CN111125527A

CN111125527A - Method and device for acquiring group instance object based on user matching degree

Info

Publication number: CN111125527A
Application number: CN201911347662.5A
Authority: CN
Inventors: 殷晓明
Original assignee: Koubei Shanghai Information Technology Co Ltd
Current assignee: Koubei Shanghai Information Technology Co Ltd
Priority date: 2019-12-24
Filing date: 2019-12-24
Publication date: 2020-05-08
Anticipated expiration: 2039-12-24
Also published as: CN111125527B

Abstract

The invention discloses a method and a device for acquiring a group instance object based on user matching degree. The method comprises the following steps: extracting user characteristics of a target user; extracting the characteristics of the cluster instance object; inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of the plurality of historical users and the plurality of historical group instance objects; obtaining the matching degree of a target user and any group of example objects output by a machine learning model; and acquiring the cluster instance object matched with the target user according to the matching degree. According to the scheme, the group instance object corresponding to the target user can be accurately acquired, and the precise putting of the group instance object is convenient to realize, so that putting resources are saved, and user experience is improved.

Description

Method and device for acquiring group instance object based on user matching degree

Technical Field

The invention relates to the technical field of data processing, in particular to a method and a device for acquiring a group instance object based on user matching degree.

Background

With the continuous development of science and technology and society, group buying is more and more favored by people due to the characteristic of high cost performance. Currently, when each platform delivers the party information to the users, an undifferentiated delivery mode is generally adopted, that is, the same party information is indiscriminately delivered to all platform users.

However, the inventor finds that the following defects exist in the prior art in the implementation process: by adopting the way of putting the piecing together information in the prior art, the put piecing together information can not be matched with the user, thereby not only causing the waste of putting resources, but also further reducing the user experience.

Disclosure of Invention

In view of the above problems, embodiments of the present invention are proposed to provide a method and an apparatus for obtaining a blob instance object based on user matching degree, which overcome or at least partially solve the above problems.

According to an aspect of the embodiments of the present invention, there is provided a method for obtaining a clique instance object based on a user matching degree, including:

extracting user characteristics of a target user from user attribute data of the target user; obtaining the group instance data of at least one group instance object with the current state as the running state, and extracting the group instance characteristics of the at least one group instance object from the group instance data; inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects; obtaining the matching degree of the target user and any group of example objects output by the machine learning model; and acquiring a group instance object matched with the target user according to the matching degree.

Optionally, the user attribute data includes at least one of the following data: basic attribute data, preference attribute data, consumption capability attribute data, and reference number attribute data.

Optionally, the clique instance data includes at least one of: offer level data, crowd data, and crowd rate data.

Optionally, the interaction data of the plurality of historical users with the plurality of historical clique instance objects includes at least one of the following data: the method comprises the following steps of obtaining group example objects, wherein the group example objects comprise participation behavior data of users to the group example objects, group result data of the users to the participated group example objects, consumption data of the users to the group example objects which are successfully clustered, and collection behavior data of the users to commodities corresponding to the non-participated group example objects.

Optionally, before the inputting the user characteristics of the target user and the blob instance characteristics of any blob instance object into the pre-trained machine learning model, the method further includes: extracting user characteristics of a plurality of historical users, group example characteristics of a plurality of historical group example objects and interaction characteristics of a plurality of historical users and a plurality of historical group example objects from user attribute data of the plurality of historical users, group example data of the plurality of historical group example objects with running states in preset historical time periods and interaction data of the plurality of historical users and the plurality of historical group example objects; aiming at the combination of any historical user and any historical clique instance object, generating sample data corresponding to the combination according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interactive characteristics of the historical user and the historical clique instance object; and training the generated machine learning model by using the sample data to obtain the trained machine learning model.

Optionally, the training the constructed machine learning model by using the sample data to obtain the trained machine learning model further includes: generating positive sample data and negative sample data based on the sample data; wherein the proportion of the positive sample data to the negative sample data meets a preset proportion; and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

Optionally, before the generating, for the combination of any historical user and any historical clique object, sample data corresponding to the combination according to the user characteristics of the historical user, the clique instance characteristics of the historical clique object, and the interaction characteristics of the historical user and the historical clique object, the method further includes: acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of any historical user and any historical community instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

Optionally, after the obtaining of the matching degree of the target user output by the machine learning model and any clique instance object, the method further includes: calculating the clustering success rate of any clustering instance object; correcting the matching degree of the target user and any group of example object by using the clustering success rate of any group of example object so as to obtain a corrected value of the matching degree of the target user and any group of example object; the obtaining the blob instance object matched with the user according to the matching degree further comprises: and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

Optionally, the calculating the clustering success rate of any clustering instance object further includes: acquiring the clustering target number and the current clustering number of any clustering example object; and calculating the clustering success rate of any clustering example object according to the clustering target number and the current clustering reference number of any clustering example object.

