CN112348614A - Method and device for pushing information - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for pushing information. One embodiment of the method comprises: acquiring portrait information of a target user; inputting search information and portrait information of a target user into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing relations between the objects targeted by the behaviors; selecting a target behavior trace map from the behavior trace maps; and pushing a target behavior track graph. The implementation mode enables the user to refer to the behavior trace diagram of other users, and improves the richness of information pushing.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for pushing information.
Background
With the development of the internet and big data, more and more people like online shopping. Users often want to be able to refer to other people's browsing history and purchasing behavior tracks to better guide themselves in shopping. Therefore, how to provide more ways for the user to refer to shopping behaviors of other people is of great significance.
Disclosure of Invention
The embodiment of the application provides a method and a device for pushing information.
In a first aspect, an embodiment of the present application provides a method for pushing information, including: acquiring portrait information of a target user; inputting search information and portrait information of a target user into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing relations between the objects targeted by the behaviors; selecting a target behavior trace map from the behavior trace maps; and pushing a target behavior track graph.
In some embodiments, selecting a target behavior trace map from the behavior trace maps comprises: acquiring a behavior trace diagram of a related user of a target user; and selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph.
In some embodiments, before inputting the search information and the portrait information of the target user into the pre-trained recommendation model to obtain the behavior trace map, the method includes: receiving initial search information of a target user, and grading the initial search information; determining whether the score of the initial search information is greater than a preset first score threshold value; in response to determining that the score of the initial search information is greater than the first score threshold, the initial search information is determined to be search information.
In some embodiments, after determining whether the score of the initial search information is greater than a preset first score threshold, the method further comprises: in response to determining that the score of the initial search information is less than or equal to a first score threshold, determining whether the score of the initial search information is greater than a preset second score threshold; in response to determining that the score of the initial search information is greater than the second score threshold, object information of an object for which an operation performed by the target user within a preset time period is acquired, and search information of the target user is determined based on the object information and the initial search information.
In some embodiments, the method further comprises: and responding to the target user to execute preset operation aiming at the target behavior trace graph, and generating user behavior information corresponding to the target behavior trace graph.
In some embodiments, the nodes in the behavior trace graph are also used to characterize behaviors; and presenting the nodes used for representing the same behavior in the behavior trace diagram in the same preset presentation mode.
In a second aspect, an embodiment of the present application provides an apparatus for pushing information, including: an acquisition unit configured to acquire portrait information of a target user; the input unit is configured to input search information and portrait information of a target user into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing relations between the objects targeted by the behaviors; the selecting unit is configured to select a target behavior trace map from the behavior trace maps; a pushing unit configured to push the target behavior trace diagram.
In some embodiments, the selecting unit is configured to select the target behavior trace map from the behavior trace maps by: acquiring a behavior trace diagram of a related user of a target user; and selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for pushing the information, provided by the embodiment of the application, the portrait information of the target user is obtained firstly; then, inputting the search information of the target user and the portrait information into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing the relationship between the objects targeted by behaviors; then, selecting a target behavior trace map from the behavior trace maps; and finally, pushing the target behavior track graph. By the method, the user can refer to the behavior trace diagram of other users, and the richness of information push is improved.
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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 various embodiments of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for pushing information, according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a method for pushing information according to the present application;
FIG. 4 is a schematic diagram of a behavior trace diagram in a method for pushing information according to the present application;
FIG. 5 is a flow diagram of yet another embodiment of a method for pushing information according to the present application;
FIG. 6 is a schematic block diagram illustrating one embodiment of an apparatus for pushing information according to the present application;
FIG. 7 is a block diagram of a computer system suitable for use in implementing the electronic device of an embodiment 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 the method for pushing information or the apparatus for pushing 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 user may use the terminal devices 101, 102, 103 to interact with the server 105 over the network 104 to receive or send messages (e.g., the terminal devices 101, 102, 103 receive the target behavior trace graph sent by the server 105), and so on. Various communication client applications, such as shopping applications, payment applications, instant messaging software, etc., may be installed on the terminal devices 101, 102, 103.
