CN112016961A - Pushing method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The application provides a pushing method, a pushing device, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: extracting user characteristics and context characteristics of unknown users and industry characteristics of games to be pushed; determining a first estimation model in a plurality of estimation models according to the game category to which the game to be pushed belongs, wherein the estimation models are estimation models based on different game categories, the game to be pushed belongs to a first game category, and the first estimation model is an estimation model of the first game category; inputting the user characteristics and the context characteristics of the unknown users and the industry characteristics of the games to be pushed into the first estimation model, and outputting target users of the unknown users; and pushing the game to be pushed to the target user.
Description
Technical Field
The present application relates to the field of advertisement technologies, and in particular, to a push method, an apparatus, an electronic device, and a computer-readable storage medium.
Background
When advertisement putting is carried out, the advertisement platform can obtain user data aiming at some shallow targets (such as clicking, downloading and the like), but aiming at some deep conversion targets (such as secondary leaving, paying and the like), a plurality of large game manufacturers are unwilling to return data to help the advertisement platform to make deep conversion estimation, mainly because the data belong to the core secrets of the game manufacturers, and under the condition, how to obtain the user data of the game to improve the advertisement putting effect is a problem which needs to be solved urgently.
Disclosure of Invention
The application provides a pushing method, a pushing device, electronic equipment and a computer-readable storage medium, wherein models of different game categories are built, and when game recommendation is carried out, a game to be recommended is input into a corresponding category model, so that an accurate user of the game can be obtained.
In a first aspect, an embodiment of the present application provides a push method, including:
extracting user characteristics and context characteristics of unknown users and industry characteristics of games to be pushed;
determining a first estimation model in a plurality of estimation models according to the game category to which the game to be pushed belongs, wherein the estimation models are estimation models based on different game categories, the game to be pushed belongs to a first game category, and the first estimation model is an estimation model of the first game category;
inputting the user characteristics and the context characteristics of the unknown users and the industry characteristics of the games to be pushed into the first estimation model, and outputting target users of the unknown users;
and pushing the game to be pushed to the target user.
In some possible implementations, the method further includes:
inputting the target distinguishing characteristics of the users of the first game category, the industry characteristics of the games belonging to the first game category and the positive and negative samples of the first game category into the first estimation model for training to obtain the parameters of the first estimation model.
In some possible implementations, the method further includes:
and performing characteristic engineering on the data of the existing users of the games of different game categories to acquire the target distinguishing characteristics of the users of different game categories.
In some possible implementation manners, the performing feature engineering on the data of the existing users of the games of different game categories to obtain the target distinguishing features of the users of different game categories includes:
constructing positive and negative samples of the first game item according to data of existing users of at least one game belonging to the first game item;
and analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories.
In some possible implementation manners, the performing feature engineering on the data of the existing users of the games of different game categories to obtain the target distinguishing features of the users of different game categories further includes:
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
In some possible implementations, the performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories includes:
acquiring historical behavior data of game users of different game categories through big data;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
For example, historical behavior data of game users of the different game categories is acquired through big data.
Optionally, the big data comprises at least one of: user behavior data on a search platform, user behavior data on other advertisement platforms, and user behavior data obtained by a data management platform.
In some possible implementations, the constructing positive and negative samples of the first game item from data of existing users of at least one game belonging to the first game item includes:
for each game of the at least one game, user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the first game category, and user data and game data that have been user activated for a period of time and have not had a depth conversion event are taken as negative examples of the first game category.
In some possible implementations, the method further includes:
and if the same user data and game data exist in both the positive sample and the negative sample of the first game item, removing the user data and the game data from the negative sample of the first game item.
In some possible implementations, the method further includes:
removing game data in the positive and negative samples of the first game item;
and inputting the positive and negative samples of the first game item with the game data removed into the first estimation model for training.
