CN110889029A

CN110889029A - City target recommendation method and device

Info

Publication number: CN110889029A
Application number: CN201810946801.5A
Authority: CN
Inventors: 朱翔宇; 翟磊
Original assignee: Beijing Jingdong Financial Technology Holding Co Ltd
Current assignee: Beijing Jingdong Financial Technology Holding Co Ltd
Priority date: 2018-08-17
Filing date: 2018-08-17
Publication date: 2020-03-17
Anticipated expiration: 2038-08-17
Also published as: CN110889029B

Abstract

The disclosure provides a city target recommendation method, which includes: the method comprises the steps of obtaining urban characteristic data of various types as source data, obtaining a target type to be recommended, taking the urban characteristic data which are in line with the target type and meet preset recommendation conditions in the source data as a candidate set, taking the urban characteristic data which are in line with the target type and do not meet the preset recommendation conditions in the source data as a sample set, training an initial model based on the sample set to obtain an optimal model, obtaining recommendation scores corresponding to the urban characteristic data in the candidate set based on the optimal model, and outputting recommendation results based on the recommendation scores. The disclosure also provides a city goal recommendation device, a computer device and a computer readable medium.

Description

City target recommendation method and device

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a method and an apparatus for recommending urban targets.

Background

The city is taken as a background, various resources, information and characteristics in the city are acquired, analyzed and integrated, and corresponding services are provided, so that the city target recommendation method is a basic framework of city target recommendation. Most of the existing urban target recommendation schemes analyze various data information manually, no good automation system is formed, and the analysis effect is uncontrollable. In addition, because the existing urban target recommendation scheme is usually considered only from the position information dimension, the decision making process is single and incomplete, and the used recommendation method is generally based on similarity calculation and then sequencing, wherein the modification of a large number of empirical parameters is involved, and the dependency on human analysis is high.

Disclosure of Invention

In view of this, the present disclosure provides a method and an apparatus for recommending urban targets more intelligently, quickly, and accurately.

One aspect of the present disclosure provides a city goal recommendation method, including: the method comprises the steps of obtaining urban characteristic data of various types as source data, obtaining a target type to be recommended, taking the urban characteristic data which are in line with the target type and meet preset recommendation conditions in the source data as a candidate set, taking the urban characteristic data which are in line with the target type and do not meet the preset recommendation conditions in the source data as a sample set, training an initial model based on the sample set to obtain an optimal model, obtaining recommendation scores corresponding to the urban characteristic data in the candidate set based on the optimal model, and outputting recommendation results based on the recommendation scores.

According to an embodiment of the present disclosure, the acquiring the various types of city feature data as source data includes: and dividing the area where the city is located into a plurality of area blocks according to a first preset rule, and dividing the acquired city characteristic data into the area blocks. In the source data, different types of city feature data within the same region block are associated, and/or the same type of city feature data in different region blocks are associated.

According to an embodiment of the present disclosure, the training of the initial model based on the sample set to obtain the optimal model includes: adding an output value mark corresponding to the city feature data in each city feature data in the sample set, and extracting a plurality of training sets from the sample set according to a second preset rule, wherein each training set comprises a plurality of city feature data with output value marks, and each training set is different; for each training set, taking part or all of the city feature data except the training set in the sample set as a verification set corresponding to the training set, training an initial model by using the training set to obtain a training model, verifying the training model by using the verification set, and obtaining an evaluation score of the training model according to a verification result; and taking the training model with the highest evaluation score as an optimal model, and taking a training set corresponding to the training model as an optimal feature combination.

According to an embodiment of the present disclosure, the extracting the plurality of training sets from the sample set according to the second preset rule includes: and selecting the urban feature data for multiple times in a mode of traversing forwards and/or backwards in the sample set by adopting a breadth-first search algorithm to obtain each training set.

According to an embodiment of the present disclosure, the extracting the plurality of training sets from the sample set according to the second preset rule includes: and for the extracted training set, changing the training set by using a simulated annealing algorithm and/or a random search method to obtain a new training set.

According to an embodiment of the present disclosure, the obtaining of the recommendation score corresponding to each city feature data in the candidate set based on the optimal model includes: and inputting each city feature data in the candidate set into the optimal model to obtain corresponding output values, wherein the output value corresponding to each city feature data is a recommendation score corresponding to the city feature data. The outputting of the recommendation result based on the recommendation score includes: and sorting the city feature data from high to low according to the recommendation scores of the city feature data in the candidate set, and selecting and outputting the preset number of city feature data as recommendation results.

According to an embodiment of the present disclosure, the inputting the city feature data in the candidate set to the optimal model, and obtaining the corresponding output value includes: and for each city feature data in the candidate set, inputting the city feature data into the optimal model for multiple times to obtain multiple output values, performing cross validation optimization on the multiple output values to obtain an optimized output value, and taking the optimized output value as a recommendation score of the city feature data.

