distance measurement method or a Pearson correlation coefficient method; the predetermined second clustering algorithm includes a primitive-based objective function clustering algorithm, a density-based clustering algorithm, or a hierarchy-based clustering algorithm.
Further, the predetermined service place recommendation model is a collaborative filtering recommendation model.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium storing a service location recommendation system based on LBS data, which is executable by at least one processor to cause the at least one processor to perform the steps of the service location recommendation method based on LBS data as described above.
Compared with the prior art, the electronic device, the individualized recommendation method based on the LBS data and the computer readable storage medium provided by the invention have the advantages that the LBS data corresponding to each user in the preset time is obtained from the predetermined database, and the obtained LBS data is subjected to cluster analysis to respectively analyze at least one behavior trace data corresponding to each user; secondly, analyzing the behavior track data of each user to obtain the similarity among the users; thirdly, clustering the users based on the similarity among the users to obtain different user groups; and finally, analyzing LBS data of all users in the same user group to analyze preferred behavior tracks of all users in the user group, and sending service place recommendation instructions aiming at the behavior tracks of the users in the user group to a predetermined terminal based on the analyzed preferred behavior tracks of all the users. Therefore, the defect of limitation on LBS data application in the prior art can be avoided, and the recommendation accuracy can be improved.
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FIG. 1 is a schematic diagram of an alternative application environment for various embodiments of the present invention;
FIG. 2 is a diagram of an alternative hardware architecture of the electronic device of FIG. 1;
FIG. 3 is a schematic view of program modules of an embodiment of the LBS data-based service location recommendation system of the present invention;
FIG. 4 is a schematic view of program modules of another embodiment of the LBS data-based service location recommendation system of the present invention;
FIG. 5 is a schematic flow chart illustrating an embodiment of a service location recommendation method based on LBS data according to the present invention;
fig. 6 is a schematic flow chart illustrating an implementation of another embodiment of the service location recommendation method based on LBS data according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Fig. 1 is a schematic diagram of an alternative application environment according to various embodiments of the present invention.
In the embodiment, the present invention can be applied to an application environment including, but not limited to, the mobile terminal 1, the electronic device 2, and the network 3.
Among them, the mobile terminal 1 may be a mobile device such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, and the like, and a fixed terminal such as a digital TV, a desktop computer, a notebook, a server, and the like.
The electronic device 2 may be a rack-mounted server, a blade server, a tower server, or a rack server, and the electronic device 2 may be an independent server or a server cluster formed by a plurality of servers.
The network 3 may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth (Bluetooth), Wi-Fi, or the like.
Fig. 2 is a schematic diagram of an alternative hardware architecture of the electronic device 2 shown in fig. 1. In this embodiment, the electronic device 2 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13, which may be communicatively connected to each other through a system bus. It is noted that fig. 2 only shows the electronic device 2 with components 11-13, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 2, such as a hard disk or a memory of the electronic device 2. In other embodiments, the memory 11 may also be an external storage device of the electronic apparatus 2, such as a plug-in hard disk provided on the electronic apparatus 2, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Of course, the memory 11 may also comprise both an internal memory unit of the electronic apparatus 2 and an external memory device thereof. In the present embodiment, the memory 11 is generally used for storing an operating system installed in the electronic device 2 and various types of application software, such as program codes of the service location recommendation system 200 based on LBS data. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally configured to control overall operation of the electronic device 2, such as performing control and processing related to data interaction or communication with the mobile terminal 1. In this embodiment, the processor 12 is configured to execute the program code stored in the memory 11 or process data, such as the executed service-location recommendation system 200 based on LBS data.
The network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is generally used to establish a communication connection between the electronic apparatus 2 and other electronic devices. In this embodiment, the network interface 13 is mainly used to connect the electronic device 2 with one or more mobile terminals 1 through the network 3, and establish a data transmission channel and a communication connection between the electronic device 2 and the one or more mobile terminals 1.
The application environment and the hardware structure and function of the related devices of the various embodiments of the present invention have been described in detail so far. Hereinafter, various embodiments of the present invention will be proposed based on the above-described application environment and related devices.
First, the present invention proposes a service location recommendation system 200 based on LBS data.
