Information push processing method and device based on historical behavior path, and medium

Abstract

The application relates to the technical field of electric digital data processing, in particular to an information pushing processing method and device based on historical behavior paths and a medium. The method comprises the following steps: obtaining a historical behavior path set of sample entities, which comprises historical behavior paths of a plurality of sample entities, any historical behavior path of a sample entity comprises a plurality of nodes and edges connecting the nodes, and any node corresponds to one behavior; comparing a preset type attribute value of a target entity with preset type attribute values of the sample entities to obtain similar entities of the target entity; obtaining a historical behavior path of the similar entities from the historical behavior path set of the sample entities, and obtaining a target directed graph according to the historical behavior path of the similar entities; and determining pushing information of the target entity according to a target behavior of the target entity and the target directed graph. The application can improve the accuracy of pushing information of users or enterprises.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric digital data processing, in particular to an information push processing method and device based on historical behavior path and a medium. BACKGROUND

[0002] In the current era of information explosion, information push technology is widely used in many fields, aiming to provide accurate personalized services for users or enterprises and improve user or enterprise experience and service efficiency. Traditional information push methods are mostly based on static attributes (such as age, gender, region, and scale) of users or enterprises or simple behavior data (such as clicks and browsing records) for content recommendation. However, such methods have the following problems: on the one hand, static attributes are difficult to reflect the dynamic changes of user or enterprise demand, and push based on static attributes cannot be adapted in time; on the other hand, simple behavior data lacks deep mining of the behavior logic and time sequence relationship of users or enterprises. The above problems lead to difficulty in accurately and individually pushing users or enterprises, that is, the accuracy of the push information for users or enterprises is low, resulting in low satisfaction of users or enterprises with the push information. How to improve the accuracy of the push information for users or enterprises is a problem to be solved. SUMMARY

[0003] The present application aims to provide an information push processing method and device based on historical behavior path to improve the accuracy of the push information for users or enterprises.

[0004] According to a first aspect of the present application, an information push processing method based on historical behavior path is provided, which comprises the following steps:

[0005] Obtaining a historical behavior path set of sample entities; the historical behavior path set of sample entities comprises historical behavior paths of a plurality of sample entities, any historical behavior path of a sample entity comprises a plurality of nodes and edges connecting the nodes, any node corresponds to one behavior, and the weight of any edge is the time interval between the occurrence of the two behaviors corresponding to the two nodes connected by the edge; the occurrence time of the behavior corresponding to the node located in front in any historical behavior path of a sample entity is earlier than the occurrence time of the behavior corresponding to the node located behind.

[0006] Comparing the preset type attribute value of the target entity with the preset type attribute value of the sample entity to obtain similar entities of the target entity; the attribute similarity between any similar entity and the target entity is greater than or equal to a preset attribute similarity threshold.

[0007] obtaining a historical behavior path of the similar entity from the historical behavior path set of the sample entity, and obtaining a target directed graph according to the historical behavior path of the similar entity; any node in the target directed graph corresponds to a behavior, and a first weight corresponding to any directed edge in the directed graph is a probability of occurrence from a behavior corresponding to a tail node of the directed edge to a behavior corresponding to a head node, and a second weight corresponding to any directed edge in the directed graph is a time interval of occurrence from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node.

[0008] determining push information of the target entity according to the target behavior of the target entity and the target directed graph.

[0009] Further, obtaining a target directed graph according to the historical behavior path of the similar entity comprises:

[0010] extracting all nodes in each historical behavior path of each similar entity to construct a node set of the directed graph by traversing the historical behavior path of all similar entities.

[0011] constructing edges of the directed graph according to the historical behavior path of the similar entity, comprising: for each historical behavior path of each similar entity, sequentially processing adjacent node pairs in order of node position to add a directed edge between two nodes corresponding to each adjacent node pair, and the directed edge added between the two nodes corresponding to each adjacent node pair is pointed from a node in the adjacent node pair in the first position to a node in the adjacent node pair in the second position.

[0012] obtaining the first weight corresponding to each directed edge in the directed graph; the first weight corresponding to any directed edge is a probability of occurrence from a behavior corresponding to a tail node of the directed edge to a behavior corresponding to a head node; and the probability of occurrence from the behavior corresponding to the tail node of any directed edge to the behavior corresponding to the head node is a ratio of a number of occurrences of an adjacent node pair corresponding to the directed edge in the historical behavior path of the similar entity to a number of occurrences of the tail node of the directed edge as a tail node.