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 that support information interaction, including but not limited to smart phones, tablet computers, laptop 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., multiple pieces of software or software modules 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 search information and the image information of the target user may be analyzed to determine a server of the target behavior trace graph to be pushed. The server 105 may first obtain portrait information for a target user; then, inputting the search information of the target user and the portrait information into a pre-trained recommendation model to obtain a behavior trace diagram; then, a target behavior trace map can be selected from the behavior trace maps; finally, the target behavior trace graph may be pushed to the terminal devices 101, 102, 103.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, 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.
It should be noted that the method for pushing information provided by the embodiment of the present application is generally performed by the server 105.
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 pushing information in accordance with the present application is shown. The method for pushing the information comprises the following steps:
In the present embodiment, an execution subject (e.g., a server shown in fig. 1) of the method for pushing information may acquire portrait information of a target user. The target user is generally a user who is performing a search operation at the present time. The execution body may determine a user from which the received search information originates as a target user. The portrait information is tagged portrait information abstracted according to user demographic information, social relationships, preference habits, consumption behaviors and other information, and the portrait information may include at least one user tag. The portrait information generally includes basic information of the user, such as age, sex, and city of residence. The representation information may also include user preference information, such as brand preference information, category preference information. By way of example, the portrait information of the target user may include: age 28, woman, city of residence, Beijing, preference for cosmetics and snacks, high consumption level, presence of kids, love for sports.
Here, the execution body generally acquires the image information of the target user after receiving the search information of the target user.
In this embodiment, the execution agent may input the search information of the target user and the portrait information acquired in step 201 into a pre-trained recommendation model to obtain a behavior trace map. The search information of the target user is generally the search information searched by the target user at the current time. The behavior trace graph may include nodes for characterizing objects targeted by behaviors and edges for characterizing relationships between the objects targeted by the behaviors, for example, the nodes for characterizing the objects targeted by the behaviors may be connected together according to a sequence of behavior time to form an edge, and a direction of the edge may be from a node in a preceding behavior time to a node in a following behavior time. The above-mentioned behavior may include, but is not limited to, at least one of: browse, click, buy, add "shopping cart", collect, share, and search.
It should be noted that the action track diagram may further include a time length of an operation performed on each object.
Here, the recommendation model may be used to represent a correspondence between both search information and picture information of the user and the behavior trace diagram. The execution subject may obtain the recommendation model from an electronic device in which the trained recommendation model is stored. Here, the electronic device storing the trained recommendation model may train a recommendation model representing a correspondence between both search information and pictorial information of the user and the behavior trace diagram in various ways.
As an example, the recommendation model may be a correspondence table in which correspondence between the behavior trace map and both the search information and the pictorial information of a plurality of users is stored, the correspondence table being prepared in advance by a technician based on statistics of search information, pictorial information, and behavior trace maps of a large number of users.
As another example, the recommendation model may be trained as follows: firstly, a training sample set can be obtained, wherein the training samples can comprise search information and image information of sample users and a sample behavior trace diagram; then, search information and portrait information of a sample user in training samples in the training sample set can be used as input of a preset initial model, a sample behavior trajectory graph in the training samples can be used as expected output of the initial model, and the initial model is trained by using a machine learning method to obtain a recommended model. Here, the initial model may include a convolutional neural network, a deep neural network, a Logistic Regression (LR), a Gradient boosting Tree (GBDT), a Factorization Machine (FM), and the like.
In this embodiment, the execution subject may select a target behavior trace map from the behavior trace maps obtained in step 202. The execution subject may determine whether a behavior trace of a preset user exists in the obtained behavior trace. The preset user may be a user who has a number of vermicelli in a preset application (e.g., a shopping-type application) greater than a preset number threshold. The preset user may also be a user corresponding to a target tag in a preset application, where the target tag is generally a tag related to search information of the target user. As an example, if the search information of the user is a computer, the predetermined user may be a user authenticated as "electronic person" in a predetermined application. If it is determined that the behavior trace map of the preset user exists in the obtained behavior trace map, the behavior trace map of the preset user can be selected from the behavior trace map to serve as a target behavior trace map.