In a second aspect, an embodiment of the present application provides a push method, including: acquiring data of existing users of games of different game categories;
constructing a positive and negative sample of the same game item according to the data of the existing users of at least one game belonging to the same game item;
performing characteristic engineering on the data of the existing users of the games of different game categories to acquire target distinguishing characteristics of the users of different game categories;
inputting the industry characteristics of games belonging to the same game category, the target distinguishing characteristics of users of the games belonging to the same game category and the positive and negative samples of the same game category into the estimation model corresponding to the same game category for training to obtain the estimation model corresponding to the same game category.
In some possible implementations, the constructing positive and negative samples of the same game item according to data of existing users of at least one game belonging to the same game item includes:
for each game of the at least one game, the user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the same game category, and the user data and game data that have been user activated for a period of time and have no depth conversion event are taken as negative examples of the same game category.
In some possible implementations, the method further includes:
and if the same user data and game data exist in the positive sample of the same game item and the negative sample of the same game item, removing the user data and the game data from the negative sample of the same game item.
In some possible implementations, the method further includes:
removing game data in the positive and negative samples of the same game item class;
and inputting the positive and negative samples of the same game item without the game data into the estimation model corresponding to the same game item for training.
In some possible implementation manners, the performing feature engineering on the data of the existing users of the games of different game categories to obtain the target distinguishing features of the users of different game categories includes:
analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories;
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
In some possible implementations, the performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories includes:
acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
For example, historical behavior data of game users of the different game categories is acquired through big data.
Optionally, the big data comprises at least one of: user behavior data on a search platform, user behavior data on other advertisement platforms, and user behavior data obtained by a data management platform.
In a third aspect, an embodiment of the present application provides a pushing apparatus, configured to perform the steps of the method in the first aspect or any possible implementation manner of the first aspect. In particular, the push device comprises means for performing the method of the first aspect described above or any possible implementation manner of the first aspect.
In a fourth aspect, the present application provides another push apparatus, configured to perform the steps of the method in the second aspect or any possible implementation manner of the second aspect. In particular, the push device comprises means for performing the method of the second aspect described above or any possible implementation of the second aspect.
In a fifth aspect, an embodiment of the present application provides an electronic device, including: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when the electronic device runs, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the push method as described in any one of the first aspect.
In a sixth aspect, an embodiment of the present application provides an electronic device, including: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is running, the processor executing the machine-readable instructions to perform the steps of the push method as described in any one of the second aspects.
In a seventh aspect, this application provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the push method as described in any one of the first aspect are performed.
In an eighth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the push method as described in any one of the second aspects.
Based on the technical scheme, the method and the device can be used for establishing an estimation model of the accurate users of the game subject matters according to the data of the existing users of the same game subject matters and by combining the industry characteristics of games of different game categories, can accurately dig out potential accurate users for subsequent unknown users, and filter out noise users, so that the target of an advertiser is more accurate when the advertisement is put, the cost performance of advertisement putting is improved, the flow efficiency of an advertisement platform is improved for the advertisement platform, and the income of the advertisement platform can be finally improved.
Drawings
Fig. 1 is a schematic flowchart of a push method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another push method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another push method provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a pushing device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a pushing device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art without making any creative effort with respect to the embodiments in the present application belong to the protection scope of the present application.
Fig. 1 is a schematic flowchart of a push method 100 provided in an embodiment of the present application, and as shown in fig. 1, the method 100 includes at least some of the following:
s101, extracting user characteristics and context characteristics of an unknown user and industrial characteristics of a game to be pushed;
s102, determining a first pre-estimation model in a plurality of pre-estimation models according to the game category to which the game to be pushed belongs, wherein the plurality of pre-estimation models are pre-estimation models based on different game categories, the game to be pushed belongs to a first game category, and the first pre-estimation model is a pre-estimation model of the first game category;
s103, inputting the user characteristics and the context characteristics of the unknown users and the industry characteristics of the games to be pushed into the first estimation model, and outputting target users of the unknown users;
s104, pushing the game to be pushed to the target user.
