CN110619090B - Regional attraction assessment method and device - Google Patents
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Abstract
The invention is suitable for the technical field of computers, and provides a regional attraction evaluation method and a device, wherein the method comprises the following steps: acquiring user access information of an area to be detected; and inputting the user access information into a region attraction evaluation model for processing to obtain the attraction evaluation information of the region to be detected. According to the method, the user access information of the area to be detected is input into the trained area attraction evaluation model for processing, the area attraction evaluation information can be accurately obtained, the urban hot spot area can be recommended based on the area attraction evaluation information, the model is different from the existing experience model, historical data are not excessively relied on, and the area attraction evaluation information can be obtained in different scenes.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a regional attraction evaluation method and device.
Background
The social media data are huge and valuable, and currently, space hotspots can be mined based on the social media data, so that the hotspots can be evaluated and popular information can be recommended to users.
However, at present, hot spot assessment is mainly performed by using some empirical models, which has great limitation in application scenes, and there is no way to accurately recommend urban hot spot areas.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and a device for evaluating area attractiveness, so as to solve the problems that in the prior art, hot spot evaluation is mainly evaluated by using some empirical models, which has great limitations in application scenarios, and there is no way to accurately recommend an urban hot spot area.
A first aspect of an embodiment of the present invention provides a method for evaluating an area attraction, including:
acquiring user access information of an area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
inputting the user access information into a region attraction evaluation model for processing to obtain attraction evaluation information of the region to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region.
A second aspect of an embodiment of the present invention provides an area attraction evaluation device, including:
the first acquisition unit is used for acquiring user access information of the area to be detected; the user access array comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
the first processing unit is used for inputting the user access information into an area attraction evaluation model for processing to obtain the attraction evaluation information of the area to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region.
Further, the area attraction force evaluation device further includes:
the second acquisition unit is used for acquiring a sample training set; the sample training set comprises sample user access information of a sample region and a sample attraction evaluation information label corresponding to the sample user access information;
and the training unit is used for training the sample training set to obtain a region attractive force evaluation model for outputting sample attractive force evaluation information of the sample region.
Further, the second obtaining unit includes:
the third acquisition unit is used for acquiring sample check-in data generated when the sample user checks in through a target social application; the sample check-in data comprises a user identification of the sample user, a place identification of a check-in place and position information thereof;
a second processing unit for obtaining sample user access information for the sample region based on the sample check-in data;
a fourth acquisition unit configured to acquire an attraction evaluation information tag of the sample region;
and the third processing unit is used for storing the sample user access information of each sample region and the attraction evaluation information label of each sample region in an associated manner to obtain a sample training set.
Further, the second processing unit is specifically configured to:
clustering the sample sign-in data to obtain sample area information; wherein the sample region information comprises at least one sample region; the sample region information includes a user identification of a sample user checking in at the sample region, a location identification of a check-in location located in the sample region, and location information thereof;
obtaining a sample user access array for the sample region based on the sample region information.
Further, the third obtaining unit is specifically configured to:
obtaining initial check-in data of the target social application; the initial check-in data comprises a check-in number for each sample user;
and when the check-in number of the sample user meets a preset condition, acquiring check-in data of the sample user.
A third aspect of embodiments of the present invention provides a regional attractiveness evaluation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the regional attractiveness evaluation method according to the first aspect.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the area appeal force estimation method according to the first aspect.
In the embodiment of the invention, user access information of a to-be-detected area is acquired; and inputting the user access array into an area attraction evaluation model for processing to obtain the attraction evaluation information of the area to be detected. According to the method, the user access information of the area to be detected is input into the trained area attraction evaluation model for processing, the area attraction evaluation information can be accurately obtained, the urban hot spot area can be recommended based on the area attraction evaluation information, the model is different from the existing experience model, historical data are not excessively relied on, and the area attraction evaluation information can be obtained in different scenes.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for evaluating an area attraction according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of another area attractiveness assessment method according to a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of another area attractiveness assessment method according to a third embodiment of the present invention;
FIG. 4 is a schematic flowchart of a refinement step S301 in another area attractiveness assessment method according to a third embodiment of the present invention;
FIG. 5 is a schematic flowchart of a refinement step S302 in another area attractiveness assessment method according to a third embodiment of the present invention;
FIG. 6 is a schematic view of an area attraction force evaluation device according to a fourth embodiment of the present invention;
fig. 7 is a schematic view of an area attractive force evaluation apparatus provided by a fifth embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for evaluating an area attraction force according to a first embodiment of the present invention. The main execution body of the area appeal evaluation method in this embodiment is a device having an area appeal evaluation function, for example, an area appeal evaluation server. The area attractiveness assessment method shown in fig. 1 may include:
s101: acquiring user access information of an area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected.