Optionally, the obtaining the blob instance object matched with the user according to the matching degree further includes: sorting the at least one group instance object according to the matching degree; and acquiring a group instance object matched with the target user according to the sequencing result.

Optionally, the obtaining of the clique instance data of at least one clique instance object whose current state is the running state further includes: acquiring commodity information of at least one commodity matched with the target user according to the user attribute data of the target user; and acquiring the group instance data of at least one group instance object of which the current state is the running state and corresponds to the commodity information.

Optionally, after the obtaining of the blob instance object matched with the target user according to the matching degree, the method further includes: and delivering the group instance object at the user terminal of the target user.

According to another aspect of the embodiments of the present invention, there is provided a device for obtaining a clique instance object based on a user matching degree, including: the user characteristic extraction module is suitable for extracting the user characteristics of the target user from the user attribute data of the target user; the cluster instance data acquisition module is suitable for acquiring cluster instance data of at least one cluster instance object with the current state as the running state; the cluster instance feature extraction module is suitable for extracting the cluster instance features of the at least one cluster instance object from the cluster instance data; the input module is suitable for inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a machine learning model trained in advance; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects; the matching degree obtaining module is suitable for obtaining the matching degree of the target user and any group of example objects output by the machine learning model; and the group instance object acquisition module is suitable for acquiring the group instance object matched with the target user according to the matching degree.

Optionally, the apparatus further comprises: the historical characteristic extraction module is suitable for extracting the user characteristics of a plurality of historical users, the group example characteristics of a plurality of historical group example objects and the interactive characteristics of a plurality of historical users and a plurality of historical group example objects from the user attribute data of the plurality of historical users, the group example data of the plurality of historical group example objects with the running states in the preset historical time period and the interactive data of the plurality of historical users and the plurality of historical group example objects; the sample data generating module is suitable for generating sample data corresponding to any combination of any historical user and any historical clique instance object according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interactive characteristics of the historical user and the historical clique instance object; and the training module is suitable for training the generated machine learning model by using the sample data to obtain the trained machine learning model.

Optionally, the training module is further adapted to: generating positive sample data and negative sample data based on the sample data; wherein the proportion of the positive sample data to the negative sample data meets a preset proportion; and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

Optionally, the apparatus further comprises: the system comprises a Cartesian operation module, a database and a database, wherein the Cartesian operation module is suitable for acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of any historical user and any historical community instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

Optionally, the apparatus further comprises: the correction module is suitable for calculating the clustering success rate of any clustering example object; correcting the matching degree of the target user and any group of example object by using the clustering success rate of any group of example object so as to obtain a corrected value of the matching degree of the target user and any group of example object; the blob instance object acquisition module is further adapted to: and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

Optionally, the modification module is further adapted to: acquiring the clustering target number and the current clustering number of any clustering example object; and calculating the clustering success rate of any clustering example object according to the clustering target number and the current clustering reference number of any clustering example object.

Optionally, the apparatus further comprises: the sorting module is suitable for sorting the at least one group instance object according to the matching degree; the blob instance object acquisition module is further adapted to: and acquiring a group instance object matched with the target user according to the sequencing result.

Optionally, the clique instance data obtaining module is further adapted to: acquiring commodity information of at least one commodity matched with the target user according to the user attribute data of the target user; and acquiring the group instance data of at least one group instance object of which the current state is the running state and corresponds to the commodity information.

Optionally, the apparatus further comprises: and the releasing module is suitable for releasing the group instance object at the user terminal of the target user.

According to still another aspect of an embodiment of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the method for acquiring the group instance object based on the user matching degree.

According to still another aspect of the embodiments of the present invention, there is provided a computer storage medium, where at least one executable instruction is stored, and the executable instruction causes a processor to execute an operation corresponding to the above-mentioned clique instance object obtaining method based on the user matching degree.

According to the method and the device for acquiring the group instance object based on the user matching degree, provided by the embodiment of the invention, the user characteristics of the target user are extracted, and the group instance characteristics of the group instance object are extracted; inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of the plurality of historical users and the plurality of historical group instance objects; obtaining the matching degree of a target user and any group of example objects output by a machine learning model; and acquiring the cluster instance object matched with the target user according to the matching degree. According to the scheme, the group instance object corresponding to the target user can be accurately acquired, and the precise putting of the group instance object is convenient to realize, so that putting resources are saved, and user experience is improved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a schematic flowchart illustrating a method for obtaining a blob instance object based on user matching degree according to an embodiment of the present invention;

fig. 2 is a schematic flowchart illustrating a method for obtaining a blob instance object based on user matching degree according to a second embodiment of the present invention;

fig. 3 is a flowchart illustrating a method for obtaining a clique instance object based on a user matching degree according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram illustrating a blob instance object obtaining apparatus based on user matching degree according to a fourth embodiment of the present invention;

fig. 5 shows a schematic structural diagram of a computing device according to a sixth embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

Example one

Fig. 1 is a flowchart illustrating a method for obtaining a blob instance object based on user matching degree according to an embodiment of the present invention. The method for acquiring the group instance object provided by the embodiment can be applied to service platforms of various industries, for example, a local life service platform, an online e-commerce platform, a take-out platform, and the like. Specifically, the method for acquiring a blob instance object provided by this embodiment can be executed by a computing device with corresponding computing capability, and this embodiment does not limit the specific type of the computing device.