It should be noted that the embodiment of the present application is applicable to recommendation of game-class applications, or may also be applicable to recommendation of other classes of applications, for example, clue-class applications. In specific implementation, different types of cue applications can be distinguished, for example, education, examination, financing, and the like, and an estimation model corresponding to each type of cue application is further constructed, and the implementation manner is similar. In the following, the estimation models corresponding to the game construction of different game types will be described as an example, but the present application is not limited thereto.
In the embodiment of the application, games can be divided into different subjects, or categories, such as, but not limited to, game subjects of three countries, quadratic elements, romance, female direction, war, officer fighting, commercial battle, science fiction, and the like, further, estimation models based on the game subjects are trained respectively according to different game subjects, when model estimation is performed, the subjects to which the games to be pushed belong can be judged, estimation is further performed on unknown users based on the estimation models of the subjects to determine target users of the games to be pushed, and then the games to be pushed can be pushed to the target users.
To better understand the embodiment of the present application, a training process based on an estimation model of game material will be described first.
1. Constructing positive and negative samples of different game themes
In embodiments of the present application, positive and negative samples of different game material may be constructed using existing users of the game of the different game material, which may include high quality users of the game, such as users with depth conversion events. The existing high-quality users of the existing games are used as sample construction models, so that the high-quality users aiming at the game themes can be screened out, and the advertisement putting effect can be improved.
In the embodiment of the present application, the positive and negative samples of the game material can be constructed according to the data of the existing users of all games belonging to the same game material, that is, the games of the same game material have common positive and negative samples.
Specifically, for each game of all games belonging to the same game subject, positive and negative samples of the game may be counted. For example, a user may activate the game for a period of time, e.g., 6 hours, or 12 hours, and have data of depth conversion events (e.g., user Identification (ID) and game ID) as positive samples. Data for a period of time, e.g., 6 hours, or 12 hours, in which the user activates the category but the event is not deeply converted, such as user Identification (ID) and game ID, is taken as a negative example. The positive samples of all games belonging to the same game material are taken as the positive samples of the game material, and the negative samples of all games are taken as the negative samples of the game material.
The deep conversion event may be, for example, a leave, a pay, etc.
It should be understood that the above positive and negative samples are only examples, and may be adjusted according to actual needs, and the application is not limited thereto.
In this embodiment, the positive and negative samples may include a user ID, a game ID, and a tag identifying a type of the sample, and when training data is input to the pre-estimation model corresponding to the game material, the game ID in the positive and negative samples may be removed, that is, the binding relationship between the user and the single game is released, so that the user and the game material are bound.
Optionally, in some embodiments, the user data and game data are removed from the negative examples of the game material if the same user data and game data are present in both the positive examples and the negative examples of the game material.
In a specific implementation, if the positive and negative samples of the same game material are not balanced, the problem can be solved by adopting an over-sampling or under-sampling mode. For example, if the number of users of the story is generally large, the number of positive samples is large, and the number of positive samples can be reduced by undersampling. For another example, if the number of users of the small theme game is small, the number of positive samples is small, and the number of positive samples can be increased by oversampling.
2. Building a pool of features
After positive and negative samples of different game subjects are constructed, user characteristics corresponding to the positive and negative samples of the different game subjects, such as but not limited to any one or more of age, gender and occupation of the user, can be further obtained, meanwhile, industry characteristics of the games of the different game subjects, such as industry description information and the like, such as industry, sub-industry and category information, can be further obtained, and the characteristics are further added into the characteristic pool.
It is understood that age, gender and occupation are only one preferred way to define the user characteristics in this application, and in practical applications, the user characteristics may further include other defining factors, such as income level, education background, etc., according to the specific type of the game to be pushed, and will not be described herein again.
And further, the user characteristics and the industry characteristics of the obtained positive and negative samples of different game themes can be subjected to characteristic engineering to determine the distinguishing characteristics of the different game themes.
