CN112187945A

CN112187945A - Information pushing method and device and electronic equipment

Info

Publication number: CN112187945A
Application number: CN202011068819.3A
Authority: CN
Inventors: 龚泽坤
Original assignee: Beijing Youzhuju Network Technology Co Ltd
Current assignee: Beijing Youzhuju Network Technology Co Ltd
Priority date: 2020-09-30
Filing date: 2020-09-30
Publication date: 2021-01-05

Abstract

The embodiment of the disclosure discloses an information pushing method and device and electronic equipment. One embodiment of the method comprises: importing first adjustment mode information and first push related information of first candidate information into a target model to obtain a first score corresponding to each piece of first adjustment mode information, wherein the number of the first adjustment mode information is at least two; selecting target first adjustment mode information from at least two pieces of first adjustment mode information according to each first score; adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value; and pushing the first candidate information based on the first adjusted characteristic value. Therefore, a new information pushing mode is provided.

Description

Information pushing method and device and electronic equipment

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to an information pushing method and apparatus, and an electronic device.

Background

With the development of the internet, users increasingly use terminal devices to browse various information. For example, a user may browse through a terminal device an information stream that is continuously updated over time. The user may be interested in one or more of the information in the information stream.

Generally, the information flow browsed by the user can be pushed to a terminal used by the user by a server. The accuracy of the server pushing information may affect the amount of effective information obtained by the user within a certain time, and further affect the time length for the user to browse the information stream.

Disclosure of Invention

This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

In a first aspect, an embodiment of the present disclosure provides an information pushing method, where the method includes: importing first adjustment mode information and first push related information of first candidate information into a target model to obtain a first score corresponding to each piece of first adjustment mode information, wherein the number of the first adjustment mode information is at least two; selecting target first adjustment mode information from at least two pieces of first adjustment mode information according to each first score; adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value; and pushing the first candidate information based on the first adjusted characteristic value.

In a second aspect, an embodiment of the present disclosure provides an information pushing apparatus, including: the system comprises an importing unit, a target model and a plurality of pieces of information of the first adjustment modes, wherein the importing unit is used for importing the first adjustment mode information and first push related information of first candidate information into the target model to obtain first scores corresponding to the first adjustment mode information, and the number of the first adjustment mode information is at least two; the selecting unit is used for selecting target first adjustment mode information from the at least two pieces of first adjustment mode information according to each first score; the generating unit is used for adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value; and the pushing unit is used for pushing the first candidate information based on the first adjusted characteristic value.

In a third aspect, the disclosed embodiments provide a model training method, including: importing second adjustment mode information and second pushing related information of second candidate information into a model to be trained to obtain a second score corresponding to each piece of second adjustment mode information, wherein the number of the second adjustment mode information is at least two; selecting target second adjustment mode information from at least two pieces of second adjustment mode information according to each second score; adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value; and adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

In a fourth aspect, an embodiment of the present disclosure provides a model training apparatus, including: the importing module is used for importing the second adjustment mode information and second pushing related information of the second candidate information into the model to be trained to obtain a second score corresponding to each piece of second adjustment mode information, wherein the number of the second adjustment mode information is at least two; the selecting module is used for selecting target second adjustment mode information from the at least two pieces of second adjustment mode information according to each second score; the generating module is used for adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value; and the adjusting module is used for adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the information push method according to the first aspect.

In a sixth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the steps of the information pushing method according to the first aspect.

According to the information pushing method, the information pushing device and the electronic equipment, the first pushing related information and the first adjustment mode information are used as input and led into the target model, and then the target model directly outputs the first score of the first adjustment mode information, so that the first adjustment mode information matched with the first pushing related information can be determined, the first token value to be adjusted can be adjusted according to the first pushing related information, the accuracy of the generated first adjusted token value is improved, and the pushing accuracy is improved.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.

