CN111131359A - Method and apparatus for generating information - Google Patents
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- CN111131359A CN111131359A CN201811291522.6A CN201811291522A CN111131359A CN 111131359 A CN111131359 A CN 111131359A CN 201811291522 A CN201811291522 A CN 201811291522A CN 111131359 A CN111131359 A CN 111131359A
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/55—Push-based network services
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Abstract
The embodiment of the application discloses a method and a device for generating information. One embodiment of the method comprises: determining a target conversion target related to the target push information; acquiring a user identification set and an operation information set, wherein the operation information set comprises operation information which corresponds to user identifications in the user identification set and is related to push information under the category to which information under a set category and/or target push information belongs; analyzing the operation information set, and determining the probability that a user indicated by each user identifier in the user identifier set achieves a target conversion target through target push information; and based on the determined probability, selecting the user identification from the user identification set as a target user identification, and generating a target user identification set. The embodiment can improve the accuracy of the determined probability, and prevent potential users from being filtered as much as possible, thereby being beneficial to the cold start of the target push information.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for generating information.
Background
Information push, also called "network broadcast", is a technology for reducing information overload by pushing information required by users on the internet through a certain technical standard or protocol. The information push technology can reduce the time spent by the user in searching on the network by actively pushing information to the user.
The existing push information includes interactive push information, and a user can enter a specific website or open a specific window by selecting (for example, clicking or double clicking) such push information. Additionally, such push information will typically be associated with a translation target, such as installation completion, activation, payment, form submission or phone call, and so forth. Existing information push approaches typically utilize user data related to a transformation target associated with such push information to lock the target user, so as to push such push information to the target user. However, for such push information in the cold start phase, user data related to the conversion target associated with the push information is generally less, and the existing information push method can only lock fewer target users.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating information.
In a first aspect, an embodiment of the present application provides a method for generating information, where the method includes: determining a target conversion target related to the target push information; acquiring a user identification set and an operation information set, wherein the operation information set comprises operation information which corresponds to user identifications in the user identification set and is related to push information under the category to which information under a set category and/or target push information belongs; analyzing the operation information set, and determining the probability that a user indicated by each user identifier in the user identifier set achieves a target conversion target through target push information; and based on the determined probability, selecting the user identification from the user identification set as a target user identification, and generating a target user identification set.
In some embodiments, determining a target translation target associated with the target push information includes: determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information; determining each associated conversion target of the at least one associated conversion target as a target conversion target.
In some embodiments, the setting category includes a first category, the information in the first category is a video without a skip protocol, the operation information associated with the information in the first category includes at least one operation identifier and a video identifier set and/or an author information set corresponding to each of the at least one operation identifier, and the operation identifier is an identifier of one of the following operations: watch, focus, like, bad.
In some embodiments, the target push information corresponds to the information identifier in advance; analyzing the operation information set, and determining the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information, wherein the probability comprises the following steps: for each user identifier in the user identifier set, forming an identifier pair by the user identifier and the information identifier; and inputting the formed identification pairs and operation information corresponding to the user identification included in each identification pair into a target prediction model to obtain a prediction result, wherein the prediction result comprises the probability that the user indicated by each user identification in the user identification set achieves a target conversion target through target push information, and the target prediction model is a model obtained by learning in advance by adopting a multi-task learning method.
In some embodiments, selecting the user identification from the set of user identifications as the target user identification based on the determined probability comprises: and for each user identifier in the user identifier set, determining whether the probability which is not lower than a probability threshold exists in the probabilities corresponding to the user identifier, and if so, selecting the user identifier as a target user identifier.
In some embodiments, the above method further comprises: and pushing the target pushing information to a user side of the target user indicated by the target user identification in the target user identification set.
In a second aspect, an embodiment of the present application provides an apparatus for generating information, where the apparatus includes: a first determination unit configured to determine a target conversion target related to the target push information; an acquisition unit configured to acquire a user identifier set and an operation information set, wherein the operation information set includes operation information corresponding to a user identifier in the user identifier set and related to push information under a category to which information under a set category and/or target push information belongs; the second determining unit is configured to analyze the operation information set and determine the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information; and the generating unit is configured to select the user identifier from the user identifier set as a target user identifier based on the determined probability, and generate a target user identifier set.