According to an embodiment of the present disclosure, the acquiring the various types of city feature data as source data includes: and carrying out data cleaning on the acquired urban characteristic data, wherein the urban characteristic data after the data cleaning forms source data.

Another aspect of the present disclosure provides a city goal recommendation apparatus, including: the recommendation processing module comprises a first acquisition module, a second acquisition module and a recommendation processing module. The first acquisition module is used for acquiring various types of city characteristic data as source data. The second obtaining module is used for obtaining the type of the target to be recommended. The recommendation processing module is used for taking the urban feature data which accord with the target type and accord with the preset recommendation condition in the source data as a candidate set, taking the urban feature data which accord with the target type and do not accord with the preset recommendation condition in the source data as a sample set, training an initial model based on the sample set to obtain an optimal model, obtaining a recommendation score corresponding to each urban feature data in the candidate set based on the optimal model, and outputting a recommendation result based on the recommendation score.

According to an embodiment of the present disclosure, the acquiring, by the first acquiring module, each type of city feature data as a source data includes: the first acquisition module is used for dividing the area where the city is located into a plurality of area blocks according to a first preset rule; dividing the acquired urban characteristic data into area blocks; in the source data, different types of city feature data within the same region block are associated, and/or the same type of city feature data in different region blocks are associated.

According to the embodiment of the disclosure, the training of the initial model by the recommendation processing module based on the sample set to obtain the optimal model comprises: the recommendation processing module is used for adding an output value mark corresponding to the city feature data in each city feature data in the sample set; extracting a plurality of training sets from the sample set according to a second preset rule, wherein each training set comprises a plurality of pieces of city characteristic data with output value marks, and each training set is different; for each training set, taking part or all of the city feature data except the training set in the sample set as a verification set corresponding to the training set, training an initial model by using the training set to obtain a training model, verifying the training model by using the verification set, and obtaining an evaluation score of the training model according to a verification result; and taking the training model with the highest evaluation score as an optimal model, and taking a training set corresponding to the training model as an optimal feature combination.

According to an embodiment of the present disclosure, the extracting, by the recommendation processing module, a plurality of training sets from the sample set according to a second preset rule includes: and the recommendation processing module is used for selecting the urban feature data for multiple times in a mode of traversing forwards and/or backwards in the sample set by adopting a breadth-first search algorithm to obtain each training set.

According to an embodiment of the present disclosure, the extracting, by the recommendation processing module, a plurality of training sets from the sample set according to a second preset rule includes: and the recommendation processing module is used for changing the training set by utilizing a simulated annealing algorithm and/or a random search method for the extracted training set to obtain a new training set.

According to the embodiment of the disclosure, the obtaining, by the recommendation processing module, the recommendation score corresponding to each city feature data in the candidate set based on the optimal model includes: and the recommendation processing module is used for inputting each city feature data in the candidate set into the optimal model to obtain corresponding output values, and the output value corresponding to each city feature data is the recommendation score corresponding to the city feature data. The recommendation processing module outputting recommendation results based on the recommendation scores comprises: and the recommendation processing module is used for sequencing the city characteristic data from high to low according to the recommendation scores of the city characteristic data in the candidate set, and selecting and outputting the preset number of city characteristic data as recommendation results.

According to an embodiment of the present disclosure, the recommending and processing module inputs each city feature data in the candidate set to the optimal model, and obtaining the corresponding output value includes: the recommendation processing module is used for inputting each city feature data in the candidate set to the optimal model for multiple times to obtain multiple output values; and performing cross validation optimization on the output values to obtain optimized output values, and taking the optimized output values as the recommendation scores of the city characteristic data.

According to an embodiment of the present disclosure, the acquiring, by the first acquiring module, each type of city feature data as a source data includes: the first acquisition module is used for carrying out data cleaning on the acquired urban characteristic data, and the urban characteristic data after the data cleaning forms source data.

Another aspect of the present disclosure provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the program.

Another aspect of the disclosure provides a computer-readable medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method as described above.

Another aspect of the disclosure provides a non-volatile storage medium storing computer-executable instructions for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

According to the embodiment of the disclosure, the problems that no good automatic system is formed in the existing urban target recommendation field, the effect is uncontrollable, the decision process is single and incomplete, the dependence on human analysis is high can be at least partially solved, alleviated, inhibited and even avoided, model training is carried out based on a sample set in source data, the optimal model obtained by training is utilized to automatically evaluate the data to be recommended in a candidate set in the source data, and further the urban target recommendation result can be output according to the evaluation result, because the sample set and the candidate set correspond to the same city target type, the optimal model obtained through the training of the sample set is adapted to the candidate set, and the optimal model can be used for intelligently, quickly and accurately evaluating the recommendation scores of all city characteristic data in the candidate set, so that a more accurate and reliable recommendation result can be obtained.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an exemplary system architecture to which city goal recommendation methods and apparatus may be applied, according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a city goal recommendation method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a schematic diagram of constructing source data, in accordance with an embodiment of the disclosure;

FIG. 4 schematically illustrates a flow diagram of a method of model training in accordance with an embodiment of the present disclosure;

FIG. 5 schematically illustrates a schematic diagram of training set iterative optimization according to an embodiment of the present disclosure;

FIG. 6 schematically shows a block diagram of a city goal recommendation device according to an embodiment of the disclosure; and

FIG. 7 schematically shows a block diagram of a computer device according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".