As shown in fig. 1, the method includes:

step S110: and extracting the user characteristics of the target user from the user attribute data of the target user.

In order to be able to deliver the matched group instance object to the user, the present embodiment obtains the user attribute data of the target user. Wherein the user attribute data comprises at least one of the following data: basic attribute data, preference attribute data, consumption capability attribute data, and reference number attribute data. Specifically, the basic attribute data may include data such as age, occupation, and/or gender, and may be obtained from basic information or the like input by the user when registering the platform; the preference attribute data is specifically data reflecting preference habits of the user, and the data can be obtained from historical consumption and browsing data of the user, for example, the commodity category preference of the user is determined according to the commodity category information which is consumed most in the user history; the consumption capability attribute data can comprise historical consumption capability data of the user and/or current consumption capability data of the user, wherein the historical consumption capability data of the user can be obtained according to data such as historical consumption records of the user, and the current consumption capability data of the user can be obtained according to account information of the user in a preset platform; in addition, the clustering frequency attribute data of the user can be obtained according to the historical frequency information of the user participating in the clustering activities. In short, the present embodiment does not limit the specific acquisition method of the user attribute data.

Further, after the user attribute data of the target user is obtained, the user features of the target user are extracted from the user attribute data, and the user features can embody the relevant characteristics of the target user in a centralized manner. In this embodiment, the specific feature extraction method is not limited. For example, if the acquired user attribute data includes structured data, the target user feature may be directly extracted according to the related attribute information of the structured data table, for example, the gender data of the target user is directly extracted through a related query statement from the data table including the user ID and the gender attribute; if the acquired user attribute data contains unstructured data (such as long text data), word segmentation processing can be firstly carried out on the unstructured data, so that a plurality of text word segments are acquired; and further extracting characteristic words from the text participles, and taking the characteristic words as the user characteristics of the target user.

Step S120: and acquiring the cluster instance data of at least one cluster instance object with the current state as the running state, and extracting the cluster instance characteristics of at least one cluster instance object from the cluster instance data.

The cluster instance object obtained in this step is specifically a cluster instance object currently in a running state. In the specific implementation process, the effective operation time period and the current time of at least one group instance object can be obtained, whether the effective operation time period of any group instance object contains the current time or not is judged, and if yes, the group instance data of the group instance object is obtained.

Specifically, the clique instance data includes at least one of the following data: offer level data, crowd data, and crowd rate data. The preference degree data further comprises preference proportion and/or preference amount of the group instance object; the group number data comprises a target group number corresponding to the group example object (wherein, when the group number reaches the target group number, the group is indicated to be formed) and a current group number; the clustering rate data includes a clustering proportion corresponding to the current instance object of the cluster.

Further, the cluster instance characteristics of at least one cluster instance object are extracted from the acquired cluster instance data, and the cluster instance characteristics can embody the specific characteristics of the cluster instance object. For example, reference may be made to the description of the corresponding part in step S110, and this step is not described herein again.

Step S130, inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a machine learning model trained in advance; the machine learning model trained in advance is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period, and interaction data of the plurality of historical users and the plurality of historical group instance objects.

In this embodiment, a machine learning model is pre-constructed, and the constructed machine learning model is trained by using the acquired historical data, so as to obtain a trained machine learning model. Wherein, the historical data specifically comprises: the user attribute data of a plurality of historical users, the clique instance data of a plurality of historical clique instance objects of which the states are running states in a preset historical time period, and the interaction data of a plurality of historical users and a plurality of historical clique instance objects. Optionally, the interaction data of the plurality of historical users with the plurality of historical community instance objects includes at least one of the following data: the data comprises participation behavior data of users on the group instance object (the data comprises participation group instance objects or non-participation group instance objects), clustering result data of the users on the participation group instance object (the data comprises successful clustering of the participation group instance object or failed clustering of the participation group instance object), consumption data of the users on the successfully clustered group instance object (the data comprises successful consumption of the successfully clustered group instance object and unsuccessful consumption of the non-clustered group instance object), and collection behavior data of the users on commodities corresponding to the non-participated group instance object (the commodities corresponding to the non-participated group instance object have collection behaviors or the commodities corresponding to the non-participated group instance object have no collection behaviors). The user participation behavior data of the user to the group instance object can be that the historical user has participated in the group instance object or has not participated in the group instance object; the user's piecing result data of the participating community instance objects can be the user's consumption behavior or the user's non-consumption behavior of the participating community instance objects.