The device obtains user access information of an area to be detected, wherein the area to be detected is defined according to actual conditions, for example, a certain business circle can be defined as the area to be detected, and a certain area in a certain market can also be defined as the area to be detected. The user access information may be an array including a plurality of access quantity elements, where the access quantity element is the access quantity of each user in the area to be detected to the area to be detected, and the user access array of the area to be detected may be recorded as the access quantity of each user in the area to be detected to the area to be detected
Wherein
For the nth user pair riThe number of accesses to the region.
S102: inputting the user access information into a region attraction evaluation model for processing to obtain attraction evaluation information of the region to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region.
The device stores a pre-trained region attraction evaluation model in advance, wherein the region attraction evaluation model can be pre-trained by the device and can also be pre-trained by other devices. The pre-trained region attraction force evaluation model is obtained by training a sample training set by using a machine learning algorithm, the input of the region attraction force evaluation model is sample user access information of a sample region and a sample attraction force evaluation information label corresponding to the sample user access information, and the output of the region attraction force evaluation model is sample attraction force evaluation information of the sample region.
The pre-trained region attraction evaluation model can comprise an input layer, a hidden layer and a loss function layer (output layer). The hidden layer comprises more than two hidden layer nodes, and the input layer comprises an input layer node for receiving the sample user access information of the input sample area and the corresponding sample attraction evaluation information label from the outside. The hidden layer is used for processing the access information of the sample user. The output layer is used for outputting sample attraction force evaluation information of the sample area.
And the equipment inputs the user access information into a pre-trained area attraction evaluation model to obtain the attraction evaluation information of the area to be detected.
In the embodiment of the invention, user access information of a to-be-detected area is acquired; and inputting the user access information into a region attraction evaluation model for processing to obtain the attraction evaluation information of the region to be detected. According to the method, the user access information of the area to be detected is input into the trained area attraction evaluation model for processing, the area attraction evaluation information can be accurately obtained, the urban hot spot area can be recommended based on the area attraction evaluation information, the model is different from the existing experience model, historical data are not excessively relied on, and the area attraction evaluation information can be obtained in different scenes.
Referring to fig. 2, fig. 2 is a schematic flow chart of another area attractiveness assessment method according to a second embodiment of the present invention. The main execution body of the area appeal evaluation method in this embodiment is a device having an area appeal evaluation function, for example, an area appeal evaluation server. In order to obtain more accurate area attraction evaluation information, the present embodiment defines a training process of an area attraction evaluation model, and the present embodiment differs from the first embodiment in S201 to S202, where S203 to S204 in the present embodiment are the same as S101 to S102 in the first embodiment, and S201 to S202 are performed before S203. As shown in fig. 2, S201 to S202 are specifically as follows:
s201: acquiring a sample training set; the sample training set comprises sample user access information of a sample region and a sample attraction evaluation information label corresponding to the sample user access information.
The device acquires a sample training set, wherein the sample training set comprises sample user access information of a sample region and sample attraction evaluation information labels corresponding to the sample user access information, and each sample user access information corresponds to one sample attraction evaluation information label. The number of samples in the sample training set can be set according to actual conditions, and to a certain extent, the more the number of the trained samples is, the more accurate the recognition result is when the neural network model obtained by training is used for recognition.
S202: and training the sample training set to obtain a region attraction evaluation model for outputting sample attraction evaluation information of the sample region.
In this embodiment, the area attraction evaluation model is trained in advance by the device. The device trains a sample training set, in the training process, a sample user access array of a sample area and sample attraction evaluation information corresponding to the sample user access array are used as training data, the training data are input into an initial neural network model, and the model is continuously improved by adjusting a loss function of the area attraction evaluation model, so that a final area attraction evaluation model is obtained.
Referring to fig. 3, fig. 3 is a schematic flowchart of another area attractiveness assessment method according to a third embodiment of the present invention. The main execution body of the area appeal evaluation method in this embodiment is a device having an area appeal evaluation function, for example, an area appeal evaluation server. In order to acquire a high-quality sample and train a neural network model, thereby acquiring accurate area attraction force evaluation information, the present embodiment differs from the second embodiment in S301 to S304, where S305 to S307 in the present embodiment are the same as S202 to S204 in the second embodiment, and S301 to S304 are further refinements of S201 in the second embodiment. As shown in fig. 3, S301 to S304 are specifically as follows:
s301: obtaining sample check-in data generated when the sample user checks in through a target social application; the sample check-in data includes a user identification of the sample user, a place identification of a check-in place, and location information thereof.