The embodiment of the disclosure provides a city target recommendation method and device. The method comprises a source data acquisition process, a target type to be recommended acquisition process, a model training process, a model prediction process and a recommendation result output process. The method comprises the steps of extracting a sample set and a candidate set from source data based on the type of a target to be recommended, carrying out a model training process based on the sample set, carrying out predictive scoring on data in the candidate set based on an optimal model obtained by training, and outputting a corresponding recommendation result based on the predictive scoring.

Fig. 1 schematically illustrates an exemplary system architecture 100 to which city goal recommendation methods and apparatus may be applied, according to an embodiment of the disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various client applications installed thereon, such as a shopping-like application, a web browser application, a target recommendation-like application, a search recommendation-like application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data of the city target recommendation request, the type of the target to be recommended, and the like, and feed back a processing result (for example, a recommendation result, a webpage, information, data, and the like obtained or generated according to a user request) to the terminal device.

It should be noted that the city goal recommendation method provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, the city goal recommendation device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The city goal recommendation method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the city goal recommendation device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

Alternatively, the city goal recommendation method provided by the embodiments of the present disclosure may also be executed by the

terminal devices

101, 102, and 103. Accordingly, the city goal recommending apparatus provided by the embodiments of the present disclosure may be generally disposed in the

terminal devices

101, 102, 103. The city goal recommendation method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the

terminal devices

101, 102, 103 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Correspondingly, the city goal recommending apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the

terminal devices

101, 102, 103 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative, and that there may be any number of terminal devices, networks, and servers, as desired for an implementation.

Fig. 2 schematically shows a flow chart of a city goal recommendation method according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes acquiring various types of city feature data as source data in operation S210.

The city characteristic data of each type in the present operation may be related characteristic data of each type of target in a designated city, for example, may be characteristic data of shops distributed in a city, characteristic data of urban traffic, characteristic data of residences distributed in a city, characteristic data of greenbelts distributed in a city, etc., without limitation, and any characteristic data of a city target that may have a recommendation requirement may be used as the source data of the present solution.

Then, in operation S220, a target type to be recommended is acquired.

In operation S230, the city feature data that meets the target type and meets the preset recommendation condition in the source data is used as a candidate set, and the city feature data that meets the target type and does not meet the preset recommendation condition in the source data is used as a sample set.

In the operation, the preset recommendation condition refers to a condition for judging whether the city feature data can be listed as a candidate recommendation target under the condition that the types are met, the preset recommendation condition can be different under different conditions and needs to be determined according to the user requirement corresponding to the target to be recommended, so that the city feature data which is in line with the recommendation target type and can be directly used by the user according to the user requirement is taken as a candidate set, a better city feature data is selected from the candidate set and recommended to the user, and the rest city feature data which is in line with the recommendation target type and cannot be used by the user can be taken as a sample set to provide reference for the city feature data in the evaluation candidate set.

In operation S240, the initial model is trained based on the sample set to obtain an optimal model.

In operation S250, a recommendation score corresponding to each city feature data in the candidate set is obtained based on the optimal model.

In operation S260, a recommendation result is output based on the recommendation score.

It should be noted that the city target recommendation method shown in fig. 2 may be implemented on a server side or a client side, when the method is implemented on the server side, the source data is obtained in operation S210, the target type to be recommended submitted by the client may be received in operation S220, a sample set and a candidate set are partitioned from the source data in operations S230 to S250, an optimal model is obtained according to training of the sample set, a recommendation score of each city feature data in the candidate set is obtained according to the optimal model, a recommendation result is output to the client based on the recommendation score in operation S260, and the recommendation result is output by the client and displayed to a user. When the method is implemented on a client side, the source data is acquired in operation S210, the target type to be recommended input or selected by a user can be received in operation S220, a sample set and a candidate set are divided from the source data in operations S230 to S250, an optimal model is obtained according to the training of the sample set, a recommendation score of each city feature data in the candidate set is obtained according to the optimal model, and a recommendation result is output to the user based on the recommendation score in operation S260.

It can be seen that the method shown in fig. 2 performs model training based on a sample set in source data, automatically evaluates data to be recommended in a candidate set in the source data by using an optimal model obtained by training, and further can output a city target recommendation result according to the evaluation result, wherein the optimal model obtained by the sample set training is adapted to the candidate set because the sample set and the candidate set correspond to the same city target type, and recommendation scores of city feature data in the candidate set can be intelligently, quickly and accurately evaluated by using the optimal model, so that a more accurate and reliable recommendation result can be obtained.