Specifically, as shown in fig. 2, in S110, the contribution degrees of the existing features of the different game categories may be analyzed first, and the feature with the larger contribution degree is determined as the basic feature, and the analysis indexes are, for example, but not limited to, the null rate and the Information Value (IV).
For example, the features with the existing feature hollow value rate larger than a certain threshold value are removed, and the feature with the IV value larger than a certain threshold value is used as one of the basic features.
Then, in S120, the character image of the user of each game material is analyzed based on the basic features of the user in the positive and negative samples of the different game materials, and a feature that is distinguished significantly for the different game materials is selected as a distinguishing feature among the basic features.
Further, in S130, the distinctive feature is associated to expand a feature range, and a target distinctive feature is obtained. For example, if the distinguishing feature includes feature a, and the user having feature a also has feature B, but feature B is not in the distinguishing feature, then feature B may be added to the distinguishing feature.
For example, for the users of the games of the romantic themes, who often have a common interest in watching hong kong movies, a similar feature of "having viewed hong kong movies in the past for a certain period of time" may be added to the distinguishing feature corresponding to the games of the romantic themes.
In some implementation manners, behavior data of users of different game subjects can be obtained through big data, the obtained user behavior data is added to the feature pool, feature association is performed based on existing features in the feature pool and the user behavior data obtained through the big data, and generalization capability of the model can be improved.
Optionally, the big data includes: the user behavior data on the search platform, the user behavior data on other advertisement platforms and the user behavior data obtained by the data management platform.
For example, user data of searched game names, game plays, game team names may be input to the feature pool.
As another example, user behavior data may be obtained from other game platform vendors. The user behavior data may be obtained, for example, through a Software Development Kit (SDK), and the obtained user behavior data is further added to the feature pool.
For another example, the user behavior Data may be acquired from a Data Management Platform (DMP), and the acquired user behavior Data may be further added to the feature pool.
It is understood that the user behavior data may include any behavior of the user on the internet, such as but not limited to the user viewing a game advertisement, clicking on a game advertisement, installing and activating a game, having paid for a game, or other like approval or comment behavior.
Based on the user behavior data, the preference of the user to game subject matters can be determined, and further, the game can be recommended in a targeted mode when the game is recommended, so that the advertisement putting effect can be improved.
Optionally, after the distinctive feature for distinguishing different game subjects is constructed, before training, the feature may be subjected to data preprocessing, such as data normalization, uniform coding format, and the like, where the uniform coding format is to make the code value of the newly added feature correspond to the code value of the existing feature.
And training according to a preset algorithm by taking the constructed characteristics of the same game subject (including the user characteristics and the industry characteristics of the game subject) and the positive and negative samples of the same game subject as training data to obtain an estimation model of the game subject. Alternatively, the pre-estimation model may be a ranking model, such as a Factorization Machines (FM) model, a Field-aware Factorization Machines (FFM) model, an LR model, or the like.
When estimating according to the trained estimation model, the industry characteristics of the game to be pushed can be extracted through the characteristic pool, the user characteristics (specifically, the distinguishing characteristics constructed in the previous embodiment) and the context characteristics of the unknown user can be further extracted, the user characteristics and the context characteristics are input into the estimation model of the subject matter to which the game to be recommended belongs, the target user of the game to be recommended in the unknown user is estimated through the estimation model, and the game to be pushed is released to the target user.
Optionally, the context information is context information of an advertisement, for example, screen opening of the first screen, refreshing of the advertisement upwards, refreshing of the advertisement downwards, and the like.
In summary, according to the data of the existing users of the same game subject, the estimation model of the accurate users of the game subject can be established, potential accurate users can be accurately mined for subsequent unknown users, noise users are filtered out, the target of an advertiser is more accurate when the advertisement is put, the cost performance of advertisement putting is improved, the flow efficiency of an advertisement platform is improved for the advertisement platform, and the income of the advertisement platform can be finally improved.