Referring to fig. 3, a program module diagram of an embodiment of the service location recommendation system 200 based on LBS data according to the present invention is shown. In this embodiment, the LBS data based service location recommendation system 200 may be partitioned into one or more modules, which are stored in the memory 11 and executed by one or more processors (in this embodiment, the processor 12) to accomplish the present invention. For example, in fig. 3, the service place recommendation system 200 based on LBS data may be partitioned into an acquisition module 201, a first analysis module 202, a clustering module 203, and a second analysis module 204. The program modules referred to herein are a series of computer program instruction segments capable of performing specific functions, and are more suitable than programs for describing the execution process of the LBS data based service location recommendation system 200 in the electronic device 2. The functions of each program module 201 and 204 will be described in detail below.
An obtaining module 201, configured to obtain LBS data corresponding to each predetermined user within a preset time from a predetermined database if service location recommendation needs to be performed on each predetermined mobile terminal user or if a service location recommendation request sent by a predetermined mobile terminal is received, and perform cluster analysis on the obtained LBS data of each user by using a predetermined first clustering algorithm to analyze at least one behavior trace data corresponding to each user respectively.
The LBS data comprises geographic position information data and various service information data related to the geographic position information data, and the behavior track data comprises travel type track data and/or entertainment type track data; the travel type trajectory data includes travel time and travel identification (e.g., a certain time period often going to a certain restaurant for lunch at noon), and the entertainment type trajectory data includes entertainment time and address identification (e.g., a weekend going to a certain location for travel).
Generally, a mobile location service system is used to find a current geographical location of a mobile terminal user, and search names and addresses of places where services are available (e.g., names and addresses of hotels, theaters, libraries, gas stations, etc.) within a certain range from the current geographical location, and then recommend the searched related names and addresses to the mobile terminal user, so that the mobile terminal user selects a corresponding service according to the recommended names and addresses. After the mobile terminal user selects the service, the mobile positioning service system records the current geographic position (i.e. the geographic position information data) of the user and the selected service (i.e. the related service information data) and stores the current geographic position and the selected service in the database. Therefore, in this embodiment, the obtaining module 201 can obtain the LBS data (i.e. the geographic location information data and the provided service information data related to the geographic location information data) of the mobile terminal user from the database.
Then, the obtained LBS data of each user is clustered and analyzed by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user, in this embodiment, the predetermined first clustering algorithm is a density-based clustering algorithm (e.g., a DBSCAN clustering algorithm).
Further, taking the obtained LBS data of the user a as an example to illustrate a specific clustering analysis process, first, a core point density reachable area, and a boundary point of the density reachable area need to be predefined, in this embodiment, a certain geographic location where the obtained user a frequently locates within a preset time interval is taken as the core point, for example, if the first time that the user a locates the restaurant E at 12 am within one month exceeds a preset number of times (20 times), the geographic location of the restaurant B is taken as the core point, if the second time that the geographic location F is located by the user a within a preset time (within one month) is greater than or equal to the first time, the geographic location F is a point in the core point density reachable area, an area formed by the core points in each density reachable area is taken as the core point density reachable area, if the third time that the geographic position G is located by the user A within the preset time is equal to the first time, the geographic position G is a boundary point of the density reachable area, so that places where the user A is frequently located within the preset time can be obtained, and behavior track data of the user A can be obtained according to the frequently located places, for example, the user A frequently goes to a restaurant to eat lunch at a certain time period in the noon, or travels to a certain place on the weekend, and the like.
The first analysis module 202 is configured to analyze the behavior trace data of each user according to a predetermined similarity analysis rule, so as to obtain a similarity between the users through analysis.
The predetermined similarity analysis rule in different embodiments may be a cosine angle similarity method, a euclidean distance metric method, or a pearson correlation coefficient method.
For example, in one embodiment, the euclidean distance metric is taken as an example to calculate the similarity by using the following formula:
it should be noted that x and y in the above formula are vectors after normalization, respectively, and the result of the above formula is the similarity between the vector x and the vector y, in this embodiment, for example, the similarity between the user a and the user B is analyzed, then the behavior trace data of the user a is normalized according to a preset normalization method to obtain a first vector x in the above formula, the behavior trace data of the user B is normalized according to a preset normalization method to obtain a second vector y in the above formula, and then the first vector x and the second vector y are respectively substituted into the calculation formula of the similarity to calculate the similarity between the user a and the user B.