In some embodiments, the first determination unit is further configured to: determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information; determining each associated conversion target of the at least one associated conversion target as a target conversion target.
In some embodiments, the setting category includes a first category, the information in the first category is a video without a skip protocol, the operation information associated with the information in the first category includes at least one operation identifier and a video identifier set and/or an author information set corresponding to each of the at least one operation identifier, and the operation identifier is an identifier of one of the following operations: watch, focus, like, bad.
In some embodiments, the target push information corresponds to the information identifier in advance; and the second determination unit is further configured to: for each user identifier in the user identifier set, forming an identifier pair by the user identifier and the information identifier; and inputting the formed identification pairs and operation information corresponding to the user identification included in each identification pair into a target prediction model to obtain a prediction result, wherein the prediction result comprises the probability that the user indicated by each user identification in the user identification set achieves a target conversion target through target push information, and the target prediction model is a model obtained by learning in advance by adopting a multi-task learning method.
In some embodiments, the generating unit is further configured to: and for each user identifier in the user identifier set, determining whether the probability which is not lower than a probability threshold exists in the probabilities corresponding to the user identifier, and if so, selecting the user identifier as a target user identifier.
In some embodiments, the above apparatus further comprises: and the pushing unit is configured to push the target pushing information to a user side of the target user indicated by the target user identifier in the target user identifier set.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon; when executed by the one or more processors, cause the one or more processors to implement a method as described in any implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which when executed by a processor implements the method described in any implementation manner of the first aspect.
According to the method and the device for generating information, the target conversion target related to the target push information is obtained, then the user identification set and the operation information set are obtained (the operation information set comprises operation information which corresponds to the user identification in the user identification set and is related to push information under the set type and/or the type to which the target push information belongs), then the operation information set is analyzed, the probability that the user indicated by each user identification in the user identification set achieves the target conversion target through the target push information is determined, so that the user identification is selected from the user identification set as the target user identification based on the determined probability, and the target user identification set is generated. By utilizing the operation information of the user related to the information under the set category, the accuracy of the determined probability can be improved, potential users are prevented from being filtered as much as possible, and the cold start of the target push information is facilitated.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for generating information according to the present application;
FIG. 3 is a schematic illustration of an application scenario of a method for generating information according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for generating information according to the present application;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for generating information according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the method for generating information or the apparatus for generating information of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The client may use the terminal devices 101, 102, 103 to interact with the server 105 over the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, an application for configuring information for a client with respect to target push information, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services. For example, the server 105 may support the client to set a conversion target for the target push information, and the server 105 may determine a target conversion target based on the conversion target, and perform processing such as analysis on the target conversion target, resulting in a processing result (e.g., the generated target user identifier set).
It should be noted that the method for generating information provided in the embodiment of the present application is generally performed by the server 105. Accordingly, the means for generating information is typically provided in the server 105.
It should be noted that the server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating information in accordance with the present application is shown. The process 200 of the method for generating information comprises the following steps:
In this embodiment, an executing subject (e.g., the server 105 shown in fig. 1) of the method for generating information may determine a target conversion target related to the target push information. The conversion goals may include, but are not limited to, download completion, installation completion, activation, payment, form submission, phone call, and the like.
The target conversion target may be a conversion target set by the client for the target push information. The execution main body can provide a conversion target setting interface for the client, so that the client can set a corresponding conversion target for the target push information according to actual needs. The executing agent may execute step 201 in response to acquiring the conversion target information set by the client for the target push information. The conversion target information may include a conversion target identifier. The conversion target identification may be a number or name of the conversion target, or the like.
It should be noted that the target push information may be interactive push information in a cold start phase. It should be understood that for any piece of push information, if the push information is not pushed to the user group or is pushed only a limited number of times, and the push information is associated with less user data, the push information may be said to be in the cold start phase.
The target push information may be used to introduce a customer's product or service to a user group, for example. The target push information may be push information in various forms (for example, in the form of pictures, text links, and the like), and the embodiment does not limit the form of the target push information.