In an embodiment of the present disclosure, the obtaining, in operation S210, each type of city feature data as source data in the method shown in fig. 2 includes: dividing the region where the city is located into a plurality of region blocks according to a first preset rule, dividing the acquired city feature data into the region blocks, and associating different types of city feature data in the same region block and/or associating the same type of city feature data in different region blocks in the source data. The city is a designated target city for target recommendation, each city feature data is divided into each region block according to the position information corresponding to each city feature data, source data can be recorded based on the association between different region blocks to which the same type of city feature data belongs, or the source data can be recorded based on the association between different types of city feature data in the same region block, so that the source data can describe the target city according to the division angle of the region blocks, can be called as a transverse feature system, can describe the target city according to the division angle of the type of the city feature data, can be called as a longitudinal feature system, and thus the source data can describe the features of the target city more comprehensively, comprehensively and abundantly from different angles, provide data feature support for multi-dimensional recommendation in the target city, and the transverse feature system describes the problem by the whole city angle, the vertical feature system describes the problem by the direction of richness of the regional content.

FIG. 3 schematically shows a schematic diagram of constructing source data according to an embodiment of the disclosure. The target city is city a, and city target recommendation is performed in city a, so that city feature data corresponding to city a needs to be acquired as source data. As shown in fig. 3, the area where the city a is located is divided into 9 area blocks, and the longitude and latitude corresponding to each area block can be converted into character strings through a GeoHash algorithm, and the character strings are used as codes corresponding to each area block. The acquired city characteristic data of the city a is divided into area blocks according to respective location information, the same type of city characteristic data in different area blocks are associated, fig. 3 shows a set of first type city characteristic data, a set of second type city characteristic data, … … and a set of nth type city characteristic data distributed in 9 area blocks, which are respectively marked as a first characteristic group, a second characteristic group, … … and an nth characteristic group in the figure, and a plurality of characteristic groups constitute all source data. In this example, dividing the city into a plurality of region blocks can integrate city feature information of different points too dense in the city, and aggregate point features scattered in the city into region features from a spatial perspective, so as to better describe the target city. Each region may include a feature group composed of various types of city feature data, including but not limited to a business district feature group, a passenger group feature group, a traffic feature group, a time period feature group, and the like, which is not limited herein, and is intended to enrich city feature information of each region and construct a city feature data network.

Further, in an embodiment of the present disclosure, in order to ensure the validity of the city feature data in the source data, the obtaining, in operation S210 of the method shown in fig. 2, each type of city feature data as the source data includes: and performing data cleaning on the acquired urban characteristic data, removing dirty data, expired data, redundant data and the like in the urban characteristic data, and forming source data based on the urban characteristic data after the data cleaning.

Fig. 4 schematically shows a flowchart of a method for training a model according to an embodiment of the present disclosure, so as to illustrate a process of training an initial model based on the sample set by operation S240 in the method shown in fig. 2 to obtain an optimal model, and the method shown in fig. 4 is implemented after the partitioning of the candidate set and the sample set in the source data is completed by operation S230 in the method shown in fig. 2.

As shown in fig. 4, the method includes adding an output value flag corresponding to each city feature data in the sample set in operation S241.

The operation is to mark output values added to each city feature data in the sample set so that the subsequent model training process is a supervised learning process.

In operation S242, a plurality of training sets, each including a plurality of pieces of city feature data labeled with output values, are extracted from the sample set according to a second preset rule, where each training set is different.

In operation S243, for each training set, taking part or all of the city feature data in the sample set except the training set as a verification set corresponding to the training set, training an initial model using the training set to obtain a training model, verifying the training model using the verification set, and obtaining an evaluation score of the training model according to a verification result.

In operation S244, the training model with the highest evaluation score is used as the optimal model, and the training set corresponding to the training model is used as the optimal feature combination.

Then, on the basis of the method shown in fig. 4, the trained optimal model may predict the output value of the city feature data in the candidate set, and in an embodiment of the present disclosure, obtaining, by operation S250 of the method shown in fig. 2, a recommendation score corresponding to each city feature data in the candidate set based on the optimal model includes: and inputting each city feature data in the candidate set into the optimal model to obtain corresponding output values, wherein the output value corresponding to each city feature data is a recommendation score corresponding to the city feature data. The operation S260 of outputting a recommendation result based on the recommendation score includes: and sorting the city feature data from high to low according to the recommendation scores of the city feature data in the candidate set, and selecting and outputting the preset number of city feature data as recommendation results.

As an optional embodiment, in order to further optimize the prediction result of the optimal model on the city feature data in the candidate set, the inputting each city feature data in the candidate set to the optimal model to obtain the corresponding output value includes: and for each city feature data in the candidate set, inputting the city feature data to the optimal model for multiple times to obtain multiple output values. And performing cross validation optimization on the output values to obtain optimized output values, and taking the optimized output values as the recommendation scores of the city characteristic data. After the optimal model is found, stability optimization is performed on the output result to avoid instability of a single output structure, so that recommendation scores corresponding to city feature data in a candidate set are more accurate and reliable.