FIG. 1 is a flow diagram of one embodiment of an information push method according to the present disclosure;

FIG. 2 is a flow chart of model training steps according to the present disclosure;

FIG. 3 is a flow diagram of one embodiment of a model training method according to the present disclosure;

FIG. 4 is a schematic block diagram of one embodiment of an information pushing device according to the present disclosure;

FIG. 5 is a schematic block diagram of one embodiment of a model training apparatus according to the present disclosure;

FIG. 6 is an exemplary system architecture to which the information push method of one embodiment of the present disclosure may be applied;

fig. 7 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

Referring to fig. 1, a flow of one embodiment of an information push method according to the present disclosure is shown. The information pushing method shown in fig. 1 includes the following steps:

step 101, importing the first adjustment mode information and the first push related information of the first candidate information into a target model to obtain a first score corresponding to the first adjustment mode information.

In this embodiment, the executing entity of the information pushing method may import the first adjustment manner information and the first pushing related information of the first candidate information into the target model, and obtain a first score corresponding to the first adjustment manner information.

In this embodiment, the first adjustment manner information may be used to indicate a manner of adjusting the first token value to be adjusted of the first candidate information. Here, the number of the first adjustment manner information may be at least two.

In this embodiment, the first candidate information may be information for push. The type of the first candidate information is not limited herein.

In this embodiment, the first push related information may comprise information related to pushing said first candidate information. The information items specifically included in the first push related information may be set according to an actual application scenario, and are not limited herein.

In this embodiment, the target model may be used to characterize a corresponding relationship between the first push related information and the first adjustment mode information, and the first score. It is to be understood that for each adjustment, the target model generates a first score for the first adjustment information.

In this embodiment, the target model may be a model constructed based on a neural network. The specific structure of the target model may be set according to an actual application scenario, and is not limited herein.

And 102, selecting target first adjustment mode information from at least two pieces of first adjustment mode information according to each first score.

In this embodiment, the execution main body may select the target first adjustment manner information from the at least two pieces of first adjustment manner information according to each first score.

In this embodiment, the executing entity may select a first score from at least two first scores generated by the target model as the target first score. Then, the first adjustment mode information corresponding to the target first score is used as the target first adjustment mode information. In other words, the first adjustment method information may be selected from the at least two first adjustment method information as the target first adjustment method information according to the first component.

Step 103, adjusting a first to-be-adjusted characteristic value of the first candidate information according to the target first adjustment mode information, and generating a first adjusted characteristic value.

In this embodiment, the executing entity may adjust the first token value to be adjusted of the first candidate information according to the target first adjustment manner information, and generate a first adjusted token value.

As an example, the first token to be adjusted may be 10. The first adjustment mode information may indicate that the value is increased by 2, and the sum of the first token value to be adjusted 10 and 2 may be used as the first adjusted token value, i.e. 12.

In this embodiment, the characterization value of the first candidate information may be used as a criterion for pushing the first candidate information. For example, when information is pushed, first candidate information related to a user feature or a user search word may be recalled according to content relevance, and then whether to present the recalled first candidate information or a ranking of the presented first candidate information may be determined with reference to a characteristic value of the first candidate information.

And 104, pushing the first candidate information based on the first adjusted characteristic value.

In this embodiment, the executing entity may push the first candidate information based on the first adjusted token value.

In this embodiment, pushing the first candidate information based on the first adjusted token value evaluation information may be implemented in various ways, which is not limited herein.

In some embodiments, at least one of, but not limited to: and determining whether to display the first candidate information or determining the order of the first candidate information to be displayed in the information sequence to be pushed based on the first adjusted characteristic value.

As an example, after recalling several candidate information according to the search term or the user attribute, a predetermined number of candidate information may be selected in the order from large to small of the first token value, and the selected candidate information may be candidate information to be presented. Then, the candidate information to be displayed may be ranked according to a first adjusted characteristic value.

It should be noted that, in the method provided in this embodiment, the first pushing related information and the first adjustment mode information are taken as inputs and introduced into the target model, and then the target model directly outputs the first score of the first adjustment mode information, so that the first adjustment mode information matched with the first pushing related information can be determined, the first token value to be adjusted can be adjusted for the first pushing related information, and therefore, the accuracy of the generated first adjusted token value is improved, and further, the pushing accuracy is improved.

In some embodiments, the step 102 may include: and determining the first adjustment mode information corresponding to the highest first score as target first adjustment mode information.