In some optional implementations of this embodiment, the executing entity may also determine the target conversion target related to the target push information by using the following determination steps: determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information; determining each associated conversion target of the at least one associated conversion target as a target conversion target. The related conversion target may be a conversion target related to a conversion target set by the client for the target push information.
Here, the executing agent may determine the at least one associated conversion target by executing the following acquiring step: sending a request for obtaining a related conversion target to a management end of a related manager, wherein the request may include a conversion target identifier of the conversion target set by a client for the target push information; and receiving the associated conversion target identifiers returned by the management terminal, and determining a set of associated conversion targets respectively indicated by each received associated conversion target identifier as the at least one associated conversion target.
Optionally, the executing body may search target correspondence information in a preset correspondence information list for characterizing a correspondence between the conversion target and the associated conversion target, and determine the at least one associated conversion target based on the target correspondence information. The target corresponding relation information may be corresponding relation information related to a conversion target set by the client for the target push information. The correspondence information list may be stored in advance in a server locally or in remote communication with the execution main body.
In this embodiment, the execution subject may obtain a user identifier set and an operation information set. The operation information set may include operation information corresponding to the user identifier in the user identifier set and related to the push information under the set category and/or the category to which the target push information belongs. The setting type may include, for example, at least one of the following: a first type, a second type, a third type, etc. Wherein the information in the first category may be, for example, video that does not contain a jump protocol. The information in the second category may be, for example, articles. The information in the third category may be, for example, pictures that do not contain a jump protocol. The video may be a small video with a short playback time. The category to which the target push information belongs may be a coarse-grained category or a fine-grained category, and is not specifically limited herein. For example, if the target push information is associated with game software, the category to which the target push information belongs may be software or game software.
For the operation information related to the push information in the category to which the target push information belongs, the operation information may include an information identifier of the push information in the category that the user indicated by the corresponding user identifier clicked once, and an operation identifier of a series of operations performed after clicking the push information. Taking the example of the push information related to the game software, the series of operations performed by the user after clicking the push information may include, but is not limited to, downloading, installing, activating, paying, etc. For operation information related to information under a set category, the operation information may include at least one operation identifier and a category identifier set corresponding to each of the at least one operation identifier. Wherein each operation identifier of the at least one operation identifier may be an identifier of one of the following operations: watch, focus, like, bad. Taking the first category as an example, it is assumed that the operation information related to the information in the first category includes a praise identifier and a category identifier set corresponding to the praise identifier, and a category identifier in the category identifier set may be an identifier of a category to which a video not including a skip protocol is attributed, which is praised by a user.
It should be noted that the various identifiers in this application may be represented by a set number (e.g., 128) dimensional floating point numbers.
In this embodiment, the execution subject may obtain a preset first user information set from a server locally or remotely connected thereto. The first user information may include a user identifier and operation information corresponding to the user identifier and related to push information under a set category and/or a category to which the target push information belongs. The execution main body may extract a user identifier set and an operation information set from the first user information set.
For another example, the executing entity may obtain the preset second user information set from a server locally or remotely connected thereto. The second user information may include a user identifier and various operation information corresponding to the user identifier. The execution body may select second user information including operation information related to push information of a set type and/or a type to which the target push information belongs from a second user information set, and may form a second user information group. Then, the execution main body may extract the user identifier from each piece of second user information in the second user information group, and form a user identifier set with the extracted user identifier. Then, for each piece of operation information in the second user information group, if the various pieces of operation information include operation information related to information under a set category, the execution main body may extract the operation information; if the various types of operation information further include operation information related to push information of a category to which the target push information belongs, the execution main body may extract the operation information. Finally, the execution main body may combine the extracted operation information corresponding to each user identifier in the user identifier set into an operation information set.
In some optional implementations of this embodiment, the operation information related to the information in the first category may include at least one operation identifier and a video identifier set and/or an author information set corresponding to each operation identifier of the at least one operation identifier. Wherein the operation identifier may be an identifier of one of the following operations: watch, focus, like, bad. The author indicated by the author information in the author information set may be an author of a video indicated by a video identification in the video identification set. The author may be an individual or an organization, and is not specifically limited herein. The author information may include the name of the author or the name of an organization, etc.