It can be seen that the method shown in fig. 4 explains operation S240 shown in fig. 2, different training models are obtained by continuously transforming combinations of training sets in a sample set, and the obtained training models are verified by a verification set in the sample set, so that an optimal model and a corresponding optimal feature combination can be effectively found out, so that subsequent recommendation results are more accurate, and a complete process of city target recommendation can be realized by combining with other operation processes in fig. 2. The following is a description with specific examples:

the method comprises the steps of carrying out city target recommendation by taking a city A as a target city, firstly obtaining various types of city characteristic data of the city A, cleaning the obtained city characteristic data, taking the cleaned city characteristic data as source data, firstly extracting a longitudinal characteristic system in each regional block divided in the city A, and then constructing a transverse characteristic system between cross-regional blocks on each type of city characteristic data, so that each information point in the city A can be connected into pieces in a regional block mode by the source data, and different regional blocks are aggregated into the characteristic system describing the characteristics of the whole city by constructing the transverse characteristic system. And then obtaining the type of the target to be recommended, wherein the type of the target to be recommended defines the recommendation problem to be solved in the range of the city A, including but not limited to shop site selection recommendation, operation scheme recommendation and the like, and the purpose of the method is to provide a necessary scheme for a subsequent design of a basic model for solving the recommendation problem through good definition of the recommendation problem. Taking a shop location problem as an example, in this example, the type of the target to be recommended is a shop, and the corresponding user requirement may be to find a shop that is most suitable for business through city target recommendation of the shop type. Extracting a corresponding candidate set and a sample set from source data according to the type of the target to be recommended, in this example, when the type of the target to be recommended is a shop, a preset recommendation condition may be that the target is in a lease status, city feature data which belongs to the shop type and is in the lease status in the source data is used as the candidate set, and city feature data which belongs to the shop type but is not in the lease status in the source data is used as the sample set. And constructing a basic model for solving a specific recommendation problem corresponding to the target to be recommended, specifically, constructing the basic model for solving the recommendation target from the aspects of characteristics and algorithm, and taking the basic model as an initial model for subsequent training.

Adding an output value mark corresponding to the city feature data into each city feature data in the sample set, in this example, each city feature data in the sample set is the city feature data which belongs to the type of the shop but is not in the renting state, for example, the city feature data related to the shop already in the business state, each city feature data in the sample set may include related information such as position information, passenger flow information, traffic condition information, and ambient environment information of the corresponding shop, and according to the related information, an output value mark corresponding to the city feature information may be calculated, and the output value mark may be used as an index for evaluating the business condition of the corresponding shop, and by adding the output value mark into the city feature data in the sample set, the model trained based on the sample set can predict the model not yet in business in the candidate set, How well the shop is in the state of renting will be in the future.

And extracting different training sets from the sample set added with the output value marks for a plurality of times to perform training attempts on the initial model so as to obtain an optimal model. The method includes the steps that a training set can be selected for multiple times in a sample set in a forward and/or backward searching mode, specifically, a breadth-first searching algorithm is adopted in the sample set to conduct multiple times of selection on city feature data in a forward and/or backward traversing mode to obtain each training set, for each training set, part or all of the city feature data except the training set in the sample set are used as verification sets corresponding to the training sets, the training sets are used for training initial models to obtain training models, the verification sets are used for verifying the training models, evaluation scores of the training models are obtained according to verification results, the training models with the highest evaluation scores are used as optimal models, and the training sets corresponding to the training models are used as optimal feature combinations. The process can be carried out in the following manner, for the training set T1 extracted for the first time, using part or all of the city feature data in the sample set except the extracted training set T1 as a verification set V1 corresponding to the training set Tl, using the training set T1 to train the initial model to obtain a training model M1, using a corresponding verification set V1 to verify the trained training model M1, obtaining an evaluation score of the training model M1 according to the verification result, judging whether the training model M1 is optimized relative to the initial model through the evaluation score, if so, continuing to extract the training set T2 on the basis of the training set T1, using part or all of the city feature data in the sample set except the training set T2 as the verification set V2, using the training set T2 to train on the basis of the initial model or the training model M1 to obtain the training model M2, and verifying the trained training model M2 by using a corresponding verification set V2, obtaining an evaluation score of the training model M2 according to a verification result, judging whether the training model M2 is optimized relative to the training model M1 or not through the evaluation score, if so, continuing to extract the training set for the third time, and so on until the training set serving as the optimal feature combination is found, and training the optimal model with the highest evaluation score. In the above process, if the model corresponding to the newly extracted training set is inferior to the model corresponding to the previously extracted training set, the new training set needs to be extracted by performing the search scan again from the sample set.

Fig. 5 schematically shows a schematic diagram of training set iterative optimization according to an embodiment of the present disclosure. In the process, the optimal feature combination is finally found through continuous iterative optimization of the training set, the optimal model can be obtained through training by using the optimal feature combination, as can be seen from fig. 5, the training set extracted from the sample set of the source data is gradually reduced in the iterative optimization process, a tower-shaped structure is formed, the training set can be called as an urban data tower, and the optimization process of the sample data required for solving the problem of the corresponding target to be recommended in the city A is represented. Any city goal recommendation problem can eventually be solved by constructing such a corresponding city data tower.