[0013] obtaining the second weight corresponding to each directed edge in the directed graph; the second weight corresponding to any directed edge is a time interval of occurrence from a behavior corresponding to a tail node of the directed edge to a behavior corresponding to a head node; and the time interval of occurrence from the behavior corresponding to the tail node of any directed edge to the behavior corresponding to the head node is a mean of weights of edges corresponding to the directed edge in the historical behavior path of the similar entity.

[0014] determining the finally obtained directed graph as the target directed graph.

[0015] Further, determining push information of the target entity according to the target behavior of the target entity and the target directed graph comprises:

[0016] locating a node matching a target behavior of a target entity in the target directed graph.

[0017] If the locating succeeds, obtaining a head node set of the matching node in the target directed graph.

[0018] For any head node in the head node set, obtaining a first weight of a directed edge between the matching node and the head node, and if the first weight is greater than or equal to a preset weight threshold, determining a push information of the target entity according to a behavior corresponding to the head node.

[0019] Further, the push information of the target entity determined according to the behavior corresponding to the head node includes the behavior corresponding to the head node and a time interval of occurrence corresponding to the head node, and the time interval of occurrence corresponding to the head node is a second weight of a directed edge corresponding to the head node, and the directed edge corresponding to the head node is a directed edge with the matching node as a tail node and the head node as a head node.

[0020] Further, comparing a preset type attribute value of the target entity with a preset type attribute value of a sample entity to obtain a similar entity of the target entity includes:

[0021] Obtaining a similarity between each preset type attribute value of the target entity and a corresponding preset type attribute value of a specified sample entity, and if a similarity between a certain preset type attribute value of the target entity and a corresponding preset type attribute value of a specified sample entity is greater than or equal to a preset first similarity threshold, determining that the preset type attribute is a similar attribute of the target entity and the specified sample entity; and the specified sample entity is any sample entity.

[0022] Obtaining a number of similar attributes of the target entity and the specified sample entity, and determining a ratio of the number of similar attributes to a preset type number as an attribute similarity of the target entity and the specified sample entity.

[0023] If the attribute similarity of the target entity and the specified sample entity is greater than or equal to a preset attribute similarity threshold, determining the specified sample entity as a similar entity of the target entity.

[0024] Further, any behavior includes a behavior action and an object name, and a construction process of the historical behavior path set of the sample entity includes:

[0025] Obtaining a text corresponding to each candidate object name in a candidate object name set; and any candidate object name corresponding text includes a judgment condition for judging whether the behavior corresponding to the candidate object name occurs.

[0026] According to the text corresponding to the specified candidate object name, it is determined whether the specified sample entity has the behavior corresponding to the specified candidate object name, and if so, a node corresponding to the behavior corresponding to the specified candidate object name is added to the historical behavior path of the specified sample entity according to the time when the specified sample entity has the behavior corresponding to the specified candidate object name; the specified sample entity is any sample entity, and the specified candidate object name is any candidate object name.

[0027] Further, the candidate object name set acquisition process comprises:

[0028] An initial object name set is acquired; the initial object name set comprises a plurality of initial object names.

[0029] For any initial object name, the keywords and corresponding parts of speech in the initial object name are acquired.

[0030] For any two initial object names, the similarity of the two initial object names is acquired according to the keywords and corresponding parts of speech of the two initial object names, and if the similarity of the two initial object names is greater than or equal to a preset object name similarity threshold, one of the two initial object names in the initial object name set is deleted.

[0031] The updated initial object name set is determined as the candidate object name set.

[0032] Further, the similarity of the two initial object names is acquired according to the keywords and corresponding parts of speech of the two initial object names, and comprises:

[0033] The keywords of the second initial object name are rearranged according to the part-of-speech order of the keywords of the first initial object name; the first initial object name is one of the two initial object names, and the second initial object name is the other one of the two initial object names.

[0034] The semantic vector of the first initial object name is acquired according to the keyword order of the first initial object name; the semantic vector of the first initial object name is obtained by concatenating the semantic vectors corresponding to the keywords of the first initial object name according to the keyword order.

[0035] The semantic vector of the second initial object name is acquired according to the keyword order after the keywords of the second initial object name are rearranged; the semantic vector of the second initial object name is obtained by concatenating the semantic vectors corresponding to the keywords of the second initial object name according to the keyword order after the keywords are rearranged.