And step 204, pushing a target behavior track graph.
In this embodiment, the executing entity may push the target behavior trace graph to the terminal device of the target user. Then, the terminal device of the target user may present the target behavior trace diagram, for example, after the target user clicks an icon for presenting the behavior trace diagram, the target behavior trace diagram may be presented.
In some optional implementations of the embodiment, the execution subject may select a target behavior trace map from the behavior trace maps by: the execution subject may obtain a behavior trace diagram of a user associated with the target user. Here, the associated user of the target user may include, but is not limited to, at least one of the following: friends of the target user and users concerned by the target user. And then, selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph. The matching of the behavior trace graphs can be that the matching degree of the objects represented by the nodes in the two behavior trace graphs is greater than a preset object matching degree threshold value. The behavior trace graphs are matched, and the first node and the last node in the two behavior trace graphs are the same.
It should be noted that the execution body may identify a user identifier of an associated user corresponding to the node, for example, identify a user name of a user from which the node is derived on the left side or the right side of the behavior trace diagram.
As an example, if there are five nodes in the first behavior trace diagram, object a, object B, object C, object D, and object E are respectively characterized. Four nodes exist in the second behavior trace diagram and respectively represent an object B, an object D, an object C and an object E. The executing body may determine a matching degree of the objects represented by the nodes in the first behavior trace diagram and the second behavior trace diagram by: first, it may be counted that the same objects among the objects represented by the nodes in the first behavior trace diagram and the second behavior trace diagram are object B, object C, object D, and object E, that is, the number of the same objects is 4. Then, the executing entity may determine that the total number of nodes in the first behavior trace diagram and the second behavior trace diagram is 9. Then, the executing body may determine a ratio of 0.89, which is two times the number of the same objects 8, to the total number of the nodes 9, as the matching degree of the objects represented by the nodes.
In some optional implementations of this embodiment, the target user may perform a preset operation on the target behavior trace graph, where the operation may include, but is not limited to, at least one of the following: comment operations, like operations, and share operations. If the target user performs the operation on the target behavior trace diagram, the execution main body may generate user behavior information corresponding to the target behavior trace diagram. As an example, if the target user performs an approval operation on the target behavior trace diagram, the execution main body may generate user approval information corresponding to the target behavior trace diagram, where the user approval information may include a user identifier of an approval user, an approval time of the user, and the like.
In some optional implementations of this embodiment, the nodes in the behavior trace graph may also be used to characterize the behavior. Here, the above-mentioned behavior may include, but is not limited to, at least one of: browse, click, buy, add "shopping cart", collect, share, and search. The nodes used for representing the same behavior in the behavior trace diagram are usually presented in the same preset presentation mode. As an example, the same behavior may be identified in the same color. For example, the "search" behavior may be identified with nodes in red and the "purchase" behavior may be identified with nodes in blue. The same behavior may also be identified in the same node shape. For example, a "search" behavior may be identified with nodes that are circular, and a "purchase" behavior may be identified with nodes that are square.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for pushing information according to the present embodiment. In the application scenario of fig. 3, after the user performs a search operation using the terminal device 302, the terminal device 302 may send the search information 303 of the user to the server 301. The server 301 may acquire the user's portrait information 304. Here, the search information 303 may be "computer", and the image information 304 may include: 28 years old, female, high consumption level, preference brand a. Then, the server 301 may input the search information 303 and the image information 304 into a recommendation model 305 trained in advance to obtain a behavior trace map 306. As shown in fig. 4, fig. 4 is a schematic diagram of a behavior trace diagram. In fig. 4, node 401 is used to characterize object a for which a behavior is intended; node 402 is used to characterize object B for which the behavior is intended; the node 403 is used for characterizing the object C for which the behavior is directed; node 404 is used to characterize the object D for which the behavior is directed; the node 405 is used to characterize the object E for which the behavior is directed. The edge 406 of the node 401 pointing to the node 402 is used to characterize the behavior for object a earlier than the behavior for object B; the edge 407 pointed to by the node 402 at the node 403 is used to characterize the behavior for the object B earlier than the behavior for the object C; the edge 408 of node 403 pointing to node 404 is used to characterize the behavior for object C earlier in time than the behavior for object D; the edge 409 of the node 404 pointing to the node 405 is used to characterize the behavior for the object D earlier than the behavior for the object E. The server 301 may then select a target behavior trace 307 from the behavior trace 306. Here, it may be determined whether there are behavior trace diagrams of users whose fans are more than one hundred thousand in the behavior trace diagram 306, and if so, the behavior trace diagram of the user whose fans are more than one hundred thousand may be selected from the behavior trace diagram 306 as the target behavior trace diagram 307. Finally, the server 301 may push the target behavior trace graph 307 to the terminal device 302.