Further, as an optional embodiment, when the effect of the training model obtained by the training is no longer improved, the current training set may be changed by using a simulated annealing algorithm and a random search method to obtain a new training set, and whether the new training set is optimized with respect to the previous training set is determined, and if so, the process of optimizing the training set may be repeated on the basis until the training model is no longer optimized by the adjusted training set. Therefore, by adding the simulated annealing algorithm and the random search method, the possibility of finding the optimal combination in the iterative optimization process of the training set is improved, namely when the iterative optimization process cannot continuously obtain a training model with better effect, one or more urban feature data in the training set is randomly replaced or one or more urban feature combinations are randomly added in the training set, and the problem that the local optimal feature combination is possibly trapped in the iterative optimization process of the training set can be avoided as much as possible.

And after finding the optimal model corresponding to the optimal feature combination, performing greedy cross validation on the optimal model, specifically, cutting the sample set to obtain a plurality of subsets, and performing cross validation on the optimal model by using the plurality of subsets to avoid the contingency in model training and train the optimal model with better stability.

In this example, after the optimal model corresponding to the optimal feature model is obtained, each city feature data in the candidate set is input to the optimal model, the optimal model outputs an output value corresponding to each city feature data, the output value is used as a recommendation score of the corresponding city feature data, the city feature data in the candidate set is sorted according to the recommendation score from high to low, a preset number of city feature data are output as recommendation results, which is equivalent to recommending the shops corresponding to the preset number of city recommendation data to the user for the user to shop address. Other types of city target recommendations are similar to the above and are not described in detail.

Fig. 6 schematically shows a block diagram of a city goal recommendation device according to an embodiment of the present disclosure.

As shown in fig. 6, the article search device 600 includes a first obtaining module 610, a second obtaining module 620, and a recommendation processing module 630.

The first obtaining module 610 is configured to obtain various types of city feature data as source data.

The second obtaining module 620 is configured to obtain a target type to be recommended.

The recommendation processing module 630 is configured to use, as a candidate set, city feature data that meets the target type and meets a preset recommendation condition in source data, use, as a sample set, city feature data that meets the target type and does not meet the preset recommendation condition in the source data, train an initial model based on the sample set, obtain an optimal model, obtain, based on the optimal model, a recommendation score corresponding to each city feature data in the candidate set, and output a recommendation result based on the recommendation score.

It can be seen that the apparatus shown in fig. 6 performs model training based on a sample set in source data, automatically evaluates data to be recommended in a candidate set in the source data by using an optimal model obtained by training, and then outputs a city target recommendation result according to the evaluation result, wherein the optimal model obtained by the sample set training is adapted to the candidate set because the sample set and the candidate set correspond to the same city target type, and recommendation scores of city feature data in the candidate set can be intelligently, quickly, and accurately evaluated by using the optimal model, so that a more accurate and reliable recommendation result can be obtained.

In an embodiment of the disclosure, the acquiring, by the first acquiring module 610, each type of city feature data as a source data includes: the first obtaining module 610 is configured to divide an area where the city is located into a plurality of area blocks according to a first preset rule, and divide the obtained city feature data into the area blocks. In the source data, different types of city feature data within the same region block are associated, and/or the same type of city feature data in different region blocks are associated.

In an embodiment of the disclosure, the recommending and processing module 630 trains the initial model based on the sample set, and obtaining the optimal model includes: the recommendation processing module 630 is configured to add an output value label corresponding to each piece of city feature data in the sample set, and extract a plurality of training sets from the sample set according to a second preset rule, where each training set includes a plurality of pieces of city feature data with output value labels, and each training set is different; for each training set, taking part or all of the city feature data except the training set in the sample set as a verification set corresponding to the training set, training an initial model by using the training set to obtain a training model, verifying the training model by using the verification set, obtaining an evaluation score of the training model according to a verification result, taking the training model with the highest evaluation score as an optimal model, and taking the training set corresponding to the training model as an optimal feature combination.

As an optional embodiment, the extracting, by the recommendation processing module 630, a plurality of training sets from the sample set according to a second preset rule includes: the recommendation processing module 630 is configured to select the city feature data for multiple times in a forward and/or backward traversal manner by using a breadth-first search algorithm in the sample set to obtain each training set.

As another alternative, the extracting, by the recommendation processing module 630, a plurality of training sets from the sample set according to a second preset rule includes: the recommendation processing module 630 is configured to change the training set according to the extracted training set by using a simulated annealing algorithm and/or a random search method, so as to obtain a new training set.