The clustering module 203 is configured to cluster the users by using a predetermined second clustering algorithm based on the similarity between the users to obtain different user groups, where users with a similarity greater than a preset threshold are classified into the same user group, and users with a similarity less than or equal to the preset threshold are classified into different user groups.
Wherein the predetermined second clustering algorithm comprises a prototype-based objective function clustering algorithm (e.g., k-means), a density-based clustering algorithm (e.g., DBSCAN), or a hierarchy-based clustering algorithm (e.g., Hiearchical).
The second analysis module 204 is configured to analyze LBS data of all users in a user group to which each predetermined user belongs by using a predetermined service location recommendation model, analyze a behavior trajectory preferred by each predetermined user, and send a recommendation instruction of a service location on the behavior trajectory preferred by each predetermined user to a predetermined terminal, or analyze LBS data of all users in a user group to which a user requested to be recommended belongs by using a predetermined service location recommendation model, determine a behavior trajectory preferred by the user, and send a recommendation instruction of a service location on the behavior trajectory preferred by the user to a predetermined terminal.
The predetermined service place recommendation model is a collaborative filtering recommendation model, and the establishment of the service place recommendation model comprises a training process of the model and a testing process of the model.
Specifically, the training process of the model comprises the following steps:
acquiring LBS data samples of a preset number (for example, 100) of similar users, and dividing the LBS data samples of the similar users into a corresponding training set with a first proportion and a corresponding test set with a second proportion;
training a service place recommendation model by using LBS data of each similar user in the training set to obtain a trained service place recommendation model;
and testing the service place recommendation model by using the LBS data of each similar user in the test set, and ending the training if the test is passed, or increasing LBS data samples of the similar users in the training set and re-executing the training service place recommendation model if the test is not passed.
The test process of the model comprises the following steps:
analyzing LBS data of each similar user in the test set by using the trained service place recommendation model to obtain a preferred behavior track of each similar user;
comparing the obtained behavior track preferred by each similar user with the behavior track frequently activated by each similar user, and if the number of the users corresponding to and consistent with the behavior track frequently activated by the corresponding preferred behavior track exceeds a preset percentage threshold (for example, 70%), determining that the test on the service place recommendation model is passed, or if the number of the users corresponding to and consistent with the behavior track frequently activated by the corresponding preferred behavior track is less than or equal to the preset percentage threshold, determining that the test on the service place recommendation model is not passed.
Referring to fig. 4, a functional block diagram of another embodiment of the service location recommendation system based on LBS data according to the present invention is shown. As shown in fig. 4, compared to the embodiment shown in fig. 3, in the embodiment, the service location recommendation system based on LBS data may further be divided into a tracking module 205, where the tracking module 205 is configured to track the LBS data of the user receiving the recommendation instruction, analyze a matching degree between the tracked LBS data of the user and a recommended service location on the behavior trace preferred by the user, and issue a restart command to the first analysis module 202 and the clustering module 203 if the matching degree between the tracked LBS data of the user and the recommended service location on the behavior trace preferred by the user is less than or equal to a preset matching threshold.
Specifically, after the second analysis module 204 sends a recommendation instruction for the service location behavior trace of the user to a predetermined terminal, the tracking module 205 is started to track the LBS data of the user, and the matching degree between the tracked LBS data of the user and the recommended service location behavior trace is analyzed according to the predetermined mapping relationship between the LBS data of the user and the behavior trace, so as to further determine the recommendation accuracy of the service location recommendation model.
It can be understood that, if the matching degree between the tracked LBS data of the user and the recommended service location behavior trajectory obtained through analysis is greater than a preset matching threshold, it is determined that the service location recommendation model recommends accurately for the service location of the user, and further, relevant service location recommendations can be performed for other users belonging to the same customer group as the user.
It should be noted that, in other embodiments of the present invention, a third analysis module (shown in the figures) is further included, and is configured to, before analyzing the similarity between the users, analyze the behavior trace data of each user by using a predetermined frequent itemset algorithm, so as to analyze and obtain the habit characteristics of each user.
The predetermined frequent item set analysis rule comprises an FP-tree matching analysis algorithm. Specifically, the FP-tree matching analysis algorithm comprises a construction process of an FP-tree matching model and a process of mining frequent patterns.