Fig. 3 is a schematic flow chart of a push method according to another embodiment of the present application, and as shown in fig. 3, the method 200 includes at least part of the following:
s201, acquiring data of existing users of games of different game categories;
s202, constructing positive and negative samples of the same game item according to the data of the existing users of at least one game belonging to the same game item;
s203, performing characteristic engineering on the data of the existing users of the games of different game categories to acquire target distinguishing characteristics of the users of different game categories;
s204, inputting the industry characteristics of the games belonging to the same game category, the target distinguishing characteristics of the users of the games belonging to the same game category and the positive and negative samples of the same game category into the estimation model corresponding to the same game category for training to obtain the estimation model corresponding to the same game category.
It should be understood that the specific implementation of the method 200 can refer to the related implementation of the method 100, and the detailed description is omitted here for brevity.
Optionally, in some embodiments, the constructing positive and negative samples of the same game item according to data of existing users of at least one game belonging to the same game item includes:
for each game of the at least one game, the user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the same game category, and the user data and game data that have been user activated for a period of time and have no depth conversion event are taken as negative examples of the same game category.
Optionally, in some embodiments, the method 200 further comprises:
and if the same user data and game data exist in the positive sample of the same game item and the negative sample of the same game item, removing the user data and the game data from the negative sample of the same game item.
Optionally, in some embodiments, the method 200 further comprises:
removing game data in the positive and negative samples of the same game item class;
and inputting the positive and negative samples of the same game item without the game data into the estimation model corresponding to the same game item for training.
Optionally, in some embodiments, the performing feature engineering on the data of the existing users of the games of different game categories to obtain the target distinguishing features of the users of different game categories includes:
analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories;
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
Optionally, in some embodiments, the performing feature association on the distinguishing features to obtain target distinguishing features for distinguishing users of different game categories includes:
acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
Optionally, in some embodiments, the big data comprises at least one of: user behavior data on a search platform, user behavior data on other advertisement platforms, and user behavior data obtained by a data management platform.
Therefore, the pushing method of the embodiment of the application can establish an estimation model of the accurate users of the game subject matters according to the data of the existing users of the same game subject matters and by combining the industry characteristics of the games of different game categories, can accurately dig out potential accurate users for subsequent unknown users, and filters out some noise users, so that the target of an advertiser is more accurate when the advertiser puts the advertisement, the cost performance of advertisement putting is improved, the flow efficiency of an advertisement platform is improved for the advertisement platform, and the income of the advertisement platform can be finally improved.
While method embodiments of the present application are described in detail above with reference to fig. 1-3, apparatus embodiments of the present application are described in detail below with reference to fig. 4-6, it being understood that apparatus embodiments correspond to method embodiments and that similar descriptions may be had with reference to method embodiments.
Fig. 4 shows a schematic block diagram of a push device 300 according to an embodiment of the application. As shown in fig. 4, the apparatus 300 includes:
the extracting unit 301 is configured to extract user characteristics and context characteristics of an unknown user, and industry characteristics of a game to be pushed;
a determining unit 302, configured to determine, according to a category of game items to which the game to be pushed belongs, a first pre-estimation model in multiple pre-estimation models, where the multiple pre-estimation models are pre-estimation models based on different categories of game items, the game to be pushed belongs to a first category of game items, and the first pre-estimation model is a pre-estimation model of the first category of game items;
the estimation unit 303 is configured to input the user characteristics and the context characteristics of the unknown user and the industry characteristics of the game to be pushed to the first estimation model, and output a target user of the unknown user;
a pushing unit 304, configured to push the game to be pushed to the target user.
Optionally, in some embodiments, the extracting unit 301 is further configured to: and performing characteristic engineering on the data of the existing users of the games of different game categories to acquire the target distinguishing characteristics of the users of different game categories.
Optionally, in some embodiments, the extracting unit 301 is further configured to:
constructing positive and negative samples of the first game item according to data of existing users of at least one game belonging to the first game item;
and analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories.