In an embodiment of the disclosure, obtaining, by the recommendation processing module 630, a recommendation score corresponding to each city feature data in the candidate set based on the optimal model includes: the recommendation processing module 630 is configured to input each city feature data in the candidate set to the optimal model, and obtain a corresponding output value, where the output value corresponding to each city feature data is a recommendation score corresponding to the city feature data. And, the recommendation processing module 630 outputting the recommendation result based on the recommendation score includes: the recommendation processing module 630 is configured to sort the city feature data from high to low according to the recommendation scores of the city feature data in the candidate set, and select a preset number of city feature data as recommendation results and output the recommendation results.

Specifically, the recommending and processing module 630 inputs the city feature data in the candidate set into the optimal model, and obtaining the corresponding output value may include: the recommendation processing module 630 is configured to, for each city feature data in the candidate set, input the city feature data to the optimal model for multiple times to obtain multiple output values, perform cross validation optimization on the multiple output values to obtain an optimized output value, and use the optimized output value as a recommendation score of the city feature data.

As an alternative embodiment, the acquiring, by the first acquiring module 610, each type of city feature data as a source data includes: the first obtaining module 610 is configured to perform data cleaning on the obtained city feature data, where the city feature data after the data cleaning constitutes source data.

It should be noted that the city target recommendation apparatus 600 shown in fig. 6 may be configured on a server side or a client side, when the city target recommendation apparatus 600 is configured on the server side, the first obtaining module 610 obtains source data, the second obtaining module 620 may receive a target type to be recommended submitted by a client, the recommendation processing module 630 divides a sample set and a candidate set from the source data, obtains an optimal model according to training of the sample set, obtains a recommendation score of each city feature data in the candidate set according to the optimal model, outputs a recommendation result to the client based on the recommendation score, and outputs and displays the recommendation result to a user by the client. When the city target recommendation device 600 is configured on the client side, the first obtaining module 610 obtains source data, the second obtaining module 620 may receive a target type to be recommended input or selected by a user, the recommendation processing module 630 divides a sample set and a candidate set from the source data, trains according to the sample set to obtain an optimal model, obtains a recommendation score of each city feature data in the candidate set according to the optimal model, and outputs a recommendation result to the user based on the recommendation score in operation S260.

It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module/unit/subunit and the like in the apparatus part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described herein again.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any plurality of the first obtaining module 610, the second obtaining module 620, and the recommendation processing module 630 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 610, the second obtaining module 620, and the recommendation processing module 630 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of the three. Alternatively, at least one of the first obtaining module 610, the second obtaining module 620 and the recommendation processing module 630 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.

Fig. 7 schematically shows a block diagram of a computer device adapted to implement the above described method according to an embodiment of the present disclosure. The computer device shown in fig. 7 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.

As shown in fig. 7, a computer device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 703, various programs and data necessary for the operation of the computer apparatus 700 are stored. The processor 701, the ROM702, and the RAM 703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM702 and/or the RAM 703. It is noted that the programs may also be stored in one or more memories other than the ROM702 and RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the computer device 700 may also include an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The system 700 may also include one or more of the following components connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

The present disclosure also provides a computer-readable medium, which may be embodied in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer readable medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, a computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical fiber cable, radio frequency signals, etc., or any suitable combination of the foregoing.

For example, according to embodiments of the present disclosure, a computer-readable medium may include the ROM702 and/or the RAM 703 and/or one or more memories other than the ROM702 and the RAM 703 described above.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A city goal recommendation method comprises the following steps:

acquiring various types of city characteristic data as source data;

acquiring a target type to be recommended;

taking the city feature data which accord with the target type and accord with the preset recommendation condition in the source data as a candidate set, and taking the city feature data which accord with the target type and do not accord with the preset recommendation condition in the source data as a sample set;

training an initial model based on the sample set to obtain an optimal model;

obtaining recommendation scores corresponding to the city feature data in the candidate set based on the optimal model;

and outputting a recommendation result based on the recommendation score.

2. The method of claim 1, wherein the obtaining each type of city feature data as source data comprises:

dividing the area where the city is located into a plurality of area blocks according to a first preset rule;

dividing the acquired urban characteristic data into area blocks;

in the source data, different types of city feature data within the same region block are associated, and/or the same type of city feature data in different region blocks are associated.

3. The method of claim 1, wherein the training an initial model based on the sample set, resulting in an optimal model comprises:

adding an output value mark corresponding to the city feature data in each city feature data in the sample set;

extracting a plurality of training sets from the sample set according to a second preset rule, wherein each training set comprises a plurality of pieces of city characteristic data with output value marks, and each training set is different;

for each training set, taking part or all of the city feature data except the training set in the sample set as a verification set corresponding to the training set, training an initial model by using the training set to obtain a training model, verifying the training model by using the verification set, and obtaining an evaluation score of the training model according to a verification result;

and taking the training model with the highest evaluation score as an optimal model, and taking a training set corresponding to the training model as an optimal feature combination.

4. The method of claim 3, wherein the extracting the plurality of training sets from the sample set according to a second preset rule comprises:

and selecting the urban feature data for multiple times in a mode of traversing forwards and/or backwards in the sample set by adopting a breadth-first search algorithm to obtain each training set.