The construction process of the FP-tree matching model comprises the steps of firstly, constructing a database DB (also called a conditional projection database) consisting of behavior track data of each user and presetting a minimum support degree, secondly, outputting a projection FP-tree according to the database DB and the minimum support degree, and continuously iterating the construction steps for the output FP-tree until the constructed new FP-tree is an empty set or the new FP-tree only comprises a path (for example, only one behavior track data), indicating that the construction process of the FP-tree matching model is finished, and mining the frequent pattern, wherein when the constructed FP-tree is empty, the prefix of the constructed FP-tree is the frequent pattern; when only one path is involved, the frequent pattern is obtained by enumerating all possible combinations of prefix connections to the book. It is to be understood that, after analyzing the life habit characteristics of each user, the first analysis module 202 analyzes the life habit characteristics of each user by using the predetermined similarity analysis rule (for example, the user a is used to go to a specific location for exercise on weekends, the user B is used to go to a specific shopping mall on weekends, etc.) to obtain the similarity between each user.
Fig. 5 is a schematic view of an implementation flow of an embodiment of the LBS data service location based recommendation method according to the present invention. As can be seen from fig. 5, in the present embodiment, the LBS data based service location recommendation method includes steps S301 to S304.
S301, configured to obtain LBS data corresponding to each predetermined user within a preset time from a predetermined database if a service location recommendation request for each predetermined mobile terminal user is required or received, and perform cluster analysis on the obtained LBS data of each user by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user.
The LBS data comprises geographic position information data and various service information data related to the geographic position information data, and the behavior track data comprises travel type track data and/or entertainment type track data; the travel type trajectory data includes travel time and travel identification (e.g., a certain time period often going to a certain restaurant for lunch at noon), and the entertainment type trajectory data includes entertainment time and address identification (e.g., a weekend going to a certain location for travel).
Generally, a mobile location service system is used to find a current geographical location of a mobile terminal user, and search names and addresses of places where services are available (e.g., names and addresses of hotels, theaters, libraries, gas stations, etc.) within a certain range from the current geographical location, and then recommend the searched related names and addresses to the mobile terminal user, so that the mobile terminal user selects a corresponding service according to the recommended names and addresses. After the mobile terminal user selects the service, the mobile positioning service system records the current geographic position (i.e. the geographic position information data) of the user and the selected service (i.e. the related service information data) and stores the current geographic position and the selected service in the database. Therefore, in this embodiment, the obtaining module 201 can obtain the LBS data (i.e. the geographic location information data and the provided service information data related to the geographic location information data) of the mobile terminal user from the database.
Then, the obtained LBS data of each user is clustered and analyzed by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user, in this embodiment, the predetermined first clustering algorithm is a density-based clustering algorithm (e.g., a DBSCAN clustering algorithm).
Further, taking the obtained LBS data of the user a as an example to illustrate a specific clustering analysis process, first, a core point density reachable area, and a boundary point of the density reachable area need to be predefined, in this embodiment, a certain geographic location where the obtained user a frequently locates within a preset time interval is taken as the core point, for example, if the first time that the user a locates the restaurant E at 12 am within one month exceeds a preset number of times (20 times), the geographic location of the restaurant E is taken as the core point, if the second time that the geographic location F is located by the user a within a preset time (within one month) is greater than or equal to the first time, the geographic location F is a point in the core point density reachable area, an area formed by the core points in each density reachable area is taken as the core point density reachable area, if the third time that the geographic position G is located by the user A within the preset time is equal to the first time, the geographic position G is a boundary point of the density reachable area, so that places where the user A is frequently located within the preset time can be obtained, and behavior track data of the user A can be obtained according to the places where the user A is frequently located, for example, the user A frequently goes to a certain restaurant for lunch at a certain time period in the noon, or travels to a certain place on the weekend, and the like.
And S302, analyzing the behavior trajectory data of each user according to a predetermined similarity analysis rule to obtain the similarity between the users.
The predetermined similarity analysis rule in different embodiments may be a cosine angle similarity method, a euclidean distance metric method, or a pearson correlation coefficient method.