Optionally, in some embodiments, the extracting unit 301 is further configured to: analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
Optionally, in some embodiments, the extracting unit 301 is further configured to: acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
Optionally, in some embodiments, the big data comprises at least one of: user behavior data on a search platform, user behavior data on other advertisement platforms, and user behavior data obtained by a data management platform.
Optionally, in some embodiments, the apparatus 300 further comprises:
and the training unit is used for inputting the industry characteristics of the games belonging to a first game category, the target distinguishing characteristics of the users of the first game category and the positive and negative samples of the first game category into the first estimation model for training to obtain the parameters of the first estimation model.
Optionally, in some embodiments, the training unit is further configured to: for each game of the at least one game, user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the first game category, and user data and game data that have been user activated for a period of time and have not had a depth conversion event are taken as negative examples of the first game category.
Optionally, in some embodiments, the training unit is further configured to:
and if the same user data and game data exist in both the positive sample and the negative sample of the first game item, removing the user data and the game data from the negative sample of the first game item.
Optionally, in some embodiments, the training unit is further configured to: removing game data in the positive and negative samples of the first game item;
and inputting the positive and negative samples of the first game item with the game data removed into the first estimation model for training.
Therefore, the pushing device of the embodiment of the application can establish an estimation model of the accurate users of the game subject matters according to the data of the existing users of the same game subject matters and by combining the industry characteristics of games of different game categories, can accurately dig out potential accurate users for subsequent unknown users, and filters out noise users, so that the target of an advertiser is more accurate when the advertisement is put, the cost performance of advertisement putting is improved, the flow efficiency of an advertisement platform is improved for the advertisement platform, and the income of the advertisement platform can be finally improved.
Fig. 5 shows a schematic block diagram of a push device 400 according to an embodiment of the application. As shown in fig. 5, the apparatus 400 includes:
an obtaining unit 401, configured to obtain data of existing users of games of different game categories;
a constructing unit 402, configured to construct a positive and negative sample of the same game item according to data of an existing user of at least one game belonging to the same game item;
a processing unit 403, configured to perform feature engineering on data of existing users of games of different game categories, and obtain target distinguishing features of users of different game categories;
the training unit 404 is configured to input the industry characteristics of the games belonging to the same category of games, the target distinguishing characteristics of the users of the games belonging to the same category of games and the positive and negative samples of the same category of games into the estimation model corresponding to the same category of games for training, so as to obtain the estimation model corresponding to the same category of games.
Optionally, in some embodiments, the constructing unit 402 is specifically configured to:
for each game of the at least one game, the user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the same game category, and the user data and game data that have been user activated for a period of time and have no depth conversion event are taken as negative examples of the same game category.
Optionally, in some embodiments, the building unit 402 is further configured to: and if the same user data and game data exist in the positive sample of the same game item and the negative sample of the same game item, removing the user data and the game data from the negative sample of the same game item.
Optionally, in some embodiments, the constructing unit 402 is specifically configured to: removing game data in the positive and negative samples of the same game item class;
the training unit 404 is further configured to: and inputting the positive and negative samples of the same game item without the game data into the estimation model corresponding to the same game item for training.
Optionally, in some embodiments, the processing unit 403 is further configured to:
analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories;
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
Optionally, in some embodiments, the processing unit 403 is further configured to:
acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
Optionally, in some embodiments, the big data comprises at least one of: user behavior data on a search platform, user behavior data on other advertisement platforms, and user behavior data obtained by a data management platform.