The social media platform is large, the updating speed is high, the content is various, so that the social media data is huge and valuable, and many current social applications have a check-in function. The location service refers to determining actual Geographic location Information of a user through a mobile phone terminal by using a Global Positioning System (GPS), a base station and other related Positioning technologies through a mobile communication network of a telecommunication operator and combining a Geographic Information System (GIS) with the GIS, and providing Geographic location Information services for the user in the forms of short messages, multimedia messages, voice, web pages, client software and the like, for example, the user can obtain a current location through a Positioning function and sign in or share the current location. Currently common social applications include WeChat, microblog, QQ space, and the like, and the target social application in the present embodiment may include, but is not limited to, the common social applications.
The device obtains sign-in data generated by sign-in of a sample user through a target social application, wherein the target social application is a social media application which is large in user group, rich in data and provided with a position sign-in function. The sample user can check in dynamically by publishing on the target social application or directly clicking a virtual button of 'position check in', and check in data generated by the sample user through checking in of the target social application comprises user identification of the user, place identification of a check-in place and position information of the place identification. The user identification of the sample user is a unique identification for identifying the user identity, the location identification of the check-in location and the location information thereof are the unique identification of the check-in location and the corresponding location information thereof, and the location information may be in a coordinate manner or in a longitude and latitude manner, which is not limited here.
Further, in order to improve the quality of the check-in data and obtain high-quality sample information, S301 may include S3011 to S3012, as shown in fig. 4, S3011 to S3012 specifically include the following:
s3011: obtaining initial check-in data of the target social application; the initial check-in data includes a number of check-ins for each sample user.
The device acquires initial check-in data of the target social application, wherein the initial check-in data is check-in data which is not subjected to any screening and processing, and the initial check-in data comprises the check-in number of each sample user, namely the number of all check-in operations of each sample user on the target social application.
S3012: and when the check-in number of the sample user meets a preset condition, acquiring check-in data of the sample user.
In order to improve the quality of the check-in data, valuable check-in data needs to be screened out, namely the check-in data of sample users with a large number of check-in is more valuable and can better reflect the attraction of a region. The equipment presets conditions for screening sample users meeting the preset conditions, and when the check-in number of the sample users meets the preset conditions, the sample users meeting the preset conditions are active users in the target social application, and the signs of the active users are obtainedAnd obtaining high-quality check-in data. For example, the number of user check-ins per sample may be set as variable xiCalculating the average sign-in number mu and standard deviation sigma of each sample user, and then removing the users with the sample user text number outside the triple standard deviation by using the triple standard deviation principle, namely the rest users satisfy xi∈[μ-3σ,μ+3σ]And obtaining check-in data of the rest sample users.
S302: obtaining sample user access information for the sample region based on the sample check-in data.
The device obtains sample user access information of the sample region based on the sample check-in data, the sample user access array comprises a plurality of sample access number elements, and the sample access number elements are the access number of each sample user in the region to be detected to the region to be detected. In one implementation, a sample area is preset, then sample check-in data is counted, and the number of visits of each sample user in the sample area to the sample area is obtained; in another embodiment, the sample check-in data may be clustered to obtain the sample region, and then the sample user access information of the sample region may be obtained based on the sample check-in data.
Further, in order to obtain a high-quality sample training set and thus train a region attraction evaluation model with accurate result, S302 may include S3021 to S3022, as shown in fig. 5, where S3021 to S3022 are specifically as follows:
s3021: clustering the sample sign-in data to obtain sample area information; wherein the sample region information comprises at least one sample region; the sample region information includes a user identification of a sample user checking in at the sample region, a location identification of a check-in location located in the sample region, and location information thereof.