For example, in one embodiment, the euclidean distance metric is taken as an example to calculate the similarity by using the following formula:
it should be noted that x and y in the above formula are vectors after normalization, respectively, and the result of the above formula is the similarity between the vector x and the vector y, in this embodiment, for example, the similarity between the user a and the user B is analyzed, then the behavior trace data of the user a is normalized according to a preset normalization method to obtain a first vector x in the above formula, the behavior trace data of the user B is normalized according to a preset normalization method to obtain a second vector y in the above formula, and then the first vector x and the second vector y are respectively substituted into the calculation formula of the similarity to calculate the similarity between the user a and the user B.
And S303, clustering the users by using a predetermined second clustering algorithm based on the similarity among the users to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups.
Wherein the predetermined second clustering algorithm comprises a prototype-based objective function clustering algorithm (e.g., k-means), a density-based clustering algorithm (e.g., DBSCAN), or a hierarchy-based clustering algorithm (e.g., Hiearchical).
S304, analyzing LBS data of all users in a user group to which each predetermined user belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of each predetermined user, and sending a recommendation instruction of a service place on the preferred behavior track of each predetermined user to a predetermined terminal.
The predetermined service place recommendation model is a collaborative filtering recommendation model, and the establishment of the service place recommendation model comprises a training process of the model and a testing process of the model.
Specifically, the training process of the model comprises the following steps:
acquiring LBS data samples of a preset number (for example, 100) of similar users, and dividing the LBS data samples of the similar users into a corresponding training set with a first proportion and a corresponding test set with a second proportion;
training a service place recommendation model by using LBS data of each similar user in the training set to obtain a trained service place recommendation model;
and testing the service place recommendation model by using the LBS data of each similar user in the test set, and ending the training if the test is passed, or increasing LBS data samples of the similar users in the training set and re-executing the training service place recommendation model if the test is not passed.
The test process of the model comprises the following steps:
analyzing LBS data of each similar user in the test set by using the trained service place recommendation model to obtain a preferred behavior track of each similar user;
comparing the obtained behavior track preferred by each similar user with the behavior track frequently activated by each similar user, and if the number of the users corresponding to and consistent with the behavior track frequently activated by the corresponding preferred behavior track exceeds a preset percentage threshold (for example, 70%), determining that the test on the service place recommendation model is passed, or if the number of the users corresponding to and consistent with the behavior track frequently activated by the corresponding preferred behavior track is less than or equal to the preset percentage threshold, determining that the test on the service place recommendation model is not passed.
Fig. 6 is a schematic flow chart illustrating an implementation of another embodiment of the service location recommendation method based on LBS data according to the present invention. As can be seen from fig. 6, compared to the embodiment shown in fig. 5, the service location recommendation method based on LBS data of the present embodiment includes steps S401 to S403.
Step S401, if a service place recommendation instruction sent by a predetermined terminal user is received, obtaining LBS data corresponding to each predetermined user within a preset time from a predetermined database, and performing cluster analysis on the obtained LBS data of each user by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user.
Step S402, analyzing the behavior trajectory data of each user according to a predetermined similarity analysis rule to obtain the similarity between each user.
Step S403, based on the similarity among the users, clustering the users by using a predetermined second clustering algorithm to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups.
Step S404, analyzing LBS data of all users in the user group to which the user who requests recommendation belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of the user, and sending a recommendation instruction of the service place on the preferred behavior track of the user to a predetermined terminal.
It should be noted that, in some other embodiments of the present invention, the method further includes step S405 (not shown in the figure), tracking LBS data of the user who receives the recommendation instruction, analyzing a matching degree between the tracked LBS data of the user and the recommended service location on the preferred behavior trace of the user, and if the matching degree between the tracked LBS data of the user and the recommended service location on the preferred behavior trace of the user is less than or equal to a preset matching threshold, repeating step S402 and step S403.
Specifically, after the execution of the service place behavior trajectory recommendation instruction for the user is sent to the predetermined terminal, the instruction of tracking the LBS data of the user is sent, the matching degree between the tracked LBS data of the user and the recommended service place behavior trajectory is analyzed, and whether the recommendation of the service place recommendation model is accurate is further determined.