Therefore, the pushing device of the embodiment of the application can establish an estimation model of the accurate users of the game subject matters according to the data of the existing users of the same game subject matters, can accurately dig out potential accurate users for subsequent unknown users, and filters out some noise users, so that the target of an advertiser is more accurate when the advertisement is put, the cost performance of advertisement putting is improved, the flow efficiency of an advertisement platform is improved for the advertisement platform, and the income of the advertisement platform can be finally improved.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application, including: a processor 501, a memory 502 and a bus 503, wherein the memory 502 stores machine-readable instructions executable by the processor 501, the processor 501 and the memory 502 communicate with each other through the bus 1103, and the processor 501 executes the machine-readable instructions to perform the steps in the method embodiments shown in fig. 1 to 3.
The embodiment of the present application further provides a chip, where the chip includes an input/output interface, at least one processor, at least one memory, and a bus, where the at least one memory is used to store instructions, and the at least one processor is used to call the instructions in the at least one memory to execute the steps in the method embodiments shown in fig. 1 to 3.
The present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the method embodiments shown in fig. 1 to 3.
It should be understood that the specific examples in the embodiments of the present application are for the purpose of promoting a better understanding of the embodiments of the present application and are not intended to limit the scope of the embodiments of the present application.
It is to be understood that the terminology used in the embodiments of the present application and the appended claims is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. For example, as used in the examples of this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the processor or processing unit of the embodiments of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
It will be appreciated that the memory of embodiments of the present application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous link SDRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
Embodiments of the present application also provide a computer program, which includes instructions that, when executed by a computer, enable the computer to execute the contents of the method embodiments.
It should be noted that, without conflict, the embodiments and/or technical features in the embodiments described in the present application may be arbitrarily combined with each other, and the technical solutions obtained after the combination also fall within the protection scope of the present application.
It should be understood that the specific examples in the embodiments of the present application are for the purpose of promoting a better understanding of the embodiments of the present application, and are not intended to limit the scope of the embodiments of the present application, and that various modifications and variations can be made by those skilled in the art based on the above embodiments and fall within the scope of the present application.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (21)
1. A push method, comprising:
extracting user characteristics and context characteristics of unknown users and industry characteristics of games to be pushed;
determining a first estimation model in a plurality of estimation models according to the game category to which the game to be pushed belongs, wherein the estimation models are estimation models based on different game categories, the game to be pushed belongs to a first game category, and the first estimation model is an estimation model of the first game category;
inputting the user characteristics and the context characteristics of the unknown users and the industry characteristics of the games to be pushed into the first estimation model, and outputting target users of the unknown users;
and pushing the game to be pushed to the target user.
2. The method of claim 1, further comprising:
inputting the target distinguishing characteristics of the users of the first game category, the industry characteristics of the games belonging to the first game category and the positive and negative samples of the first game category into the first estimation model for training to obtain the parameters of the first estimation model.
3. The method of claim 2, further comprising:
and performing characteristic engineering on the data of the existing users of the games of different game categories to acquire the target distinguishing characteristics of the users of different game categories.
4. The method of claim 3, wherein said performing feature engineering on data of existing users of games of different game categories to obtain target distinguishing features of users of different game categories comprises:
constructing positive and negative samples of the first game item according to data of existing users of at least one game belonging to the first game item;
analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories;
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
5. The method of claim 4, wherein said feature associating said distinctive features to obtain a target distinctive feature for distinguishing users of different categories of games comprises:
acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
6. The method according to any one of claims 2-5, wherein the extracting the user features of the unknown user comprises:
and extracting the target distinguishing features of the unknown user.
7. The method of claim 4, wherein said constructing positive and negative examples of said first category of games from data of existing users of at least one game belonging to said first category of games comprises:
for each game of the at least one game, user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the first game category, and user data and game data that have been user activated for a period of time and have not had a depth conversion event are taken as negative examples of the first game category.
8. The method of claim 7, further comprising:
and if the same user data and game data exist in both the positive sample and the negative sample of the first game item, removing the user data and the game data from the negative sample of the first game item.
9. The method according to claim 7 or 8, characterized in that the method further comprises:
removing game data in the positive and negative samples of the first game item;
and inputting the positive and negative samples of the first game item with the game data removed into the first estimation model for training.