The method provided by the embodiment of the application pushes the behavior trace diagrams of other users to the user through the search information and the image information based on the user. By the method, the richness of information pushing is improved, and the user can refer to the behavior trace diagrams of other users, so that shopping reference information is enriched.
With further reference to fig. 5, a flow 500 of yet another embodiment of a method for pushing information is shown. The process 500 of the method for pushing information includes the following steps:
In this embodiment, an executing body (for example, a server shown in fig. 1) of the method for pushing information may receive initial search information of a target user and score the initial search information. Here, the search information is generally a word or phrase.
Here, the execution body may determine importance of the initial search information by using a term Frequency-Inverse Document Frequency (TF-IDF) method, and set the determined importance as a score of the initial search information. The main idea of the Term Frequency-inverse document Frequency method is that if a word or phrase appears frequently (TF) in an article (which may be a search information set of a target user in a period of one month or one week) and rarely appears in other articles (which may be search information sets of other users in a period of one month or one week), the word or phrase is considered to have a good category distinguishing capability and is suitable for classification. The Inverse Document Frequency (IDF) mainly means that if fewer documents contain a certain word or phrase, the larger the IDF is, the word or phrase has a good category distinguishing capability. Thus, using the word frequency-inverse document frequency method, the importance of a word or phrase within an article can be calculated.
Here, the execution subject may also score the initial search information by a rule-based scoring method. The execution body may determine parts of speech of each word in the initial search information. Corresponding scores may be set in advance for words of each part of speech, and then, the sum of the scores may be determined as the score of the above-described initial search information. For example, if the search information is "yellow sweater", the execution main body may specify that "yellow" is an adjective, "sweater" is a noun, the score corresponding to the adjective is 0.2, and the score corresponding to the noun is 0.5, and the execution main body may specify that the score of the search information "yellow sweater" is 0.7. Here, the total score is generally set to 1.
Here, the execution subject may also score the initial search information by using a statistical learning-based method. The execution body may input the initial search information into a pre-trained scoring model to obtain a score of the initial search information.
In this embodiment, the executing body may determine whether the score of the initial search information is greater than a preset first score threshold. It should be noted that the first score threshold is generally a first preset proportion (e.g., 0.5) of the score of the initial search information. If it is determined that the score of the initial search information is greater than the first score threshold, the executing entity may execute step 503. If it is determined that the score of the initial search information is less than or equal to the first score threshold, the executing entity may execute step 504.
In this embodiment, if it is determined in step 502 that the score of the initial search information is greater than the first score threshold, the execution main body may determine the initial search information as the search information. Thereafter, step 506 may be performed.
In this embodiment, if it is determined in step 502 that the score of the initial search information is less than or equal to the first score threshold, the execution main body may determine whether the score of the initial search information is greater than a preset second score threshold. It should be noted that the second score threshold is generally a second preset ratio (e.g., 0.3) of the score of the initial search information. If it is determined that the score of the initial search information is greater than the second score threshold, the executing entity may execute step 505.