The process of dividing a collection of physical or abstract objects into classes composed of similar objects is called clustering. The cluster generated by clustering is a collection of a set of data objects that are similar to objects in the same cluster and distinct from objects in other clusters. The equipment clusters the sample check-in data, and the cluster analysis and calculation methods mainly comprise the following steps:
1. partitioning methods, i.e. given a data set with N tuples or records, the splitting method will construct K groups, each representing a cluster, K < N. Most partitioning methods are distance-based. Given the number of partitions k to be constructed, the partitioning method first creates an initial partition. It then employs an iterative relocation technique to divide by moving objects from one group to another. A good general preparation for partitioning is: objects in the same cluster are as close to or related to each other as possible, while objects in different clusters are as far apart or different as possible. There are many other criteria for assessing the quality of the partitioning. Traditional partitioning methods can be extended to subspace clustering, rather than searching the entire data space. This is useful when there are many attributes and the data is sparse. To achieve global optimality, partition-based clustering may require an exhaustive list of all possible partitions, which is computationally expensive.
2. Hierarchical methods (hierarchical methods) that decompose a given data set hierarchically until a condition is satisfied. The method can be divided into two schemes of bottom-up and top-down. For example, in a "bottom-up" scheme, initially each data record is grouped into a separate group, and in the next iteration it groups those records that are adjacent to each other into a group until all records are grouped into a group or some condition is met. Representative algorithms include CHAMELEON (CHAMELEON) algorithm and the like. Hierarchical clustering methods may be distance-based or density or connectivity-based. Some extensions to the hierarchical clustering approach also consider subspace clustering.
3. Density-based methods, one fundamental difference between density-based methods and others is: it is not based on a wide range of distances, but rather on density. This overcomes the disadvantage that distance-based algorithms can only find "circle-like" clusters. The guiding idea of this method is to add a point in a region to its neighboring clusters whenever its density exceeds a certain threshold.
4. Grid-based methods, which first divide the data space into a grid structure of a finite number of cells (cells), all of the processing is targeted at a single cell. A significant advantage of this is that the processing is fast, typically independent of the number of records in the target database, which is only dependent on how many cells the data space is divided into.
5. Model-based methods (model-based methods) assume a model for each cluster and then look for a data set that can well satisfy the model. Such a model may be a function of the density distribution of data points in space or otherwise. One potential assumption is that the target data set is determined by a series of probability distributions. There are generally two directions of attempt: statistical approaches and approaches to neural networks.
And clustering the check-in data by the equipment to obtain sample area information. In the present embodiment, the check-in data is clustered using a preset density clustering algorithm. The Density-Based Clustering of Applications with Noise (DBSCAN) is a relatively representative Density-Based Clustering algorithm. Different from the dividing and hierarchical clustering method, the method defines clusters as the maximum set of points connected in density, can divide areas with high enough density into clusters, and can find clusters with any shapes in a noise spatial database.
For the DBSCAN algorithm, two parameters are needed to be determined, one parameter is the radius of each class (Eps), i.e. the radius of the interest area; one parameter is the minimum number of check-in points (MinPts) in each class, i.e., the number of check-in places contained in the area of interest.
The parameters can be determined through a k-dist graph, taking a check-in place p as an example, and the k-dist is the distance between p and the k-th point closest to p. The k-dist graph is constructed by calculating the k-dist value of check-in data of each user, then arranging the check-in data in descending order according to the k-dist, wherein the first point in the ranking is the check-in point with the maximum k-dist value, and the last point in the ranking is the point with the minimum k-dist value. And then drawing the check-in points into a two-dimensional coordinate system according to the sequence, wherein the abscissa is the check-in points arranged according to the descending order of k-dist, and the ordinate is the k-dist value. I.e. k-dist satisfies the condition: the nearest k points around p are all within the k-dist distance range of p. Performing visual analysis on the k-dist graph, listing k-dist values of all check-in places according to a descending order, finding an obvious valley bottom of the k-dist graph, and taking a threshold value corresponding to the valley bottom as an Eps of the DBSCAN; then, MinPts is calculated using the following formula:
wherein p isiTo the number of check-in places within an Eps distance around the corresponding check-in place. The regional attraction evaluation device clusters the check-in data by adopting a preset density clustering algorithm based on the check-in data, the radius of the interest region and the number of check-in places contained in the interest region, can select an unvisited check-in place to start, and finds out all nearby check-in places which are within the range of Eps (including Eps) from the unvisited check-in place. If the number of nearby check-in places is ≧ MinPts, the current point forms a cluster with its nearby points, and the departure point is marked as visited (visited). Then recursively, all check-in places within the cluster that are not marked as visited (visited) are processed in the same way, thereby expanding the cluster. If the number of nearby points<MinPts, the check-in location is temporarily marked as a noise point. If the cluster is sufficiently expanded, i.e., all check-in locations within the cluster are marked as visited, then the same algorithm is used to process the non-visited check-in locations, resulting in sample region information.