It should be noted that, by determining the first adjustment mode information corresponding to the highest first score as the target first adjustment mode information, the accuracy of adjustment can be improved, and then the accuracy of the generated first adjusted token value is improved, thereby improving the accuracy of information pushing.

In some embodiments, the first push related information belongs to push related information. I.e. the first push related information may inherit the relevant situation in the push related information.

In some embodiments, the specific item in the push related information may be set according to an actual application scenario, and is not limited herein.

In some embodiments, the push related information may include, but is not limited to, at least one of: pushing the basis information and pushing the historical information.

It should be noted that, by using the push history information as the push related information, the response to the adjustment mode information can be determined by using the push history information as a determination basis. Therefore, the adjustment amplitude of the characteristic value to be adjusted can be determined by referring to the pushing history information of the candidate information, and the accuracy of adjusting the characteristic value to be adjusted is improved.

In some embodiments, the push compliance information may include, but is not limited to, at least one of: attribute information of the candidate information, attribute information of the candidate information owner.

In some embodiments, the attribute information of the candidate information may include, but is not limited to, at least one of the following: the representation value of the candidate information, the form type of the candidate information (such as pictures, texts or videos) and the field type of the candidate information (such as education, sports or literature and the like).

In some embodiments, the attribute information of the candidate information owner may include, but is not limited to, at least one of: name, type, etc. of the candidate information owner. The candidate information owner may include an owner of the candidate information.

In some embodiments, the push history information may include, but is not limited to, at least one of: pushing process history information, history operation information performed on the pushed candidate information.

It should be noted that the push related information includes push history data determined in real time, and the token value to be adjusted can be adjusted according to the real-time push environment, so that the real-time property of the adjusted token value is improved, that is, the token value can be adjusted in accordance with the latest push environment, and the accuracy of adjustment is improved.

In some embodiments, the push process history information may include, but is not limited to, at least one of: the push start time, the duration from the push start time to the current time, the number of pushes, the total cost since the push of the candidate information.

In some embodiments, the historical operation information on the pushed candidate information may include, but is not limited to, at least one of: the user image of the user who operates the candidate information, the first operation frequency of the first operation on the candidate information, and the second operation frequency of the second operation on the candidate information.

Here, the operation content of the first operation may be set according to an actual scene, and is not limited herein; as an example, the first operation may be an operation of triggering the candidate information so as to browse the candidate information.

Here, the operation content of the second operation may be set according to an actual application scenario, and is not limited herein; as an example, the second operation may be an operation of triggering a preset control in the candidate information.

In some application scenarios, the first push related information may belong to push related information. The second push related information may also belong to push related information.

In some embodiments, the first push related information comprises at least one of: the first pushing basis information and the first pushing history information; wherein the first push compliance information comprises at least one of: attribute information of the first candidate information, attribute information of the first candidate information owner; the first push history information includes at least one of: first push process history information, first history operation information on the pushed candidate information.

In some embodiments, the second push related information comprises at least one of: the second pushing basis information and the second pushing history information; wherein the second push-by-information comprises at least one of: attribute information of the second candidate information, attribute information of the second candidate information owner; the second push history information includes at least one of: second pushing process history information, and second pair of pushed candidate information.

In some embodiments, the at least two pieces of adjustment mode information may be set according to actual situations, and are not limited herein.

As an example, the adjustment range is equal to or greater than-10 and equal to or less than 10, and the adjustment amplitude. The minimum adjustment unit is 0.1. Therefore, 200 adjustment amplitudes can be set, and when the adjustment amplitudes are positive numbers, the representation values to be adjusted are improved; when the adjustment amplitude is negative, the representation value to be adjusted is reduced.

In some embodiments, the target model may be obtained through a model training step. Here, the model training step may be implemented by the flow shown in fig. 2. The process shown in fig. 2 may include:

step 201, importing the second adjustment mode information and the second push related information of the second candidate information into the model to be trained.

The second adjustment method information in the model training step is adjustment method information in a different stage from the first adjustment method information in the information push method corresponding to fig. 1. The contents of the first adjustment mode information and the second adjustment mode information may be correspondingly the same; for example, the first adjustment method information may be two pieces of information, the second adjustment method information may be two pieces of information, the first adjustment method information may be respectively 1 plus and 1 minus, and the second adjustment method information may be respectively 1 plus and 1 minus.