In this embodiment, if it is determined in step 504 that the score of the initial search information is greater than the second score threshold, the executing entity may obtain object information of an object targeted by an operation (e.g., an operation of searching, browsing, adding to a shopping cart, collecting, etc.) performed by the target user within a preset time period (e.g., a month or a week). Here, the object information generally includes, but is not limited to, at least one of the following: object name, category to which the object belongs, brand of the object. Thereafter, the execution body may determine search information of the target user based on the object information and the initial search information. Here, the execution body may store a correspondence table between the search information and the related information. The execution body may search the corresponding information corresponding to the initial search information in the correspondence table, and then may determine whether there is object information matching the corresponding information in the object information, and if so, may determine the matching object information as the search information of the target user. Here, the object information matching the related information may be object information of the same type as the related information or object information of the same brand as the related information.
As an example, if the initial search information is "home small", the executing entity may find the associated information corresponding to the initial search information "home small" in the correspondence table, including: washing machine, refrigerator, electric cooker, fan, water dispenser and dust collector. If the search information of the target user in the week comprises: computer, mobile phone, laundry detergent, chocolate and refrigerator, the search information "refrigerator" existing in the above related information can be determined as the search information of the above target user.
And 507, inputting the search information and the portrait information of the target user into a pre-trained recommendation model to obtain a behavior trace diagram.
And step 508, selecting a target behavior trace map from the behavior trace maps.
In the present embodiment, the steps 506-509 can be performed in a similar manner as the steps 201-204, and will not be described herein again.
As can be seen from fig. 5, compared with the embodiment corresponding to fig. 2, the flow 500 of the method for pushing information in the present embodiment embodies the step of determining the search information of the target user. Therefore, the scheme described in the embodiment can determine the search information input into the recommendation model based on the initial search information, so that the target behavior trace diagram can be pushed more accurately.
With further reference to fig. 6, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for pushing information, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.
As shown in fig. 6, the apparatus 600 for pushing information of the present embodiment includes: an acquisition unit 601, an input unit 602, a selection unit 603, and a push unit 604. Wherein the acquisition unit 601 is configured to acquire portrait information of a target user; the input unit 602 is configured to input search information and portrait information of a target user into a pre-trained recommendation model, resulting in a behavior trace graph, where the behavior trace graph includes nodes for characterizing objects targeted by behaviors and edges for characterizing relationships between the objects targeted by behaviors; the selecting unit 603 is configured to select a target behavior trace diagram from the behavior trace diagrams; the pushing unit 604 is configured to push the target behavior trace graph.
In this embodiment, specific processing of the acquiring unit 601, the inputting unit 602, the selecting unit 603, and the pushing unit 604 of the apparatus 600 for pushing information may refer to step 201, step 202, step 203, and step 204 in the corresponding embodiment of fig. 2.
In some optional implementation manners of this embodiment, the selecting unit 603 may select a target behavior trace map from the behavior trace maps by: the selecting unit 603 may obtain a behavior trace diagram of a user associated with the target user. Here, the associated user of the target user may include, but is not limited to, at least one of the following: friends of the target user and users concerned by the target user. And then, selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph. The matching of the behavior trace graphs can be that the matching degree of the objects represented by the nodes in the two behavior trace graphs is greater than a preset object matching degree threshold value. The behavior trace graphs are matched, and the first node and the last node in the two behavior trace graphs are the same.
In some optional implementations of the present embodiment, the apparatus 600 for pushing information may further include a receiving unit (not shown in the figure), a first determining unit (not shown in the figure), and a second determining unit (not shown in the figure). The receiving unit may receive initial search information of a target user and score the initial search information. Here, the search information is generally a word or phrase. Here, the receiving unit may determine the importance of the initial search information by using a term frequency-inverse document frequency method, and use the determined importance as the score of the initial search information. The receiving unit may also score the initial search information by a rule-based scoring method. The receiving unit may further score the initial search information by using a statistical learning-based method. The first determining unit may determine whether the score of the initial search information is greater than a preset first score threshold. The first score threshold is generally a first preset ratio of the score of the initial search information. The second determination unit may determine the initial search information as the search information if it is determined that the score of the initial search information is greater than the first score threshold.