In this embodiment, the executing entity may analyze the acquired operation information set, and determine a probability that a user indicated by each user identifier in the user identifier set achieves a target conversion target through the target push information. Wherein the probability may be a value within the interval [0, 1 ]. The greater the probability value, the greater the likelihood that the user may be characterized to achieve the corresponding target conversion goal.
As an example, the execution subject may perform analysis such as similar user and/or similar push information based on the operation information set. For the user identifier in the user identifier set, the executing body may determine, based on the operation information and the analysis result corresponding to the user identifier, a probability that the user indicated by the user identifier achieves the target conversion target through the target push information.
It is assumed that the category to which the target push information belongs is education software. The target push information corresponding information identifier a1 indicates that the target conversion target associated with the target push information is activated. The user identification set comprises user identifications B1 and B2. The operation information corresponding to the user id B1 includes operation information M1 related to the information of the first category and operation information M2 related to the push information of the education software category. The operation information M1 includes an attention identifier and a category identifier set corresponding to the attention identifier. The set of category identifications includes identifications of education categories. The operation information M2 includes an information identifier a1 and an activation operation identifier corresponding to the information identifier a 1. The operation information corresponding to the user id B2 includes operation information M3 related to the information of the first category. The operation information M3 includes an attention identifier and a category identifier set corresponding to the attention identifier. The set of category identifications includes identifications of education categories. The executing entity may determine that the users indicated by the user identifiers B1 and B2 are all interested in videos belonging to the education category based on the operation information corresponding to the user identifiers B1 and B2, respectively, and thus the executing entity may determine that the users indicated by the user identifiers B1 and B2 are similar users. For the user id B1, based on the operation information corresponding to the user id B1, the executing entity may determine that the probability that the user indicated by the user id B1 achieves the activation conversion target through the target push information is 1. For the user id B2, the operation information corresponding to the user id B2 does not include operation information related to push information in the category of educational software, but the execution subject analyzes that the users indicated by the user ids B1 and B2 are similar users, so the execution subject may also determine that the probability that the user indicated by the user id B2 achieves the activation conversion target through the target push information is 1.
And 204, selecting the user identifier from the user identifier set as a target user identifier based on the determined probability, and generating a target user identifier set.
In this embodiment, the executing agent may select a user identifier from the user identifier set as the target user identifier based on the probability determined in step 203, and generate the target user identifier set.
As an example, for each user identifier in the user identifier set, the executing agent may determine a ratio between the number of probabilities not lower than the probability threshold in the respective probabilities corresponding to the user identifier and the determined number of respective target conversion targets. The execution body may then determine whether the ratio is lower than a preset ratio. If not, the execution subject may select the user identifier as the target user identifier. It should be understood that the probability threshold and the preset ratio can be set according to actual needs, and are not particularly limited herein.
In some optional implementations of this embodiment, for each user identifier in the user identifier set, the executing entity may determine whether there is a probability not lower than a probability threshold in the respective probabilities corresponding to the user identifier. If so, the execution subject may select the user identifier as a target user identifier.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for generating information according to the present embodiment. In the application scenario of fig. 3, the server 301 stores target push information B for introducing educational software of the client C to the user group. If the client C pays attention to the payment conversion target in comparison, the client C can transmit payment conversion target information related to the target push information B to the server 301 through the terminal device 302 owned by the client C. The server 301 may determine that the client C sets a payment target for the target push information B based on the received payment target information, and the server 301 may determine that the target conversion target related to the target push information B is a payment (as indicated by reference numeral 303). Then, the server 301 may obtain a user identification set and an operation information set (as shown by reference numeral 304). Wherein, the operation information set may include operation information corresponding to the user identifier in the user identifier set and related to the push information under the category of the education software to which the set category and/or the target push information B belongs. Next, the server 301 may parse the operation information set, and determine a probability that the user indicated by each user identifier in the user identifier set achieves the payment conversion target through the target push information B (as shown by reference numeral 305). Server 301 may then select a user identification from the set of user identifications as the target user identification based on the determined probability (as indicated by reference numeral 306). Finally, the server 301 may group the selected target user identifications into a set of target user identifications (as indicated by reference numeral 307).