In the embodiment, the machine learning model is trained by using the user attribute data of the historical user, the group instance data of the historical group instance object and the interaction data of the historical user and the historical group instance objects, so that the machine learning model can acquire the group reference results corresponding to different combinations of user characteristics and group instance characteristics, the preference degrees of different users to different group instances are accurately acquired through the group reference results, and the matching degree of the target user and any group instance object can be accurately predicted.

In an alternative embodiment, when the number of the clique instance objects of which the current state is the running state is large, the data input to the machine learning model is avoided from being missed. In this embodiment, a current group instance object set may be generated, a cartesian product operation may be performed on a user set including a target user and the current group instance object set, so as to obtain a cartesian set, and a combination of the target user and any group instance object may be determined according to the generated cartesian set, where each set element in the cartesian set corresponds to a combination of the target user and one group instance object. By adopting the method, the combination of the target user and the group instance object with the current state as the running state can be accurately obtained, so that the data input to the machine learning model is avoided being omitted; and moreover, all combinations of the target user and the group instance object in the running state at present can be quickly determined only through single operation, so that the acquisition efficiency of the combination formed by the target user and different group instance objects can be effectively improved, and the improvement of the overall implementation efficiency of the method is facilitated.

And step S140, acquiring the matching degree of the target user output by the machine learning model and any clique instance object.

The matching degree of the target user and any group of example objects can be accurately obtained through the machine learning model. The higher the matching degree of the target user and the group instance object is, the higher the preference degree of the target user to the group instance object is, and the more easily the target user participates in or consumes the group instance object.

And step S150, acquiring the cluster instance object matched with the target user according to the matching degree.

After the matching degree of the target user and any one of the community instance objects is obtained, the community instance object matched with the target user can be further determined based on the obtained matching degree. For example, at least one group instance object may be sorted according to the matching degree, and a group instance object matching the target user may be obtained according to the sorting result (for example, a group instance object n-th from the top of the sorted order list is used as the group instance object matching the target user); or, the group instance object with the matching degree larger than the preset matching degree is used as the group instance object matched with the target user.

Optionally, after obtaining the group instance object matched with the target user according to the matching degree, the group instance object may be further delivered at the user terminal of the target user, and the specific delivery form is not limited in this embodiment.

Therefore, in the embodiment, the machine learning model is trained by using the historical data, specifically, the user attribute data of the historical user, the clique instance data of the historical clique instance object, and the interaction data of the plurality of historical users and the plurality of historical clique instance objects, so that the trained machine learning model can accurately obtain the matching degree between the target user and the clique instance object; and then the group instance object matched with the target user is determined according to the matching degree, so that the accurate delivery of the group instance object is convenient to realize, the defects of reduced user experience and waste of delivery resources such as system transmission and the like caused by delivering the group instance object which is not interested by the user to the user in the prior art are avoided, and the improvement of the user experience is facilitated and the delivery resources are saved.

Example two

Fig. 2 is a flowchart illustrating a method for acquiring a blob instance object based on user matching degree according to a second embodiment of the present invention. The method for acquiring the clique instance object provided by the embodiment is directed to further optimization of the method for acquiring the clique instance object in the first embodiment.

As shown in fig. 2, the method includes:

step S210: a machine learning model is generated.

The present embodiment does not limit the specific type of the machine learning model. For example, a multi-neural network layer machine learning model may be employed, which includes an input layer, at least one fully-connected layer, and an output layer. The input layer user receives input data, the full-connection layer user processes the received input data, and the output layer is used for outputting results.

Step S220: and generating sample data according to the user attribute data of the plurality of historical users, the group instance data of the plurality of historical group instance objects with the running states in the preset historical time period and the interaction data of the plurality of historical users and the plurality of historical group instance objects.

In this embodiment, the historical data, that is, the group instance data of the plurality of historical group instance objects whose states are running states in the preset historical time period, and the interaction data between the plurality of historical users and the plurality of historical group instance objects are used to generate corresponding sample data.

Specifically, user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects in a running state in a preset historical time period, and interaction data of the plurality of historical users and the plurality of historical group instance objects are obtained. In this embodiment, the acquisition manner of the history data is not limited.

Further, user characteristics of a plurality of historical users, group instance characteristics of a plurality of historical group instance objects and interaction characteristics of the plurality of historical users and the plurality of historical group instance objects are extracted from the acquired historical data. In this embodiment, the specific extraction manner may refer to the description of the corresponding part in the first embodiment, which is not described herein again. In an optional implementation manner, in order to facilitate subsequent training of the machine learning model, the interaction features extracted in this embodiment are specifically different interaction degree identifiers, for example, identifier 2 corresponds to that the user successfully consumes the clustered instance object, which indicates that the user has the highest preference degree for the clustered instance object; the identification 1 corresponds to the fact that the user unsuccessfully consumes the successfully clustered example object, or the successfully clustered example object participated by the user is unsuccessfully clustered, or the user collects the commodity corresponding to the clustered example object, which indicates that the preference degree of the user on the clustered example object is higher; the identifier 0 corresponds to the user not participating in the clique instance object and not storing the commodity corresponding to the clique instance object, which indicates that the user has the weakest preference for the clique instance object.