5. The method of claim 3, wherein the extracting the plurality of training sets from the sample set according to a second preset rule comprises:

and for the extracted training set, changing the training set by using a simulated annealing algorithm and/or a random search method to obtain a new training set.

6. The method of claim 3, wherein:

the obtaining of the recommendation score corresponding to each city feature data in the candidate set based on the optimal model includes: inputting each city feature data in the candidate set into the optimal model to obtain corresponding output values, wherein the output value corresponding to each city feature data is a recommendation score corresponding to the city feature data;

the outputting of the recommendation based on the recommendation score includes: and sorting the city feature data from high to low according to the recommendation scores of the city feature data in the candidate set, and selecting and outputting the preset number of city feature data as recommendation results.

7. The method of claim 6, wherein said inputting each city feature data in the candidate set to the optimal model, obtaining a respective output value comprises:

for each city feature data in the candidate set, inputting the city feature data to the optimal model for multiple times to obtain multiple output values;

and performing cross validation optimization on the output values to obtain optimized output values, and taking the optimized output values as the recommendation scores of the city characteristic data.

8. The method of claim 1, wherein obtaining each type of city feature data as source data comprises:

and carrying out data cleaning on the acquired urban characteristic data, wherein the urban characteristic data after the data cleaning forms source data.

9. A city goal recommendation device comprising:

the first acquisition module is used for acquiring various types of city characteristic data as source data;

the second acquisition module is used for acquiring the type of the target to be recommended;

the recommendation processing module is used for taking the urban feature data which accord with the target type and accord with the preset recommendation condition in the source data as a candidate set and taking the urban feature data which accord with the target type and do not accord with the preset recommendation condition in the source data as a sample set; training an initial model based on the sample set to obtain an optimal model; obtaining recommendation scores corresponding to the city feature data in the candidate set based on the optimal model; and outputting a recommendation result based on the recommendation score.

10. The apparatus of claim 9, wherein the first obtaining module obtaining various types of city feature data as source data comprises:

the first acquisition module is used for dividing the area where the city is located into a plurality of area blocks according to a first preset rule; dividing the acquired urban characteristic data into area blocks; in the source data, different types of city feature data within the same region block are associated, and/or the same type of city feature data in different region blocks are associated.

11. The apparatus of claim 9, wherein the recommendation processing module trains initial models based on the sample set, resulting in optimal models comprising:

the recommendation processing module is used for adding an output value mark corresponding to the city feature data in each city feature data in the sample set; extracting a plurality of training sets from the sample set according to a second preset rule, wherein each training set comprises a plurality of pieces of city characteristic data with output value marks, and each training set is different; for each training set, taking part or all of the city feature data except the training set in the sample set as a verification set corresponding to the training set, training an initial model by using the training set to obtain a training model, verifying the training model by using the verification set, and obtaining an evaluation score of the training model according to a verification result; and taking the training model with the highest evaluation score as an optimal model, and taking a training set corresponding to the training model as an optimal feature combination.

12. The apparatus of claim 11, wherein the recommendation processing module extracts a plurality of training sets from the sample set according to a second preset rule comprises:

and the recommendation processing module is used for selecting the urban feature data for multiple times in a mode of traversing forwards and/or backwards in the sample set by adopting a breadth-first search algorithm to obtain each training set.

13. The apparatus of claim 11, wherein the recommendation processing module extracts a plurality of training sets from the sample set according to a second preset rule comprises:

and the recommendation processing module is used for changing the training set by utilizing a simulated annealing algorithm and/or a random search method for the extracted training set to obtain a new training set.

14. The apparatus of claim 11, wherein:

the obtaining, by the recommendation processing module, recommendation scores corresponding to the city feature data in the candidate set based on the optimal model includes: the recommendation processing module is used for inputting each city feature data in the candidate set into the optimal model to obtain corresponding output values, and the output value corresponding to each city feature data is a recommendation score corresponding to the city feature data;

the recommendation processing module outputting recommendation results based on the recommendation scores comprises: and the recommendation processing module is used for sequencing the city characteristic data from high to low according to the recommendation scores of the city characteristic data in the candidate set, and selecting and outputting the city characteristic data with the preset number as a recommendation result.

15. The apparatus of claim 14, wherein the recommendation processing module inputs each city feature data in the candidate set to the optimal model, and obtaining the corresponding output value comprises:

the recommendation processing module is used for inputting each city feature data in the candidate set to the optimal model for multiple times to obtain multiple output values; and performing cross validation optimization on the output values to obtain optimized output values, and taking the optimized output values as the recommendation scores of the city characteristic data.

16. The apparatus of claim 9, wherein the first obtaining module obtaining various types of city feature data as source data comprises:

the first acquisition module is used for carrying out data cleaning on the acquired urban characteristic data, and the urban characteristic data after the data cleaning forms source data.

17. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing a city goal recommendation method as claimed in any one of claims 1-8.

18. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform a city goal recommendation method as claimed in any one of claims 1 to 8.