In the method provided by the above embodiment of the application, a target conversion target related to target push information is obtained, then a user identifier set and an operation information set are obtained (the operation information set includes operation information corresponding to user identifiers in the user identifier set and related to push information under a set category and/or under a category to which the target push information belongs), then the operation information set is analyzed, a probability that a user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information is determined, so that based on the determined probability, the user identifier is selected from the user identifier set as the target user identifier, and a target user identifier set is generated. By utilizing the operation information of the user related to the information under the set category, the accuracy of the determined probability can be improved, potential users are prevented from being filtered as much as possible, and the cold start of the target push information is facilitated.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating information is shown. The flow 400 of the method for generating information comprises the steps of:
In this embodiment, after acquiring the conversion target set by the client for the target push information, the executing body (e.g., the server 105 shown in fig. 1) of the method for generating information may determine at least one associated conversion target based on the conversion target, and determine each associated conversion target in the at least one associated conversion target as the target conversion target. The related conversion target may be a conversion target related to a conversion target set by the client for the target push information. The conversion goals may include, but are not limited to, download completion, installation completion, activation, payment, form submission, phone call placement, and the like. The target push information may be interactive push information in a cold start phase.
It should be noted that, the related description in the embodiment shown in fig. 2 can be referred to for the determination method of the associated transformation target, and details are not repeated here.
In this embodiment, the execution subject may obtain a user identifier set and an operation information set. The operation information set may include operation information corresponding to the user identifier in the user identifier set and related to the push information under the set category and/or the category to which the target push information belongs. Here, for the explanation of step 402, refer to the related explanation of step 202 in the embodiment shown in fig. 2, and no further description is provided here.
In this embodiment, for each user identifier in the user identifier set, the execution main body may combine the user identifier and the information identifier of the target push information into an identifier pair.
In this embodiment, the execution main body may input the operation information corresponding to the user identifier included in each identifier pair formed in step 403 and each identifier pair in the identifier pairs into the target prediction model, so as to obtain a prediction result. The prediction result may include a probability that the user indicated by each user identifier in the user identifier set achieves each target conversion target through the target push information.
The target prediction model may be a model that is learned in advance by a Multi-task learning (MTL) method and is used to predict a probability that the user indicated by the user identifier in the input identifier pair will reach each target conversion target determined in step 401 by the push information indicated by the information identifier in the identifier pair.
The multi-task learning method is a machine learning method for learning a plurality of related tasks together based on shared representation (shared representation). The multi-task learning relates to simultaneous parallel learning of a plurality of related tasks, gradient simultaneous backward propagation, and the plurality of tasks mutually help learning through shared representation of a bottom layer, so that the generalization effect is improved. Since the multitask learning method is a well-known technology which is widely researched and applied at present, the details are not described herein.
And 405, selecting the user identifier from the user identifier set as a target user identifier based on the probability in the prediction result, and generating a target user identifier set.
In this embodiment, the execution subject may select a user identifier from the user identifier set as a target user identifier based on a probability in the obtained prediction result, and generate a target user identifier set. Here, the method for determining the target user identifier may refer to the related description of step 204 in the embodiment shown in fig. 2, and is not described herein again.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for generating information in the present embodiment highlights a step of determining an associated conversion target of the conversion target set by the client for the target push information as a target conversion target, and a step of predicting, by using the target prediction model, a probability that the user indicated by each user identifier in the user identifier set achieves each determined target conversion target through the target push information. Therefore, the scheme described in the embodiment can realize the selection of the target user under the condition of fully utilizing the operation information of the user, can further improve the accuracy of the determined probability, avoids the filtering of potential users, and avoids the over cost of customers.