The sample region information comprises at least one sample region, and the sample region is a region obtained by dividing based on the check-in data; the sample region information includes a user identification of a sample user who checked in to the sample region, a location identification of a check-in location located in the sample region, and position information thereof.
S3022: obtaining sample user access information for the sample region based on the sample region information.
The sample user access array comprises a plurality of sample access number elements, the sample access number elements are the access number of each sample user in the area to be detected to the area to be detected, and the sample area information comprises the user identification of the sample user checked in the sample area, the location identification of the check-in location in the sample area and the position information of the location identification. Therefore, the access number of each sample user to the region to be detected can be obtained by performing statistics based on the sample region information, so that the sample user access array of the sample region is obtained.
S303: acquiring an attractiveness assessment information label of the sample region.
The device can preset an attraction evaluation information tag of the sample region, wherein the attraction evaluation information tag of the sample region can be obtained by scoring the historical attraction of the user to the region, obtaining historical attraction scores of the plurality of users to the region, and performing weighted calculation on the historical attraction scores to obtain the attraction evaluation information tag of the sample region.
S304: and storing the sample user access information of each sample region and the attraction evaluation information label of each sample region in an associated manner to obtain a sample training set.
The device stores the sample user access information of each sample region and the attraction evaluation information labels of the corresponding sample regions in an associated manner, and the groups are one sample to obtain a sample training set.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Referring to fig. 6, fig. 6 is a schematic view of an area attractive force evaluation apparatus according to a fourth embodiment of the present invention. The included units are used for executing steps in the embodiments corresponding to fig. 1 to fig. 5, and refer to the related descriptions in the embodiments corresponding to fig. 1 to fig. 5. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 6, the area attractive force evaluation device 6 includes:
a first obtaining unit 610, configured to obtain user access information of an area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
the first processing unit 620 is configured to input the user access information into an area attraction evaluation model for processing, and acquire attraction evaluation information of the area to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region.
Further, the area attraction force evaluation device 6 further includes:
the second acquisition unit is used for acquiring a sample training set; the sample training set comprises sample user access information of a sample region and a sample attraction evaluation information label corresponding to the sample user access information;
and the training unit is used for training the sample training set to obtain a region attractive force evaluation model for outputting sample attractive force evaluation information of the sample region.
Further, the second obtaining unit includes:
the third acquisition unit is used for acquiring sample check-in data generated when the sample user checks in through a target social application; the sample check-in data comprises a user identification of the sample user, a place identification of a check-in place and position information thereof;
a second processing unit for obtaining sample user access information for the sample region based on the sample check-in data;
a fourth acquisition unit configured to acquire an attraction evaluation information tag of the sample region;
and the third processing unit is used for storing the sample user access information of each sample region and the attraction evaluation information label of each sample region in an associated manner to obtain a sample training set.
Further, the second processing unit is specifically configured to:
clustering the sample sign-in data to obtain sample area information; wherein the sample region information comprises at least one sample region; the sample region information includes a user identification of a sample user checking in at the sample region, a location identification of a check-in location located in the sample region, and location information thereof;
obtaining sample user access information for the sample region based on the sample region information.
Further, the third obtaining unit is specifically configured to:
obtaining initial check-in data of the target social application; the initial check-in data comprises a check-in number for each sample user;
and when the check-in number of the sample user meets a preset condition, acquiring check-in data of the sample user.
Referring to fig. 7, fig. 7 is a schematic diagram of a regional attractive force assessment apparatus according to a fifth embodiment of the present invention. As shown in fig. 7, the area attractive force evaluation apparatus 7 of this embodiment includes: a processor 70, a memory 71 and a computer program 72, such as a regional appeal assessment program, stored in said memory 71 and executable on said processor 70. The processor 70, when executing the computer program 72, implements the steps in the above-described embodiments of the area attraction force evaluation method, such as the steps 101 to 102 shown in fig. 1. Alternatively, the processor 70, when executing the computer program 72, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 610 to 620 as shown in the figure.
Illustratively, the computer program 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution process of the computer program 72 in the area attractiveness evaluation device 7. For example, the computer program 72 may be divided into a first acquisition unit and a first processing unit, and the specific functions of each unit are as follows:
the first acquisition unit is used for acquiring user access information of the area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
the first processing unit is used for inputting the user access information into an area attraction evaluation model for processing to obtain the attraction evaluation information of the area to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region.