Still further, aiming at any combination of any historical user and any historical clique object, according to the user characteristics of the historical user, the clique example characteristics of the historical clique object and the interaction characteristics of the historical user and the historical clique object, sample data corresponding to the combination is generated. That is, in this embodiment, a combination of a history user and a history clique object corresponds to a sample data, and the sample data includes the user characteristics of the history user, the clique instance characteristics of the history clique object, and the interaction characteristics of the history user and the history clique object.

In an alternative embodiment, in order to improve the prediction accuracy of the machine learning model, a large amount of sample data may be used to train the machine learning model. When a large amount of sample data is used for model training, a large amount of combinations of historical users and historical clique instance objects need to be determined. In the embodiment, a historical user set and a historical group instance object set can be obtained; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of a historical user and a historical group instance object according to a Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object. By adopting the implementation mode, the combination of the historical users and the historical community instance objects can be quickly and accurately obtained from a large amount of data, so that the execution efficiency of the method is improved.

In an optional implementation manner, after generating sample data corresponding to each historical user and historical clique instance object combination, sample data obtained may be further subjected to sample balance processing. Specifically, in an actual implementation process, because the number of users released by different history clique instance objects is greatly different (for example, the number of users released by the history clique instance object a is 100000, and the number of users released by the history clique instance object B is 100), the sample data amount corresponding to different history clique instance objects in the generated sample data is greatly different, so that the prediction accuracy of the subsequent machine learning model for different history clique instance objects is easily greatly different, and particularly, the disadvantage of low prediction accuracy for the history clique instance object with small volume of sample data is easily caused. In order to avoid this drawback, the embodiment performs sample balance processing on the obtained sample data. The method comprises the following steps that (1) sampling is carried out on a historical clique instance object of large sample data by adopting a lower sampling frequency; and for the historical clique instance object of the small sample data, sampling is carried out by adopting higher sampling frequency, so that the sample data amount corresponding to different historical clique instance objects in the sample data after balance processing is the same or has less difference.

In this embodiment, the execution sequence of the steps S210 and S220 is not limited, and the steps may be executed in parallel or sequentially.

Step S230: and training the generated machine learning model by using the sample data to obtain the trained machine learning model.

Specifically, positive sample data and negative sample data may be generated based on the sample data; wherein, the ratio of the positive sample data to the negative sample data meets the preset ratio (for example, the ratio of the positive sample data to the negative sample data is 3: 1); and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

In this embodiment, the specific machine learning model training method is not limited. For example, an XGBoost (eXtreme Gradient Boosting) algorithm, or random forest algorithm may be used for training of the machine learning model, and so on.

Step S240: and extracting the user characteristics of the target user from the user attribute data of the target user.

Step S250: and acquiring the cluster instance data of at least one cluster instance object with the current state as the running state, and extracting the cluster instance characteristics of at least one cluster instance object from the cluster instance data.

Step S260: and inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model.

Step S270: and obtaining the matching degree of the target user and any group of example object output by the machine learning model, and obtaining the group of example object matched with the target user according to the matching degree.

The specific implementation process of steps S240 to S270 may refer to the description of the corresponding parts in the first embodiment, which is not described herein again.

Therefore, in the embodiment, the machine learning model is trained by using the user attribute data of the historical users, the group instance data of the historical group instance object and the interaction data of the plurality of historical users and the plurality of historical group instance objects, so that the machine learning model can acquire the group participation results corresponding to the combinations of different user characteristics and the group instance characteristics, the preference degrees of different users to different group instances are accurately acquired through the group participation results, and the matching degree of a target user and any group instance object can be accurately predicted; in addition, in the sample data processing process, the combination of each historical user and each historical community instance object can be quickly and accurately determined by adopting a Cartesian product operation mode, so that the execution efficiency of the method is improved; moreover, the embodiment further guarantees the consistency of the prediction precision of the machine learning model to different clique instance objects through the balance processing of the sample data.

EXAMPLE III

Fig. 3 is a flowchart illustrating a method for acquiring a blob instance object based on user matching degree according to a third embodiment of the present invention. The method for acquiring the clique instance object provided by the embodiment is directed to further optimization of the method for acquiring the clique instance object in the first embodiment and/or the second embodiment.

As shown in fig. 3, the method includes:

step S310: and extracting the user characteristics of the target user from the user attribute data of the target user.

Step S320: and acquiring the cluster instance data of at least one cluster instance object with the current state as the running state, and extracting the cluster instance characteristics of at least one cluster instance object from the cluster instance data.