[0036] The similarity of the semantic vector of the first initial object name and the semantic vector of the second initial object name is determined as the similarity of the two initial object names.

[0037] According to a second aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the historical behavior path based information pushing processing method described above when executing the computer program.

[0038] According to a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is executed by a processor to implement the historical behavior path based information pushing processing method described above.

[0039] Compared with the prior art, the present application has at least the following beneficial effects:

[0040] The present application obtains a historical behavior path set of a sample entity, and the historical behavior path of any sample entity can reflect the behavior information of the sample entity and the time interval between behaviors. Based on the historical behavior path of the sample entity, the present application can obtain the time sequence features and logical relationships of the behaviors of the sample entity. On this basis, the present application compares the preset type attribute value of the target entity with the sample entity to obtain similar entities, and then constructs a target directed graph suitable for the target entity based on the historical behavior path of the similar entities. Based on the directed graph, the occurrence order relationship and the time interval relationship between different behaviors of the similar entities can be obtained, and then the target entity can be pushed more matched and more accurate personalized information through the directed graph and the target behavior of the target entity, which can improve the satisfaction of the target entity to the pushed information. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0042] Figure 1 The flowchart of the historical behavior path based information pushing processing method provided for the first embodiment of the present application is shown in the figure.

[0043] Figure 2 The flowchart of the construction process of the historical behavior path set of the sample entity provided for the first embodiment of the present application is shown in the figure.

[0044] Figure 3 The flowchart of the acquisition process of the candidate object name set provided for the first embodiment of the present application is shown in the figure.

[0045] Figure 4 The flowchart of obtaining the similarity of the two initial object names provided for the first embodiment of the present application is shown in the figure.

[0046] Figure 5 A flowchart for obtaining a similar entity of a target entity is provided for the first embodiment of the present application;

[0047] Figure 6 A flowchart for obtaining a target directed graph is provided for the first embodiment of the present application;

[0048] Figure 7 A flowchart for determining push information of a target entity is provided for the first embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work fall within the scope of protection of the present application.

[0050] Embodiment one:

[0051] According to the present embodiment, as shown in FIG. 1, a historical behavior path information push processing method is provided, and the method comprises the following steps: Figure 1

[0052] S100, obtaining a historical behavior path set of a sample entity; the historical behavior path set of the sample entity comprises historical behavior paths of a plurality of sample entities, any historical behavior path of a sample entity comprises a plurality of nodes and edges connecting the nodes, any node corresponds to a behavior, and the weight of any edge is the time interval between the occurrence of the two behaviors corresponding to the two nodes connected by the edge; the occurrence time of the behavior corresponding to the node located in front in any historical behavior path of a sample entity is earlier than the occurrence time of the behavior corresponding to the node located behind.

[0053] In the present embodiment, the sample entity is an entity (such as a user, an enterprise, etc.) participating in historical behavior data collection, the historical behavior path can reflect the behavior of the sample entity in the time dimension, one node in the historical behavior path represents one behavior, and any behavior comprises a behavior action and an object name, for example, purchasing a toothbrush, wherein purchasing is the behavior action and toothbrush is the object name; for example, applying for a high-tech enterprise, wherein applying is the behavior action and high-tech enterprise is the object name. As a specific embodiment, the edge in the historical behavior path is a directed edge connecting adjacent nodes, and the weight is the time interval between the occurrence of the two behaviors (for example, the time interval between purchasing a toothbrush and purchasing toothpaste is 3 days). As a specific embodiment, the node in the historical behavior path is the object name in the corresponding behavior, for example, the behavior corresponding to a certain node is purchasing a toothbrush, and the node is represented by the character toothbrush.

[0054] ​In this embodiment, the historical behavior path set of the sample entity is pre-constructed. As a specific implementation, as shown in Figure 2 the construction process of the historical behavior path set of the sample entity includes:

[0055] S110, obtaining the text corresponding to each candidate object name in the candidate object name set; the text corresponding to any candidate object name includes a judgment condition for judging whether the behavior corresponding to the candidate object name occurs.

[0056] As a specific implementation, as shown in Figure 3 the obtaining process of the candidate object name set includes:

[0057] S111, obtaining an initial object name set; the initial object name set includes a plurality of initial object names.