It should be noted that the second candidate information in the model training step is candidate information in a different stage from the first candidate information in the information push method corresponding to fig. 1. The information content of the first candidate information and the second candidate information may be the same. In some application scenarios, the candidate information a may serve as the second candidate information in the model training stage, or may serve as the first candidate information after the model training is completed.

Here, the second push related information may be used to indicate information related to the push of the second candidate information.

Here, the model to be trained may be an untrained initial model, or may be a model that has been trained but is not trained.

Here, the model to be trained may be used to characterize a correspondence between the first push related information, the first adjustment mode information, and the first score. It is understood that for each adjustment, the model to be trained generates a first score for the first adjustment information.

Here, the model to be trained may be a model constructed based on a neural network. The specific structure of the model to be trained may be set according to the actual application scenario, and is not limited herein.

Step 202, selecting target second adjustment mode information from at least two second adjustment mode information according to each second score,

here, the manner of selecting the target second adjustment manner information according to the second score may be the same as or different from the manner of selecting the target adjustment manner information according to the first score.

Step 203, utilizing the target second adjustment mode information to adjust a second to-be-adjusted characteristic value of the second candidate information, and generating a second adjusted characteristic value.

Here, the second of the second token values to be adjusted is to be consistent with the second expression in the second candidate information, and it can be understood that the second candidate information does not have the first token value to be adjusted.

And 204, adjusting parameters of the model to be trained based on the push effect information.

Here, the push effect information is used to characterize a push effect of pushing the second candidate information based on the second adjusted characterizing value.

In this embodiment, the specific representation manner of the push effect information may be set according to an actual application scenario, and is not limited herein.

In some application scenarios, the push effect information may be represented by a ratio of the first operation times to the display times. As an example, the first operation may be an operation of clicking the second candidate information by the user, and may also be an operation of clicking a preset control in the second candidate information by the user.

In this embodiment, the parameters in the model to be trained may also include, but are not limited to, at least one of the following: weights in the model to be trained, bias terms in the model to be trained.

In this embodiment, based on the push effect information, the parameter adjustment of the model to be trained can be implemented in various ways, which is not limited herein.

By way of example, the model to be trained may be adjusted in parameters by using back propagation, gradient descent, and the like.

In some embodiments, the stopping condition of the model training may be set according to an actual application scenario, and is not limited herein. As an example, the stopping condition of the model training may include, but is not limited to, at least one of the following: the updating amplitude of the model parameters is smaller than a preset updating amplitude threshold value, and the updating times of the model are larger than a preset updating time threshold value.

In this embodiment, the model to be trained after the parameter adjustment may be used as the target model, or the model to be trained after the parameter adjustment may be trained again to obtain the target model.

It should be noted that, in the model training method provided in this embodiment, the push related information imported into the model to be trained may be used to represent the push environment of the candidate information, and scoring the adjustment mode information of the push environment may be based on the actual push effect information, and parameters of the model to be trained are adjusted, so that the scoring may be changed toward the direction of improving the evaluation of the push effect, thereby avoiding calculating the correlation between the representation value and the push related information or the correlation between the representation value and the push effect from the content, and directly judging the adjustment mode information from the perspective of the push effect information, thereby reducing the complexity of determining the more accurate adjustment mode information.

In some embodiments, step 202 may include: selecting a target second score from the second scores by adopting a random selection mode for setting the selection probability; and determining second adjustment mode information corresponding to the selected second score of the target as second adjustment mode information of the target.

In some embodiments, the hit probability may be set for each score. The manner of setting the selection probability may be various.

In some application scenarios, the specific value of the first preset probability may be set according to an actual application scenario, and is not limited herein. As an example, the first preset probability may be set to 90%. The first predetermined probability is less than 1.

Here, the probability that the highest second score is determined as the target second score is the first preset probability. It will be appreciated that, in general, the number of top scores is one.

It should be noted that, by adopting a random calculation method for setting the selection probability, the exploratory property for selecting information of various adjustment methods can be increased. The situation that the model is in local optimization due to the fact that the highest second score is selected every time is avoided.