In an optional implementation manner, commodity information of at least one commodity matched with a target user can be acquired according to user attribute data of the target user; and further acquiring the group instance data of at least one group instance object corresponding to the commodity information and with the current state being the running state. Therefore, the embodiment only acquires the group instance data of the group instance object of the commodity which the user is interested in, thereby reducing the data processing amount of the subsequent steps and further avoiding the waste of system resources; and the finally obtained group instance object matched with the target user is made to correspond to the actual preference degree of the target user.

Step S330: and inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model.

The machine learning model trained in advance is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period, and interaction data of the plurality of historical users and the plurality of historical group instance objects.

Step S340: and obtaining the matching degree of the target user and any group of example object output by the machine learning model.

The specific implementation process of steps S310 to S320 may refer to the description of the corresponding parts in the first embodiment, which is not described herein again.

Step S350: and calculating the clustering success rate of any cluster of example objects, and correcting the matching degree of the target user and any cluster of example objects by using the clustering success rate of any cluster of example objects so as to obtain a corrected value of the matching degree of the target user and any cluster of example objects.

In this embodiment, after the matching degree between the target user and any group of instance objects output by the machine learning model is obtained, in order to further improve the actual matching degree between the finally determined group of instance objects and the target user, the matching degree between the target user and any group of instance objects is corrected through this step.

In a specific correction process, the clustering success rate of any clustering instance object is calculated first. The method specifically comprises the following steps of calculating the clustering success rate of any clustering instance object: acquiring the clustering target number and the current clustering number of any clustering example object; the clustering success rate of any clustering example object is calculated according to the clustering target number and the current clustering population of any clustering example object, for example, the clustering success rate of the clustering example object can be calculated according to the ratio of the difference value of the clustering target number and the current clustering population to the clustering target number, and the higher the ratio is, the lower the clustering success rate is.

Further, the matching degree of the target user and any group of example object is corrected by using the clustering success rate of any group of example object, so that a corrected value of the matching degree of the target user and any group of example object is obtained. The matching degree correction value of the target user and any one group example object positively relates to the grouping success rate of the group example object. For example, the following formula can be used to calculate the matching degree correction value of the target user and any clique instance object:

wherein P' is a matching degree correction value, P is a matching degree before correction, and α is a ratio of a difference value between the number of the clustered target people and the current number of the clustered target people to the number of the clustered target people.

Step S360: and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

Therefore, after the matching degree of the target user and any group of example object output by the machine learning model is obtained, the matching degree of the target user and any group of example object is further corrected, so that the corrected matching degree can reflect the preference degree of the target user to the group of example object more truly, and the improvement of the clustering rate is facilitated; in addition, the group instance object corresponding to the target user acquired in the embodiment is the group instance object of the commodity in which the user is interested, so that the calculation amount of the method is reduced, the system resources can be effectively saved, and the finally acquired group instance object is matched with the actual preference degree of the target user.

Example four

Fig. 4 is a schematic structural diagram illustrating a blob instance object obtaining apparatus based on user matching degree according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: the system comprises a user characteristic extraction module 41, a group example data acquisition module 42, a group example characteristic extraction module 43, an input module 44, a matching degree acquisition module 45 and a group example object acquisition module 46.

A user feature extraction module 41, adapted to extract and retrieve user features of a target user from user attribute data of the target user; a cluster instance data obtaining module 42 adapted to obtain cluster instance data of at least one cluster instance object whose current state is a running state; a blob instance feature extraction module 43 adapted to extract blob instance features of the at least one blob instance object from the blob instance data; an input module 44, adapted to input the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects; a matching degree obtaining module 45, adapted to obtain a matching degree between the target user output by the machine learning model and any clique instance object; and a group instance object obtaining module 46, adapted to obtain the group instance object matched with the target user according to the matching degree.

Optionally, the apparatus further comprises: the system comprises a Cartesian operation module, a database and a database, wherein the Cartesian operation module is suitable for acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of a historical user and a historical group instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

Optionally, the apparatus further comprises: the correction module is suitable for calculating the clustering success rate of any clustering example object; correcting the matching degree of the target user and any group of example object by using the clustering success rate of any group of example object so as to obtain a corrected value of the matching degree of the target user and any group of example object;

the blob instance object acquisition module matching is further adapted to: and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

Optionally, the modification module is further adapted to: acquiring the clustering target number and the current clustering number of any clustering example object;

and calculating the clustering success rate of any clustering example object according to the clustering target number and the current clustering reference number of any clustering example object.

Optionally, the apparatus further comprises:

the sorting module is suitable for sorting the at least one group instance object according to the matching degree;

the blob instance object acquisition module is further adapted to: and acquiring a group instance object matched with the target user according to the sequencing result.

In this embodiment, the specific implementation process of each module may refer to the description in the corresponding method embodiment, which is not described herein again.