In some optional implementations of the present embodiment, the apparatus 600 for pushing information may further include a third determining unit (not shown in the figure) and a fourth determining unit (not shown in the figure). If it is determined that the score of the initial search information is less than or equal to the first score threshold, the third determining unit may determine whether the score of the initial search information is greater than a preset second score threshold. It should be noted that the second score threshold is generally a second preset proportion of the score of the initial search information. If it is determined that the score of the initial search information is greater than the second score threshold, the fourth determining unit may obtain object information of an object for which an operation performed by the target user within a preset time period is directed. Here, the object information generally includes, but is not limited to, at least one of the following: object name, category to which the object belongs, brand of the object. Thereafter, the fourth determination unit may determine search information of the target user based on the object information and the initial search information. Here, the fourth determination unit may store a correspondence table between the search information and the related information. The fourth determining unit may search the corresponding relationship table for the associated information corresponding to the initial search information, and then may determine whether there is object information matching the associated information in the object information, and if so, may determine the matching object information as the search information of the target user. Here, the object information matching the related information may be object information of the same type as the related information or object information of the same brand as the related information.
In some optional implementations of the present embodiment, the apparatus 600 for pushing information may further include a generating unit (not shown in the figure). The target user may perform a preset operation on the target behavior trace diagram, where the operation may include, but is not limited to, at least one of the following: comment operations, like operations, and share operations. If the target user performs the operation on the target behavior trace diagram, the generating unit may generate user behavior information corresponding to the target behavior trace diagram. As an example, if the target user performs an approval operation on the target behavior trace diagram, the generating unit may generate user approval information corresponding to the target behavior trace diagram, where the user approval information may include a user identifier of an approval user, an approval time of the user, and the like.
In some optional implementations of this embodiment, the nodes in the behavior trace graph may also be used to characterize the behavior. Here, the above-mentioned behavior may include, but is not limited to, at least one of: browse, click, buy, add "shopping cart", collect, share, and search. The nodes used for representing the same behavior in the behavior trace diagram are usually presented in the same preset presentation mode. As an example, the same behavior may be identified in the same color. For example, the "search" behavior may be identified with nodes in red and the "purchase" behavior may be identified with nodes in blue. The same behavior may also be identified in the same node shape. For example, a "search" behavior may be identified with nodes that are circular, and a "purchase" behavior may be identified with nodes that are square.
Referring now to fig. 7, shown is a schematic diagram of an electronic device (e.g., terminal device in fig. 1) 700 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 fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the use range of the embodiments of the present disclosure.
As shown in fig. 7, electronic device 700 may include a processing means (e.g., central processing unit, graphics processor, etc.) 701 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from storage 708 into a Random Access Memory (RAM) 703. In the RAM703, 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 RAM703 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 706 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; and a communication device 709. The communication means 709 may allow the electronic device 700 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 illustrates an electronic device 700 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. Each block shown in fig. 7 may represent one device or may represent multiple devices as desired.
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 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 embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure 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 embodiments of the 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 embodiments of the present disclosure, however, a computer readable signal medium may comprise 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
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: acquiring portrait information of a target user; inputting search information and portrait information of a target user into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing relations between the objects targeted by the behaviors; selecting a target behavior trace map from the behavior trace maps; and pushing a target behavior track graph.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including 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. The described units may also be provided in a processor, and may be described as: a processor comprises an acquisition unit, an input unit, a selection unit and a pushing unit. Here, the names of these units do not constitute a limitation to the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires portrait information of a target user".
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 invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method for pushing information, comprising:
acquiring portrait information of a target user;
inputting the search information of the target user and the portrait information into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes used for representing objects targeted by behaviors and edges used for representing relations between the objects targeted by the behaviors;
selecting a target behavior trace map from the behavior trace maps;
and pushing the target behavior track graph.
2. The method of claim 1, wherein the selecting a target behavior trace graph from the behavior trace graphs comprises:
acquiring a behavior trace diagram of the associated user of the target user;
and selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph.
3. The method of claim 1, wherein before entering the search information and the portrait information of the target user into a pre-trained recommendation model to obtain a behavior trace map, the method comprises:
receiving initial search information of a target user, and grading the initial search information;
determining whether the score of the initial search information is greater than a preset first score threshold value;
determining the initial search information as search information in response to determining that the score of the initial search information is greater than the first score threshold.