In some application scenarios, the selected probabilities of the non-highest second responses may or may not be equal.

As an example, the hit probability corresponding to the highest score may be set to a first preset probability. And determining the ratio of the difference value of 1 and the first preset probability to the number of scores with the highest score as a second preset probability. The second predetermined probability may be taken as the respective non-highest scored hit probability. In this case, the probability of hits for each non-highest second score is equal.

In other words, if the product of the number of highest scores and the first preset probability is taken as the first product. And determining the probability of other second answers except the highest score as the target second score as a second preset probability. The product of the number of replies other than the highest score with a second preset probability is taken as the second product. The sum of the first product and the second product equals 1.

As an example, if there are 10 answers, 10 different hit probabilities may be set; the sum of these 10 selection probabilities is 1. In this case, the selected probabilities for the respective non-highest second scores are not equal.

In some embodiments, the model training step comprises: and increasing the first preset probability based on the increase of the number of times of model training and updating.

Here, as the model training proceeds, the first preset probability, i.e., the hit probability of the highest second answer, may be increased. Therefore, the convergence rate of the model can be improved, namely, the accuracy of the model is generally improved along with the progress of model training, namely, the accuracy of the second score is also continuously improved, in this case, the exploration for other adjusting modes can be reduced, and the model parameters are improved based on the highest second score. When the real data is used for model training and the model to be trained is continuously applied to the scene of the real scene, the pushing effect of the real scene can be improved.

In some embodiments, the push effect information may comprise a push effect characterizing value. The step 204 may include: determining a loss function value according to the change of the pushing effect information of the second candidate information; and adjusting the parameters of the model to be trained according to the loss function value.

In some application scenarios, the push effect information may include a push effect characterization value. As an example, the characteristic value of the pushing effect may be a ratio of the number of display times to the number of clicks, and the larger the number of clicks, the smaller the characteristic value of the pushing effect is, which indicates that the pushing effect is better.

As an example, the square of the push effect characterization value may be taken as the loss function value. Alternatively, the product of the square of the push effect representation value and the preset attenuation coefficient may be used as the loss function value.

As an example, the present pushing effect representation value may be compared with the last pushing effect representation value, and if the pushing effect representation value increases, the loss function may be generated according to the highest score.

It should be noted that, if the representation value of the pushing effect increases, it is indicated that the pushing effect is reduced by using the target adjustment mode, and the score corresponding to the information of the target adjustment mode can be reduced. Thus, the score corresponding to the target adjustment method can be determined as the loss function value. Based on the model parameter to be trained adjusted by the loss function value, the score corresponding to the target adjustment mode information can be gradually reduced.

In some embodiments, step 204 includes: and adjusting parameters of the model to be trained according to the pushing effect information corresponding to a second candidate information set, wherein the second candidate information set comprises at least two pieces of second candidate information to be pushed.

It should be noted that, by using the correlation data of a plurality of second candidate information to train the model to be trained, the training model can learn the correlation characteristics of the second candidate information set space, so that when the model to be trained is used to process a single second candidate information, the processing mode can refer to the overall situation of the second candidate information set space to process the single second candidate information, that is, a certain second candidate information is processed with reference to the overall situation of the second candidate information, and the characteristic value of the processed second candidate information can be matched with the overall situation, that is, the accuracy of adjustment is improved.

Referring to fig. 3, the present disclosure provides a model training method, which may include the flow shown in fig. 3. The flow shown in fig. 3 may include step 301, step 302, step 303 and step 304.

Step 301, importing the second adjustment mode information and the second push related information of the second candidate information into the model to be trained.

The second adjustment method information in the model training method is adjustment method information in a different stage from the first adjustment method information in the information pushing method corresponding to fig. 1. The contents of the first adjustment mode information and the second adjustment mode information may be correspondingly the same; for example, the first adjustment method information may be two pieces of information, the second adjustment method information may be two pieces of information, the first adjustment method information may be respectively 1 plus and 1 minus, and the second adjustment method information may be respectively 1 plus and 1 minus.