In an optional implementation manner of the method for generating information provided by the embodiments of the present application, an executing entity (e.g., the server 105 shown in fig. 1) of the method for generating information may push the target push information to a user side of a target user indicated by a target user identifier (e.g., each target user identifier) in the set of target user identifiers, so as to improve a delivery effect of the target push information in a cold start stage. Here, an application (e.g., a short message application, a mail application, a browser application, a social application, etc.) communicatively connected to the execution main body may be installed on the user side of the target user, and the execution main body may send the target push information to the application.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of an apparatus for generating information, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus 500 for generating information of the present embodiment includes: the first determining unit 501 is configured to determine a target conversion target related to the target push information; the obtaining unit 502 is configured to obtain a user identifier set and an operation information set, where the operation information set may include operation information corresponding to a user identifier in the user identifier set and related to push information under a category to which information under a set category and/or target push information belongs; the second determining unit 503 is configured to parse the operation information set, and determine a probability that a user indicated by each user identifier in the user identifier set achieves a target conversion target through the target push information; the generating unit 504 is configured to select a user identifier from the set of user identifiers as a target user identifier based on the determined probability, and generate a set of target user identifiers.
In the present embodiment, in the apparatus 500 for generating information: the specific processing of the first determining unit 501, the obtaining unit 502, the second determining unit 503 and the generating unit 504 and the technical effects thereof can refer to the related descriptions of step 201, step 202, step 203 and step 204 in the corresponding embodiment of fig. 2, which are not described herein again.
In some optional implementations of this embodiment, the first determining unit 501 may be further configured to: determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information; determining each associated conversion target of the at least one associated conversion target as a target conversion target.
In some optional implementations of this embodiment, the setting category may include a first category, the information in the first category is a video that does not include a skip protocol, and the operation information associated with the information in the first category may include at least one operation identifier and a video identifier set and/or an author information set corresponding to each operation identifier in the at least one operation identifier, where the operation identifier may be an identifier of one of the following operations: watch, focus, like, bad.
In some optional implementation manners of this embodiment, the target push information may correspond to the information identifier in advance; and the second determining unit 503 may be further configured to: for each user identifier in the user identifier set, forming an identifier pair by the user identifier and the information identifier; and inputting the operation information corresponding to the user identifications included in each formed identification pair and each identification pair into a target prediction model to obtain a prediction result, wherein the prediction result can comprise the probability that the user indicated by each user identification in the user identification set achieves a target conversion target through target push information, and the target prediction model can be a model obtained by learning in advance by adopting a multi-task learning method.
In some optional implementations of this embodiment, the generating unit 504 may be further configured to: and for each user identifier in the user identifier set, determining whether the probability which is not lower than a probability threshold exists in the probabilities corresponding to the user identifier, and if so, selecting the user identifier as a target user identifier.
In some optional implementations of this embodiment, the apparatus 500 may further include: and a pushing unit (not shown in the figure) configured to push the target push information to the user side of the target user indicated by the target user identifier in the target user identifier set.
The device provided by the above embodiment of the present application obtains the target conversion target related to the target push information, then obtains the user identifier set and the operation information set (the operation information set includes operation information corresponding to the user identifier in the user identifier set and related to push information under a set category and/or push information under a category to which the target push information belongs), then analyzes the operation information set, determines the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information, so that based on the determined probability, selects the user identifier from the user identifier set as the target user identifier, and generates the target user identifier set. By utilizing the operation information of the user related to the information under the set category, the accuracy of the determined probability can be improved, potential users are prevented from being filtered as much as possible, and the cold start of the target push information is facilitated.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use in implementing an electronic device (e.g., server 105 of FIG. 1) of an embodiment of the present application is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
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 embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, 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 this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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 application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first determining unit, an obtaining unit, a second determining unit, and a generating unit. The names of these units do not form a limitation on the unit itself in some cases, for example, the first determination unit may also be described as a "unit that determines a target conversion target related to the target push information".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to: determining a target conversion target related to the target push information; acquiring a user identifier set and an operation information set, wherein the operation information set may include operation information corresponding to a user identifier in the user identifier set and related to push information under a category to which information under a set category and/or target push information belongs; analyzing the operation information set, and determining the probability that a user indicated by each user identifier in the user identifier set achieves a target conversion target through target push information; and based on the determined probability, selecting the user identification from the user identification set as a target user identification, and generating a target user identification set.