4. The method of claim 3, wherein after the determining whether the score of the initial search information is greater than a preset first score threshold, the method further comprises:
in response to determining that the score of the initial search information is less than or equal to the first score threshold, determining whether the score of the initial search information is greater than a preset second score threshold;
in response to determining that the score of the initial search information is greater than the second score threshold, obtaining object information of an object for which an operation performed by the target user within a preset time period is aimed, and determining search information of the target user based on the object information and the initial search information.
5. The method of claim 1, wherein the method further comprises:
and responding to the target user to execute preset operation aiming at the target behavior trace graph, and generating user behavior information corresponding to the target behavior trace graph.
6. The method of one of claims 1-5, wherein the nodes in the behavior trace graph are also used to characterize behaviors; and
and presenting the nodes used for representing the same behavior in the behavior trace diagram in the same preset presentation mode.
7. An apparatus for pushing information, comprising:
an acquisition unit configured to acquire portrait information of a target user;
the input unit is configured to input the search information of the target user and the portrait information into a pre-trained recommendation model to obtain a behavior trace graph, wherein the behavior trace graph comprises nodes for representing objects targeted by behaviors and edges for representing relations between the objects targeted by the behaviors;
a selecting unit configured to select a target behavior trace map from the behavior trace maps;
a pushing unit configured to push the target behavior trace diagram.
8. The apparatus of claim 7, wherein the selecting unit is configured to select a target behavior trace map from the behavior trace maps by:
acquiring a behavior trace diagram of the associated user of the target user;
and selecting a behavior trace graph matched with the behavior trace graph of the associated user from the obtained behavior trace graphs as a target behavior trace graph.
9. 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.
10. 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 1-6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925982A (en) * | 2021-03-12 | 2021-06-08 | 上海意略明数字科技股份有限公司 | User redirection method and device, storage medium and computer equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228887A (en) * | 2018-01-31 | 2018-06-29 | 百度在线网络技术（北京）有限公司 | For generating the method and apparatus of information |
| CN108280200A (en) * | 2018-01-29 | 2018-07-13 | 百度在线网络技术（北京）有限公司 | Method and apparatus for pushed information |
| US20180314990A1 (en) * | 2016-01-08 | 2018-11-01 | Alibaba Group Holding Limited | Data-driven method and apparatus for handling user inquiries using collected data |
| WO2019061656A1 (en) * | 2017-09-30 | 2019-04-04 | 平安科技（深圳）有限公司 | Electronic apparatus, service place recommendation method based on lbs data, and storage medium |
| CN110245719A (en) * | 2019-03-27 | 2019-09-17 | 中国海洋大学 | A kind of Feature fusion of entity-oriented and user's portrait |
-
2019
- 2019-11-27 CN CN201911181152.5A patent/CN112348614A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180314990A1 (en) * | 2016-01-08 | 2018-11-01 | Alibaba Group Holding Limited | Data-driven method and apparatus for handling user inquiries using collected data |
| WO2019061656A1 (en) * | 2017-09-30 | 2019-04-04 | 平安科技（深圳）有限公司 | Electronic apparatus, service place recommendation method based on lbs data, and storage medium |
| CN108280200A (en) * | 2018-01-29 | 2018-07-13 | 百度在线网络技术（北京）有限公司 | Method and apparatus for pushed information |
| CN108228887A (en) * | 2018-01-31 | 2018-06-29 | 百度在线网络技术（北京）有限公司 | For generating the method and apparatus of information |
| CN110245719A (en) * | 2019-03-27 | 2019-09-17 | 中国海洋大学 | A kind of Feature fusion of entity-oriented and user's portrait |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925982A (en) * | 2021-03-12 | 2021-06-08 | 上海意略明数字科技股份有限公司 | User redirection method and device, storage medium and computer equipment |
| CN112925982B (en) * | 2021-03-12 | 2023-04-07 | 上海意略明数字科技股份有限公司 | User redirection method and device, storage medium and computer equipment |
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