The area attractiveness assessment device may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is merely an example of the area attractiveness assessment device 10 and does not constitute a limitation of the area attractiveness assessment device 7 and may include more or less components than those shown, or some components may be combined, or different components may be included, for example, the area attractiveness assessment device may also include input and output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the area attraction force evaluation device 7, such as a hard disk or a memory of the area attraction force evaluation device 7. The memory 71 may also be an external storage device of the area attraction evaluation device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the area attraction evaluation device 7. Further, the memory 71 may also include both an internal storage unit of the area attraction force evaluation device 7 and an external storage device. The memory 71 is used to store the computer program and other programs and data required by the area attractiveness evaluation device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for evaluating regional attractiveness, which is used for recommending a hotspot region, includes:
acquiring user access information of an area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
inputting the user access information into a pre-trained area attraction evaluation model for processing to obtain attraction evaluation information of the area to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region; the sample region is a region where sample check-in data is gathered; the attractiveness assessment information label is obtained by scoring historical attractiveness of the sample region by a plurality of users.
2. The area attractiveness assessment method according to claim 1, wherein before the inputting the user access information into an area attractiveness assessment model for processing and obtaining the attractiveness assessment information of the area to be detected, the method further comprises:
acquiring a sample training set; the sample training set comprises sample user access information of a sample region and a sample attraction evaluation information label corresponding to the sample user access information;
and training the sample training set to obtain a region attraction evaluation model for outputting sample attraction evaluation information of the sample region.
3. The regional appeal assessment method of claim 2, wherein said obtaining a sample training set comprises:
obtaining sample check-in data generated when the sample user checks in through a target social application; the sample check-in data comprises a user identification of the sample user, a place identification of a check-in place and position information thereof;
obtaining sample user access information for the sample region based on the sample check-in data;
acquiring an attraction evaluation information tag of the sample region;
and storing the sample user access information of each sample region and the attraction evaluation information label of each sample region in an associated manner to obtain a sample training set.
4. The area attractiveness assessment method of claim 3 wherein the obtaining sample user access information for the sample area based on the sample check-in data comprises:
clustering the sample sign-in data to obtain sample area information; wherein the sample region information comprises at least one sample region; the sample region information includes a user identification of a sample user checking in at the sample region, a location identification of a check-in location located in the sample region, and location information thereof;
obtaining sample user access information for the sample region based on the sample region information.
5. The area attractiveness assessment method of claim 3 wherein the obtaining sample check-in data generated by the sample user upon checking in through a target social application comprises:
obtaining initial check-in data of the target social application; the initial check-in data comprises a check-in number for each sample user;
and when the check-in number of the sample user meets a preset condition, acquiring check-in data of the sample user.
6. An apparatus for evaluating attraction of a region, the apparatus being configured to recommend a hotspot region, the apparatus comprising:
the first acquisition unit is used for acquiring user access information of the area to be detected; the user access information comprises a plurality of access number elements; the access number element is the access number of each user in the area to be detected to the area to be detected;
the first processing unit is used for inputting the user access information into a pre-trained area attraction evaluation model for processing to obtain attraction evaluation information of the area to be detected; in the training process, the input of the region attraction evaluation model is sample user access information of a sample region and a corresponding sample attraction evaluation information label thereof, and the output of the region attraction evaluation model is sample attraction evaluation information of the sample region; the sample region is a region where sample check-in data is gathered; the attractiveness assessment information label is obtained by scoring historical attractiveness of the sample region by a plurality of users.
7. The area attraction force evaluation device according to claim 6, further comprising:
the second acquisition unit is used for acquiring a sample training set; the sample training set comprises sample user access information of a sample region and a sample attraction evaluation information label corresponding to the sample user access information;
and the training unit is used for training the sample training set to obtain a region attractive force evaluation model for outputting sample attractive force evaluation information of the sample region.
8. The area attraction force evaluation device according to claim 7, wherein the second acquisition unit includes:
the third acquisition unit is used for acquiring sample check-in data generated when the sample user checks in through a target social application; the sample check-in data comprises a user identification of the sample user, a place identification of a check-in place and position information thereof;
a second processing unit for obtaining sample user access information for the sample region based on the sample check-in data;
a fourth acquisition unit configured to acquire an attraction evaluation information tag of the sample region;
and the third processing unit is used for storing the sample user access array of each sample region and the attraction evaluation information label of each sample region in an associated manner to obtain a sample training set.
9. An area attractiveness assessment device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the method according to any one of claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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