It can be understood that, if the matching degree between the tracked LBS data of the user and the recommended service location behavior trajectory obtained through analysis is greater than a preset matching threshold, it is determined that the service location recommendation model recommends accurately for the service location of the user, and further, relevant service location recommendations can be performed for other users belonging to the same customer group as the user.
It should be further noted that, in other embodiments of the present invention, before the step of analyzing the similarity between the users, a step of analyzing the behavior trace data of each user by using a predetermined frequent itemset algorithm is further included to analyze and obtain the lifestyle characteristics of each user (none of which is shown in the figure).
The predetermined frequent item set analysis rule comprises an FP-tree matching analysis algorithm. Specifically, the FP-tree matching analysis algorithm comprises a construction process of an FP-tree matching model and a process of mining frequent patterns.
The construction process of the FP-tree matching model comprises the steps of firstly, constructing a database DB (also called a conditional projection database) consisting of behavior track data of each user and presetting a minimum support degree, secondly, outputting a projection FP-tree according to the database DB and the minimum support degree, and continuously iterating the construction steps for the output FP-tree until the constructed new FP-tree is an empty set or the new FP-tree only comprises a path (for example, only one behavior track data), indicating that the construction process of the FP-tree matching model is finished, and mining the frequent pattern, wherein when the constructed FP-tree is empty, the prefix of the constructed FP-tree is the frequent pattern; when only one path is involved, the frequent pattern is obtained by enumerating all possible combinations of prefix connections to this tree.
It can be understood that, after the lifestyle characteristics of each user are analyzed, the lifestyle characteristics of each user are analyzed by using a predetermined similarity analysis rule (for example, the user a is used to go to a specific place for exercise on weekends, the user B is used to go to a specific shopping mall on weekends, and the like) to analyze and obtain the similarity between each user.
According to the embodiments, the electronic device, the service location recommendation method based on the user LBS data and the storage medium of the invention are characterized in that, firstly, the LBS data corresponding to each user in the preset time is acquired from the predetermined database, and the acquired LBS data is subjected to cluster analysis by using the predetermined first clustering algorithm, so as to analyze at least one behavior trace data corresponding to each user; then, analyzing the behavior trajectory data of each user according to a predetermined similarity analysis rule to obtain the similarity between each user through analysis; then, substituting the similarity between the users obtained by analysis into a predetermined second clustering algorithm for clustering analysis so as to define different user groups respectively consisting of acquainted users; and finally, analyzing LBS data of each user in a user group to which the user with the identification information belongs by using a predetermined service place recommendation model to analyze the preferred behavior track of the user, and sending a service place behavior track recommendation instruction aiming at the user to a predetermined terminal based on the preferred behavior track of the user. Therefore, the defect of limitation on LBS data application in the prior art can be avoided, and the recommendation accuracy can be improved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An electronic device, comprising a memory, a processor, the memory having stored thereon an LBS data based service location recommendation program executable on the processor, the LBS data based service location recommendation program when executed by the processor implementing the steps of:
A. if service place recommendation needs to be performed on each predetermined user, or if a service place recommendation request sent by a terminal of a predetermined user is received, acquiring LBS data corresponding to each predetermined user within a preset time from a predetermined database, and performing cluster analysis on the acquired LBS data of each user by using a predetermined first clustering algorithm to respectively analyze at least one behavior trace data corresponding to each user;
B. analyzing the behavior trajectory data of each user by utilizing a predetermined frequent itemset algorithm to obtain the living habit characteristics of each user through analysis, wherein the predetermined frequent itemset analysis rule comprises an FP-tree matching analysis algorithm, and after the living habit characteristics of each user are analyzed, the living habit characteristics of each user are analyzed according to a predetermined similarity analysis rule to obtain the similarity between each user through analysis;
C. based on the similarity among the users, clustering the users by using a predetermined second clustering algorithm to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups;
D. analyzing LBS data of all users in a user group to which each predetermined user belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of each predetermined user, and sending a recommendation instruction of a service place on the preferred behavior track of each predetermined user to a predetermined terminal, or analyzing the LBS data of all users in the user group to which the user who requests the recommendation belongs by using the predetermined service place recommendation model, determining the preferred behavior track of the user, and sending the recommendation instruction of the service place on the preferred behavior track of the user to the predetermined terminal;
the service place recommendation program based on LBS data when executed by the processor further implements the steps of:
and B, tracking LBS data of the user receiving the recommendation instruction, analyzing the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior track of the user, and if the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior track of the user is less than or equal to a preset matching threshold value, repeatedly executing the step B and the step C.