It should be noted that the second candidate information in the model training method is candidate information in a different stage from the first candidate information in the information pushing method corresponding to fig. 1. The information content of the first candidate information and the second candidate information may be the same. In some application scenarios, the candidate information a may serve as the second candidate information in the model training stage, or may serve as the first candidate information after the model training is completed.

Step 302, selecting target second adjustment mode information from at least two second adjustment mode information according to each second score,

Step 303, utilizing the target second adjustment mode information to adjust a second to-be-adjusted characteristic value of the second candidate information, and generating a second adjusted characteristic value.

And 304, adjusting parameters of the model to be trained based on the pushing effect information.

In some embodiments, the step 302 may include: selecting a target second score from the second scores by adopting a random selection mode for setting the selection probability; and determining second adjustment mode information corresponding to the selected second score of the target as second adjustment mode information of the target.

In some embodiments, the model training method comprises: and increasing the first preset probability based on the increase of the number of times of model training and updating.

In some embodiments, the push effect information may comprise a push effect characterizing value. The step 304 may include: determining a loss function value according to the change of the pushing effect information of the second candidate information; and adjusting the parameters of the model to be trained according to the loss function value.

In some embodiments, step 304, above, includes: and adjusting parameters of the model to be trained according to the pushing effect information corresponding to a second candidate information set, wherein the second candidate information set comprises at least two pieces of second candidate information to be pushed.

With further reference to fig. 4, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an information pushing apparatus, which corresponds to the method embodiment shown in fig. 1, and which can be applied in various electronic devices.

As shown in fig. 4, the information pushing apparatus of the present embodiment includes: an importing unit 401, a selecting unit 402, a generating unit 403 and a pushing unit 404. The system comprises an importing unit, a target model and a plurality of pieces of first adjustment mode information, wherein the importing unit is used for importing first adjustment mode information and first push related information of first candidate information into the target model to obtain first scores corresponding to the first adjustment mode information, and the number of the first adjustment mode information is at least two; the selecting unit is used for selecting target first adjustment mode information from the at least two pieces of first adjustment mode information according to each first score; the generating unit is used for adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value; and the pushing unit is used for pushing the first candidate information based on the first adjusted characteristic value.

In this embodiment, specific processes of the importing unit 401, the selecting unit 402, the generating unit 403, and the pushing unit 404 of the information pushing apparatus and technical effects thereof may refer to related descriptions of step 101, step 102, step 103, and step 104 in the corresponding embodiment of fig. 1, which are not described herein again.

In some embodiments, the selecting, according to each first score, target first adjustment mode information from at least two pieces of first adjustment mode information includes: and determining the first adjustment mode information corresponding to the highest first score as target first adjustment mode information.

In some embodiments, the target model is obtained by the following model training steps, wherein the model training steps include: importing second adjustment mode information and second pushing related information of second candidate information into a model to be trained to obtain a second score corresponding to each piece of second adjustment mode information, wherein the number of the second adjustment mode information is at least two; selecting target second adjustment mode information from at least two pieces of second adjustment mode information according to each second score; adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value; and adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

In some embodiments, the second push related information comprises at least one of: the second pushing basis information and the second pushing history information; wherein

The second push compliance information includes at least one of: attribute information of the second candidate information, attribute information of the second candidate information owner;

the second push history information includes at least one of: second pushing process history information, and second pair of pushed candidate information. In some embodiments, the selecting, according to each second score, target second adjustment mode information from at least two pieces of second adjustment mode information includes: selecting a target second score from the second scores by adopting a random selection mode for setting the selection probability, wherein the selection probability of the highest second score is a first preset probability; and determining second adjustment mode information corresponding to the selected second score of the target as second adjustment mode information of the target.

In some embodiments, the adjusting the parameters of the model to be trained based on the push effect information includes: determining a loss function value according to a change of the push effect information of the second candidate information; and adjusting the parameters of the model to be trained according to the loss function value.

In some embodiments, the adjusting the parameters of the model to be trained based on the push effect information includes: and adjusting parameters of the model to be trained according to the pushing effect information corresponding to a second candidate information set, wherein the second candidate information set comprises at least two pieces of second candidate information to be pushed.

With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an information pushing apparatus, which corresponds to the method embodiment shown in fig. 1, and which can be applied in various electronic devices.