Therefore, the group instance object corresponding to the target user can be accurately acquired, and the accurate putting of the group instance object is convenient to realize, so that putting resources are saved, and user experience is improved.

EXAMPLE five

Embodiments of the present invention provide a non-volatile computer storage medium, where at least one executable instruction is stored in the computer storage medium, and the computer executable instruction may execute the method in any of the above method embodiments.

The executable instructions may be specifically configured to cause the processor to: extracting user characteristics of a target user from user attribute data of the target user; obtaining the group instance data of at least one group instance object with the current state as the running state, and extracting the group instance characteristics of the at least one group instance object from the group instance data; inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects; obtaining the matching degree of the target user and any group of example objects output by the machine learning model; and acquiring a group instance object matched with the target user according to the matching degree.

In an alternative embodiment, the user attribute data comprises at least one of the following data: basic attribute data, preference attribute data, consumption capability attribute data, and reference number attribute data.

In an alternative embodiment, the blob instance data includes at least one of the following: offer level data, crowd data, and crowd rate data.

In an alternative embodiment, the interaction data of the plurality of historical users with the plurality of historical community instance objects comprises at least one of the following data: the method comprises the following steps of obtaining group example objects, wherein the group example objects comprise participation behavior data of users to the group example objects, group result data of the users to the participated group example objects, consumption data of the users to the group example objects which are successfully clustered, and collection behavior data of the users to commodities corresponding to the non-participated group example objects.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: extracting user characteristics of a plurality of historical users, group example characteristics of a plurality of historical group example objects and interaction characteristics of a plurality of historical users and a plurality of historical group example objects from user attribute data of the plurality of historical users, group example data of the plurality of historical group example objects with running states in preset historical time periods and interaction data of the plurality of historical users and the plurality of historical group example objects; aiming at the combination of any historical user and any historical clique instance object, generating sample data corresponding to the combination according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interactive characteristics of the historical user and the historical clique instance object; and training the generated machine learning model by using the sample data to obtain the trained machine learning model.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: generating positive sample data and negative sample data based on the sample data; wherein the proportion of the positive sample data to the negative sample data meets a preset proportion; and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of any historical user and any historical community instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: calculating the clustering success rate of any clustering instance object; correcting the matching degree of the target user and any group of example object by using the clustering success rate of any group of example object so as to obtain a corrected value of the matching degree of the target user and any group of example object; and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: acquiring the clustering target number and the current clustering number of any clustering example object; and calculating the clustering success rate of any clustering example object according to the clustering target number and the current clustering reference number of any clustering example object.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: sorting the at least one group instance object according to the matching degree; and acquiring a group instance object matched with the target user according to the sequencing result.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: acquiring commodity information of at least one commodity matched with the target user according to the user attribute data of the target user; and acquiring the group instance data of at least one group instance object of which the current state is the running state and corresponds to the commodity information.

In an alternative embodiment, the executable instructions may be specifically configured to cause the processor to: and delivering the group instance object at the user terminal of the target user.

EXAMPLE six

Fig. 5 is a schematic structural diagram of a computing device according to a sixth embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.

As shown in fig. 5, the computing device may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508. Wherein: the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508. A communication interface 504 for communicating with network elements of other devices, such as clients or other servers. The processor 502 is configured to execute the program 510, and may specifically perform the relevant steps in the above method embodiments. In particular, program 510 may include program code that includes computer operating instructions. The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.

And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 510 may specifically be used to cause the processor 502 to perform the following operations: extracting user characteristics of a target user from user attribute data of the target user; obtaining the group instance data of at least one group instance object with the current state as the running state, and extracting the group instance characteristics of the at least one group instance object from the group instance data; inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects; obtaining the matching degree of the target user and any group of example objects output by the machine learning model; and acquiring a group instance object matched with the target user according to the matching degree.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: extracting user characteristics of a plurality of historical users, group example characteristics of a plurality of historical group example objects and interaction characteristics of a plurality of historical users and a plurality of historical group example objects from user attribute data of the plurality of historical users, group example data of the plurality of historical group example objects with running states in preset historical time periods and interaction data of the plurality of historical users and the plurality of historical group example objects; aiming at the combination of any historical user and any historical clique instance object, generating sample data corresponding to the combination according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interactive characteristics of the historical user and the historical clique instance object; and training the generated machine learning model by using the sample data to obtain the trained machine learning model.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: generating positive sample data and negative sample data based on the sample data; wherein the proportion of the positive sample data to the negative sample data meets a preset proportion; and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object; carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set; acquiring a combination of any historical user and any historical community instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: calculating the clustering success rate of any clustering instance object; correcting the matching degree of the target user and any group of example object by using the clustering success rate of any group of example object so as to obtain a corrected value of the matching degree of the target user and any group of example object; and acquiring the team instance object matched with the target user according to the matching degree correction value of the target user and any team instance object.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: acquiring the clustering target number and the current clustering number of any clustering example object; and calculating the clustering success rate of any clustering example object according to the clustering target number and the current clustering reference number of any clustering example object.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: sorting the at least one group instance object according to the matching degree; and acquiring a group instance object matched with the target user according to the sequencing result.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: acquiring commodity information of at least one commodity matched with the target user according to the user attribute data of the target user; and acquiring the group instance data of at least one group instance object of which the current state is the running state and corresponds to the commodity information.