[0058] As a specific implementation, the initial object name is an object name collected from the Internet; it should be understood that the initial object name is used to distinguish the candidate object name; both the initial object name and the candidate object name are object names, the difference is that the candidate object name is processed by deleting repeated semantic names, and the initial object name is not processed by deleting repeated semantic names, therefore, there may be object names with the same semantics in the initial object name set, and the number of initial object names is greater than or equal to the number of candidate object names. For example, the two initial object names with the same semantics are 2025 model intelligent robot and intelligent robot 2025 model.

[0059] S112, for any initial object name, obtaining the keywords and corresponding parts of speech in the initial object name.

[0060] Those skilled in the art know that the process of obtaining keywords and corresponding parts of speech is prior art, which will not be described here. For example, the initial object name is 2025 model intelligent robot, wherein the keywords are 2025 model, intelligent and robot, 2025 model is a time noun phrase, intelligent is an adjective, and robot is a noun.

[0061] S113, for any two initial object names, obtaining the similarity of the two initial object names according to the keywords and corresponding parts of speech of the two initial object names, and if the similarity of the two initial object names is greater than or equal to a preset object name similarity threshold, deleting one of the two initial object names in the initial object name set.

[0062] Optionally, the preset object name similarity threshold is an empirical value, for example, the preset object name similarity threshold is 0.9.

[0063] As a specific embodiment, if the number of keywords included in the two initial object names is the same, the similarity of the two initial object names is determined according to S1131-S1134; otherwise, the similarity of the two initial object names is determined as 0. As a specific embodiment, as shown in FIG. 11B, the similarity of the two initial object names is determined according to the keywords and corresponding parts of speech of the two initial object names, including: Figure 4

[0064] S1131, re-arranging the keywords of the second initial object name according to the part-of-speech arrangement order of the keywords of the first initial object name; the first initial object name is one of the two initial object names, and the second initial object name is the other one of the two initial object names except the first initial object name.

[0065] For example, the first initial object name is a 2025 model intelligent robot, the keywords of the first initial object name are 2025 model, intelligent and robot, and the part-of-speech arrangement order of the keywords of the first initial object name is time noun phrase, adjective and noun; the second initial object name is intelligent robot 2025 model, the keywords of the second initial object name are intelligent, robot and 2025 model, and the part-of-speech arrangement order of the keywords of the second initial object name is adjective, noun and time noun phrase. The result of re-arranging the keywords of the second initial object name according to the part-of-speech arrangement order of the keywords of the first initial object name is: 2025 model, intelligent and robot.

[0066] S1132, obtaining the semantic vector of the first initial object name according to the keyword order of the first initial object name; the semantic vector of the first initial object name is obtained by concatenating the semantic vectors corresponding to the keywords of the first initial object name according to the keyword order.

[0067] Those skilled in the art know that the process of converting keywords into corresponding semantic vectors is prior art, which will not be described here. The dimensions of the semantic vectors obtained after converting any keyword are the same.

[0068] S1133, obtaining the semantic vector of the second initial object name according to the keyword order of the re-arranged keywords of the second initial object name; the semantic vector of the second initial object name is obtained by concatenating the semantic vectors corresponding to the keywords of the second initial object name according to the re-arranged keyword order.

[0069] S1134, determining the similarity of the semantic vector of the first initial object name and the semantic vector of the second initial object name as the similarity of the two initial object names.

[0070] ​In this embodiment, the semantic vector of the first initial object name and the semantic vector of the second initial object name have the same dimension. Those skilled in the art know that the process of judging the similarity of two vectors is prior art, which will not be described here.

[0071] Based on S1131-S1134, in this embodiment, the keywords of one of the two initial object names are reordered according to the part-of-speech order of the keywords of the other initial object name, and the similarity of the two initial object names is obtained by splicing the corresponding semantic vectors of the keywords, thereby solving the problem of inaccurate judgment of the similarity of the two initial object names due to different keyword orders, and improving the accuracy of the judgment result of the similarity of the two initial object names.

[0072] S114, determining the updated initial object name set as the candidate object name set.

[0073] In this embodiment, the similarity of any two initial object names in the updated initial object name set is less than the preset object name similarity threshold.

[0074] Based on S111-S114, in this embodiment, the initial object name set is processed to delete repeated semantic names, thereby obtaining a relatively concise candidate object name set, ensuring the uniqueness and semantic accuracy of the node, and enabling the historical behavior path to be more concise and accurate, and providing high-quality data for subsequent analysis.