As shown in fig. 5, the information pushing apparatus of the present embodiment includes: the device comprises an importing module 501, a selecting module 502, a generating module 503 and an adjusting module 504. The model training device comprises an importing unit, a model matching unit and a model matching unit, wherein the importing unit is used for importing first push related importing modules of first adjusting mode information and first candidate information and importing second push related information of second adjusting mode information and second candidate information into a model to be trained to obtain second scores corresponding to the second adjusting mode information, and the number of the second adjusting mode information is at least two; the selecting module is used for selecting target second adjustment mode information from the at least two pieces of second adjustment mode information according to each second score; the generating module is used for adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value; and the adjusting module is used for adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

In this embodiment, the information pushing apparatus: the specific processing of the importing module 501, the selecting module 502, the generating module 503 and the adjusting module 504 and the technical effects thereof can refer to the related descriptions of step 301, step 302, step 303 and step 304 in the corresponding embodiment of fig. 3, which are not described herein again.

In some embodiments, the selecting, according to each second score, target second adjustment mode information from at least two pieces of second adjustment mode information includes: selecting a target second score from the second scores by adopting a random selection mode for setting the selection probability, wherein the selection probability of the highest second score is a first preset probability; and determining second adjustment mode information corresponding to the selected second score of the target as second adjustment mode information of the target.

Referring to fig. 6, fig. 6 illustrates an exemplary system architecture to which the information push method of one embodiment of the present disclosure may be applied.

As shown in fig. 6, the system architecture may include

terminal devices

601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the

terminal devices

601, 602, 603 and the server 605. Network 606 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.

The

terminal devices

601, 602, 603 may interact with the server 605 via the network 604 to receive or send messages or the like. The

terminal devices

601, 602, 603 may have various client applications installed thereon, such as a web browser application, a search-type application, and a news-information-type application. The client application in the

terminal device

601, 602, 603 may receive the instruction of the user, and complete the corresponding function according to the instruction of the user, for example, add the corresponding information in the information according to the instruction of the user.

The

terminal devices

601, 602, 603 may be hardware or software. When the

terminal devices

601, 602, 603 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like. When the

terminal device

601, 602, 603 is software, it can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 605 may be a server providing various services, for example, receiving an information acquisition request sent by the

terminal devices

601, 602, and 603, and acquiring the presentation information corresponding to the information acquisition request in various ways according to the information acquisition request. And the relevant data of the presentation information is sent to the

terminal devices

601, 602, 603.

It should be noted that the information push method provided by the embodiment of the present disclosure may be executed by a terminal device, and accordingly, the information push apparatus may be disposed in the

terminal device

601, 602, 603. In addition, the information pushing method provided by the embodiment of the disclosure may also be executed by the server 605, and accordingly, an information pushing apparatus may be provided in the server 605.

It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to fig. 7, shown is a schematic diagram of an electronic device (e.g., a terminal device or a server of fig. 6) suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 7, the electronic device may include a processing device (e.g., central processing unit, graphics processor, etc.) 701, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage device 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Generally, the following devices may be connected to the I/O interface 705: input devices 707 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 707 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 708 including, for example, magnetic tape, hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication means 709, or may be installed from the storage means 708, or may be installed from the ROM 702. The computer program, when executed by the processing device 701, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

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

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (hypertext transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: importing first adjustment mode information and first push related information of first candidate information into a target model to obtain a first score corresponding to each piece of first adjustment mode information, wherein the number of the first adjustment mode information is at least two; selecting target first adjustment mode information from at least two pieces of first adjustment mode information according to each first score; adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value; and pushing the first candidate information based on the first adjusted characteristic value.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

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

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation on the unit itself, for example, an import unit may also be described as an "import target model unit".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An information pushing method, comprising:

importing first adjustment mode information and first push related information of first candidate information into a target model to obtain a first score corresponding to each piece of first adjustment mode information, wherein the number of the first adjustment mode information is at least two;

selecting target first adjustment mode information from at least two pieces of first adjustment mode information according to each first score;

adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value;

and pushing the first candidate information based on the first adjusted characteristic value.