10. A push method, comprising:
acquiring data of existing users of games of different game categories;
constructing a positive and negative sample of the same game item according to the data of the existing users of at least one game belonging to the same game item;
performing characteristic engineering on the data of the existing users of the games of different game categories to acquire target distinguishing characteristics of the users of different game categories;
inputting the industry characteristics of games belonging to the same game category, the target distinguishing characteristics of users of the games belonging to the same game category and the positive and negative samples of the same game category into the estimation model corresponding to the same game category for training to obtain the estimation model corresponding to the same game category.
11. The method of claim 10, wherein said constructing positive and negative examples of the same game item based on data from existing users of at least one game belonging to the same game item comprises:
for each game of the at least one game, the user data and game data that have been user activated for a period of time and have a depth conversion event are taken as positive examples of the same game category, and the user data and game data that have been user activated for a period of time and have no depth conversion event are taken as negative examples of the same game category.
12. The method of claim 11, further comprising:
and if the same user data and game data exist in the positive sample of the same game item and the negative sample of the same game item, removing the user data and the game data from the negative sample of the same game item.
13. The method according to any one of claims 10-12, further comprising:
removing game data in the positive and negative samples of the same game item class;
and inputting the positive and negative samples of the same game item without the game data into the estimation model corresponding to the same game item for training.
14. The method of any one of claims 10 to 12, wherein said performing feature engineering on data of existing users of games of different game categories to obtain target distinguishing features of users of different game categories comprises:
analyzing the contribution degrees of the user characteristics corresponding to the positive and negative samples of different game categories, and determining the basic characteristics of the users of different game categories;
analyzing the crowd portraits of the users of different game categories according to the basic characteristics of the positive and negative samples of the different game categories, and determining the distinguishing characteristics of the users of the different game categories;
and performing feature association on the distinguishing features to obtain target distinguishing features of users distinguishing different game categories.
15. The method of claim 14, wherein said feature associating said distinctive features to obtain a target distinctive feature for distinguishing users of different categories of games comprises:
acquiring historical behavior data of game users of different game categories;
and performing feature association on the distinguishing features according to the historical behavior data of the game users of different game categories to obtain the target distinguishing features.
16. A pushing device, comprising:
the extracting unit is used for extracting the user characteristics and the context characteristics of the unknown user and the industry characteristics of the game to be pushed;
the determining unit is used for determining a first pre-estimation model in a plurality of pre-estimation models according to the game category to which the game to be pushed belongs, wherein the plurality of pre-estimation models are pre-estimation models based on different game categories, the game to be pushed belongs to a first game category, and the first pre-estimation model is a pre-estimation model of the first game category;
the estimation unit is used for inputting the user characteristics and the context characteristics of the unknown user and the industry characteristics of the game to be pushed into the first estimation model and outputting a target user in the unknown user;
and the pushing unit is used for pushing the game to be pushed to the target user.
17. A pushing device, comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring data of existing users of games of different game categories;
the building unit is used for building a positive sample and a negative sample of the same game item according to the data of the existing users of at least one game belonging to the same game item;
the processing unit is used for performing characteristic engineering on the data of the existing users of the games of different game categories to acquire target distinguishing characteristics of the users of different game categories;
and the training unit is used for inputting the industrial characteristics of the games belonging to the same game category, the target distinguishing characteristics of the users of the games belonging to the same game category and the positive and negative samples of the same game category into the estimation model corresponding to the same game category for training to obtain the estimation model corresponding to the same game category.
18. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the push method according to any one of claims 1 to 9.
19. A computer-readable storage medium, characterized in that the computer-readable storage medium is used for storing a computer program which, when executed by a processor, performs the push method according to any one of claims 1 to 9.
20. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the push method according to any one of claims 10 to 15.
21. A computer-readable storage medium, characterized in that the computer-readable storage medium is used for storing a computer program which, when executed by a processor, performs the push method according to any one of claims 10 to 15.
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