The above description is only a preferred embodiment of the application 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 invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (14)
1. A method for generating information, comprising:
determining a target conversion target related to the target push information;
acquiring a user identifier set and an operation information set, wherein the operation information set comprises operation information which corresponds to user identifiers in the user identifier set and is related to push information under a set category and/or a category to which the target push information belongs;
analyzing the operation information set, and determining the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information;
and selecting the user identification from the user identification set as a target user identification based on the determined probability, and generating a target user identification set.
2. The method of claim 1, wherein the determining a target translation target associated with the target push information comprises:
determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information;
determining each associated conversion target of the at least one associated conversion target as a target conversion target.
3. The method of claim 1, wherein the set category comprises a first category, the information in the first category is a video without a skip protocol, and the operation information associated with the information in the first category comprises at least one operation identifier and a video identifier set and/or an author information set corresponding to each of the at least one operation identifier, the operation identifier being an identifier of one of the following operations: watch, focus, like, bad.
4. The method according to one of claims 1-3, wherein the target push information corresponds to an information identifier in advance; and
the analyzing the operation information set and determining the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information includes:
for each user identifier in the user identifier set, forming an identifier pair by the user identifier and the information identifier;
inputting the operation information corresponding to the user identifications included in each formed identification pair and each identification pair into a target prediction model to obtain a prediction result, wherein the prediction result comprises the probability that the user indicated by each user identification in the user identification set achieves the target conversion target through the target push information, and the target prediction model is a model obtained by adopting a multi-task learning method in advance.
5. The method of claim 1, wherein said selecting a user identification from the set of user identifications as a target user identification based on the determined probability comprises:
and for each user identifier in the user identifier set, determining whether the probability which is not lower than a probability threshold exists in the probabilities corresponding to the user identifier, and if so, selecting the user identifier as a target user identifier.
6. The method of claim 1, wherein the method further comprises:
and pushing the target pushing information to a user side of a target user indicated by the target user identification in the target user identification set.
7. An apparatus for generating information, comprising:
a first determination unit configured to determine a target conversion target related to the target push information;
an obtaining unit configured to obtain a user identifier set and an operation information set, wherein the operation information set includes operation information corresponding to a user identifier in the user identifier set and related to push information under a set category and/or a category to which the target push information belongs;
the second determining unit is configured to analyze the operation information set and determine the probability that the user indicated by each user identifier in the user identifier set achieves the target conversion target through the target push information;
and the generating unit is configured to select the user identifier from the user identifier set as a target user identifier based on the determined probability, and generate a target user identifier set.
8. The apparatus of claim 7, wherein the first determining unit is further configured to:
determining at least one associated conversion target based on a conversion target set by a client aiming at the target push information;
determining each associated conversion target of the at least one associated conversion target as a target conversion target.
9. The apparatus of claim 7, wherein the setting category comprises a first category, the information in the first category is a video without a skip protocol, and the operation information associated with the information in the first category comprises at least one operation identifier and a video identifier set and/or an author information set corresponding to each of the at least one operation identifier, the operation identifier being an identifier of one of: watch, focus, like, bad.
10. The apparatus according to one of claims 7-9, wherein the target push information corresponds to an information identifier in advance; and
the second determination unit is further configured to:
for each user identifier in the user identifier set, forming an identifier pair by the user identifier and the information identifier;
inputting the operation information corresponding to the user identifications included in each formed identification pair and each identification pair into a target prediction model to obtain a prediction result, wherein the prediction result comprises the probability that the user indicated by each user identification in the user identification set achieves the target conversion target through the target push information, and the target prediction model is a model obtained by adopting a multi-task learning method in advance.
11. The apparatus of claim 7, wherein the generating unit is further configured to:
and for each user identifier in the user identifier set, determining whether the probability which is not lower than a probability threshold exists in the probabilities corresponding to the user identifier, and if so, selecting the user identifier as a target user identifier.
12. The apparatus of claim 7, wherein the apparatus further comprises:
a pushing unit configured to push the target pushing information to a user side of a target user indicated by a target user identifier in the target user identifier set.
13. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
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