2. The method according to claim 1, wherein the selecting the target first adjustment mode information from the at least two first adjustment mode information according to each first score comprises:

and determining the first adjustment mode information corresponding to the highest first score as target first adjustment mode information.

3. The method of claim 1, wherein the first push related information comprises at least one of: the first pushing basis information and the first pushing history information; wherein

The first push compliance information includes at least one of: attribute information of the first candidate information, attribute information of the first candidate information owner;

the first push history information includes at least one of: first push process history information, first history operation information on the pushed candidate information.

4. The method of claim 1, wherein pushing the first candidate information based on the first adjusted token value comprises at least one of:

determining whether to display first candidate information based on the first adjusted characterizing value;

the bit order of the first candidate information to be shown in the information sequence to be pushed is determined.

5. The method of claim 1, wherein the target model is obtained by the model training step, wherein the model training step comprises:

importing second adjustment mode information and second pushing related information of second candidate information into a model to be trained to obtain a second score corresponding to each piece of second adjustment mode information, wherein the number of the second adjustment mode information is at least two;

selecting target second adjustment mode information from at least two pieces of second adjustment mode information according to each second score;

adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value;

and adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

6. The method of claim 5, wherein the second push related information comprises at least one of: the second pushing basis information and the second pushing history information; wherein

the second push history information includes at least one of: second pushing process history information, and second pair of pushed candidate information.

7. The method according to claim 5, wherein the selecting the target second adjustment mode information from the at least two second adjustment mode information according to the respective second scores comprises:

selecting a target second score from the second scores by adopting a random selection mode for setting the selection probability, wherein the selection probability of the highest second score is a first preset probability;

and determining second adjustment mode information corresponding to the selected second score of the target as second adjustment mode information of the target.

8. The method of claim 7, wherein the model training step comprises:

and increasing the first preset probability based on the increase of the number of times of model training and updating.

9. The method of claim 5, wherein the adjusting the parameters of the model to be trained based on the push effect information comprises:

determining a loss function value according to a change of the push effect information of the second candidate information;

and adjusting the parameters of the model to be trained according to the loss function value.

10. The method of claim 5, wherein the adjusting the parameters of the model to be trained based on the push effect information comprises:

and adjusting parameters of the model to be trained according to the pushing effect information corresponding to a second candidate information set, wherein the second candidate information set comprises at least two pieces of second candidate information to be pushed.

11. A method of model training, the method comprising:

12. The method of claim 11, wherein the second push related information comprises at least one of: the second pushing basis information and the second pushing history information; wherein

13. The method according to claim 11, wherein the selecting the target second adjustment mode information from the at least two second adjustment mode information according to the respective second scores comprises:

14. The method of claim 13, wherein the model training step comprises:

15. An information pushing apparatus, comprising:

the system comprises an importing unit, a target model and a plurality of pieces of information of the first adjustment modes, wherein the importing unit is used for importing the first adjustment mode information and first push related information of first candidate information into the target model to obtain first scores corresponding to the first adjustment mode information, and the number of the first adjustment mode information is at least two;

the selecting unit is used for selecting target first adjustment mode information from the at least two pieces of first adjustment mode information according to each first score;

the generating unit is used for adjusting a first to-be-adjusted characteristic value of the first candidate information by using the target first adjustment mode information to generate a first adjusted characteristic value;

and the pushing unit is used for pushing the first candidate information based on the first adjusted characteristic value.

16. A model training apparatus, the apparatus comprising:

the importing module is used for importing the second adjustment mode information and second pushing related information of the second candidate information into the model to be trained to obtain a second score corresponding to each piece of second adjustment mode information, wherein the number of the second adjustment mode information is at least two;

the selecting module is used for selecting target second adjustment mode information from the at least two pieces of second adjustment mode information according to each second score;

the generating module is used for adjusting a second to-be-adjusted characteristic value of the second candidate information by using the target second adjustment mode information to generate a second adjusted characteristic value;

and the adjusting module is used for adjusting parameters of the model to be trained based on the pushing effect information, wherein the pushing effect information is used for representing the pushing effect of pushing the second candidate information based on the second adjusted characteristic value.

17. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10 or the method of any one of claims 11-14.

18. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-10 or the method according to any one of claims 11-14.