2. The electronic device according to claim 1, wherein the predetermined database comprises mobile positioning data obtained from positioning service programs of all mobile terminal users and provided location-related service data, the LBS data comprises geographic location information data and various types of service data provided in relation to the geographic location information data, the behavior trace data comprises journey type trace data, and/or entertainment type trace data; the travel type track data includes a travel time and a travel identifier, and the entertainment type track data includes an entertainment time and an address identifier.
3. The electronic device of claim 1, wherein the predetermined first clustering algorithm comprises a density-based clustering algorithm; the predetermined similarity analysis rule comprises a cosine included angle similarity method, a Euclidean distance measurement method or a Pearson correlation coefficient method; the predetermined second clustering algorithm includes a prototype-based objective function clustering algorithm, a density-based clustering algorithm, or a hierarchy-based clustering algorithm.
4. The electronic device of claim 1, wherein the predetermined service place recommendation model is a collaborative filtering recommendation model.
5. A service place recommendation method based on LBS data is characterized by comprising the following steps:
s1, if service place recommendation needs to be carried out on each predetermined user, or if a service place recommendation request sent by a predetermined user terminal is received, obtaining LBS data corresponding to each predetermined user within preset time from a predetermined database, and carrying out cluster analysis on the obtained LBS data of each user by using a predetermined first clustering algorithm so as to respectively analyze at least one behavior track data corresponding to each user;
s2, analyzing the behavior trajectory data of each user by using a predetermined frequent item set algorithm to obtain the living habit characteristics of each user, wherein the predetermined frequent item set analysis rule comprises an FP-tree matching analysis algorithm, and after the living habit characteristics of each user are analyzed, the living habit characteristics of each user are analyzed according to a predetermined similarity analysis rule to obtain the similarity between each user;
s3, clustering the users by using a predetermined second clustering algorithm based on the similarity among the users to obtain different user groups, wherein the users with the similarity larger than a preset threshold are classified into the same user group, and the users with the similarity smaller than or equal to the preset threshold are classified into different user groups;
s4, analyzing LBS data of all users in a user group to which each predetermined user belongs by using a predetermined service place recommendation model, analyzing a preferred behavior track of each predetermined user, and sending a recommendation instruction of a service place on the preferred behavior track of each predetermined user to a predetermined terminal, or analyzing LBS data of all users in the user group to which the user who requests the recommendation belongs by using the predetermined service place recommendation model, determining the preferred behavior track of the user, and sending the recommendation instruction of the service place on the preferred behavior track of the user to the predetermined terminal;
the method further comprises the steps of:
and tracking the LBS data of the user receiving the recommendation instruction, analyzing the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior trace of the user, and if the matching degree between the tracked LBS data of the user and the recommended service place on the preferred behavior trace of the user is less than or equal to a preset matching threshold value, repeatedly executing the steps S3 and S4.
6. The service location recommendation method based on LBS data according to claim 5, wherein said predetermined database comprises mobile positioning data obtained from positioning service programs of all mobile terminal users and provided service data related to location, said LBS data comprises geographical location information data and various types of service data provided in relation to said geographical location information data, said behavior trajectory data comprises trip type trajectory data, and/or entertainment type trajectory data; the travel type track data includes a travel time and a travel identifier, and the entertainment type track data includes an entertainment time and an address identifier.
7. The LBS data-based service location recommendation method of claim 5, wherein the predetermined first clustering algorithm comprises a density-based clustering algorithm; the predetermined similarity analysis rule comprises a cosine included angle similarity method, a Euclidean distance measurement method or a Pearson correlation coefficient method; the predetermined second clustering algorithm includes a primitive-based objective function clustering algorithm, a density-based clustering algorithm, or a hierarchy-based clustering algorithm.
8. The LBS data-based service location recommendation method of claim 5, wherein the predetermined service location recommendation model is a collaborative filtering recommendation model.
9. A computer-readable storage medium storing a LBS data based service location recommendation program executable by at least one processor to cause the at least one processor to perform the steps of the LBS data based service location recommendation method according to any one of claims 5-8.
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