In an alternative embodiment, the program 510 may be specifically configured to cause the processor 502 to perform the following operations: and delivering the group instance object at the user terminal of the target user.

The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.

Claims

1. A method for acquiring a group instance object based on user matching degree comprises the following steps:

extracting user characteristics of a target user from user attribute data of the target user;

obtaining the group instance data of at least one group instance object with the current state as the running state, and extracting the group instance characteristics of the at least one group instance object from the group instance data;

inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a pre-trained machine learning model; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects;

obtaining the matching degree of the target user and any group of example objects output by the machine learning model;

and acquiring a group instance object matched with the target user according to the matching degree.

2. The method of claim 1, wherein the user attribute data comprises at least one of:

basic attribute data, preference attribute data, consumption capability attribute data, and reference number attribute data.

3. The method of claim 1 or 2, wherein the clique instance data comprises at least one of:

offer level data, crowd data, and crowd rate data.

4. The method of any of claims 1-3, wherein the interaction data of the plurality of historical users with the plurality of historical blob instance objects includes at least one of:

the method comprises the following steps of obtaining group example objects, wherein the group example objects comprise participation behavior data of users to the group example objects, group result data of the users to the participated group example objects, consumption data of the users to the group example objects which are successfully clustered, and collection behavior data of the users to commodities corresponding to the non-participated group example objects.

5. The method according to any of claims 1-4, wherein prior to said inputting user characteristics of the target user and blob instance characteristics of any blob instance object into a pre-trained machine learning model, the method further comprises:

extracting user characteristics of a plurality of historical users, group example characteristics of a plurality of historical group example objects and interaction characteristics of a plurality of historical users and a plurality of historical group example objects from user attribute data of the plurality of historical users, group example data of the plurality of historical group example objects with running states in preset historical time periods and interaction data of the plurality of historical users and the plurality of historical group example objects;

aiming at the combination of any historical user and any historical clique instance object, generating sample data corresponding to the combination according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interactive characteristics of the historical user and the historical clique instance object;

and training the generated machine learning model by using the sample data to obtain the trained machine learning model.

6. The method of claim 5, wherein said training the constructed machine learning model with the sample data to obtain the trained machine learning model further comprises:

generating positive sample data and negative sample data based on the sample data; wherein the proportion of the positive sample data to the negative sample data meets a preset proportion;

and training the constructed machine learning model by using the positive sample data and the negative sample data to obtain the trained machine learning model.

7. The method according to claim 5 or 6, wherein before generating sample data corresponding to any combination of any historical user and any historical clique instance object according to the user characteristics of the historical user, the clique instance characteristics of the historical clique instance object and the interaction characteristics of the historical user and the historical clique instance object for the combination, the method further comprises:

acquiring a historical user set and a historical group instance object set; any collection element in the historical user collection corresponds to a historical user, and any collection element in the historical clique instance object collection corresponds to a historical clique instance object;

carrying out Cartesian product operation on the historical user set and the historical community instance object set, and obtaining a Cartesian set;

acquiring a combination of any historical user and any historical community instance object according to the Cartesian set; wherein any collection element in the Cartesian collection corresponds to a combination of a history user and a history clique instance object.

8. A clique instance object acquisition device based on user matching degree comprises:

the user characteristic extraction module is suitable for extracting the user characteristics of the target user from the user attribute data of the target user;

the cluster instance data acquisition module is suitable for acquiring cluster instance data of at least one cluster instance object with the current state as the running state;

the cluster instance feature extraction module is suitable for extracting the cluster instance features of the at least one cluster instance object from the cluster instance data;

the input module is suitable for inputting the user characteristics of the target user and the group instance characteristics of any group instance object into a machine learning model trained in advance; the pre-trained machine learning model is obtained by training based on user attribute data of a plurality of historical users, group instance data of a plurality of historical group instance objects of which the states are running states in a preset historical time period and interaction data of a plurality of historical users and a plurality of historical group instance objects;

the matching degree obtaining module is suitable for obtaining the matching degree of the target user and any group of example objects output by the machine learning model;

and the group instance object acquisition module is suitable for acquiring the group instance object matched with the target user according to the matching degree.

9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the user matching degree-based blob instance object acquisition method in any one of claims 1-7.

10. A computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the method for obtaining a clique instance object based on user matching degree as claimed in any one of claims 1 to 7.