[0075] S120, judging whether the specified sample entity has the behavior corresponding to the specified candidate object name according to the text corresponding to the specified candidate object name, and if so, appending the node corresponding to the behavior corresponding to the specified candidate object name to the historical behavior path of the specified sample entity according to the time when the specified sample entity has the behavior corresponding to the specified candidate object name; the specified sample entity is any sample entity, and the specified candidate object name is any candidate object name.

[0076] In this embodiment, the historical behavior path is constructed in time sequence, and the time sequence relationship of the behavior is preserved.

[0077] Based on S110-S120, in this embodiment, the obtained historical behavior path set of the sample entity is more concise and accurate.

[0078] S200, comparing the preset type attribute value of the target entity with the preset type attribute value of the sample entity to obtain similar entities of the target entity; the attribute similarity between any similar entity and the target entity is greater than or equal to a preset attribute similarity threshold.

[0079] In this embodiment, the target entity is the entity that needs to push information (such as the current user or the target enterprise), and the preset type attribute is the key attribute of the entity (such as the user's age, region, etc., and the enterprise's industry, size, etc.). The preset type attribute is predetermined and known.

[0080] In this embodiment, the group most closely matching the characteristics of the target entity is selected from the sample entities to ensure the relevance of subsequent behavioral analysis. As a specific implementation method, such as... Figure 5 As shown, the preset type attribute values ​​of the target entity are compared with the preset type attribute values ​​of the sample entity to obtain similar entities of the target entity, including:

[0081] S210, obtain the similarity between each preset type attribute value of the target entity and the corresponding preset type attribute value of the specified sample entity. If the similarity between a preset type attribute value of the target entity and the corresponding preset type attribute value of the specified sample entity is greater than or equal to a preset first similarity threshold, then the preset type attribute is determined to be a similar attribute between the target entity and the specified sample entity; the specified sample entity is any sample entity.

[0082] Optionally, the first preset similarity threshold is an empirical value, for example, the first preset similarity threshold is 0.9. As a specific implementation, the similarity between different attribute values ​​corresponding to any preset type attribute is pre-constructed, or a large language model is used to determine the similarity between different attribute values ​​corresponding to the same preset type attribute.

[0083] S220, obtain the number of similar attributes between the target entity and the specified sample entity, and determine the ratio of the number of similar attributes to the number of preset types as the attribute similarity between the target entity and the specified sample entity.

[0084] S230, if the attribute similarity between the target entity and the specified sample entity is greater than or equal to the preset attribute similarity threshold, then the specified sample entity is determined as a similar entity to the target entity.

[0085] Optionally, the preset attribute similarity threshold is an empirical value, such as an attribute similarity threshold of 0.8.

[0086] Based on S210-S230, similar entities to the target entity can be accurately obtained.

[0087] In this embodiment, based on similar entities, the push of behavioral data of irrelevant entities can be avoided, which improves the targeting of subsequent directed graph construction and helps to improve the accuracy of the pushed information.

[0088] S300, obtain the historical behavior path of the similar entity from the historical behavior path set of the sample entity, and obtain a target directed graph according to the historical behavior path of the similar entity; any node in the target directed graph corresponds to a behavior, and any directed edge in the directed graph corresponds to a first weight, which is the occurrence probability from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node; and any directed edge in the directed graph corresponds to a second weight, which is the time interval from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node.

[0089] In this embodiment, the target directed graph is obtained according to the historical behavior path of the similar entity, one node in the target directed graph corresponds to a behavior, and different nodes correspond to different behaviors; the directed edge in the target directed graph includes the occurrence probability and the time interval from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node. It should be understood that the tail node of the directed edge is the node connected by the directed edge and corresponding to the behavior occurring earlier in time, and the head node of the directed edge is the node connected by the directed edge and corresponding to the behavior occurring later in time.

[0090] As a specific implementation, as shown in Figure 6 obtaining the target directed graph according to the historical behavior path of the similar entity includes:

[0091] S310, traverse all the historical behavior paths of the similar entities, extract all the nodes in each historical behavior path of each similar entity, and construct a node set of the directed graph.

[0092] S320, construct the edges of the directed graph according to the historical behavior paths of the similar entities, including: for each historical behavior path of each similar entity, sequentially process adjacent node pairs in the order of node positions, add a directed edge between the two nodes corresponding to each adjacent node pair, and add a directed edge between the two nodes corresponding to each adjacent node pair, and the direction of the directed edge added between the two nodes corresponding to each adjacent node pair is from the node with the earlier position in the adjacent node pair to the node with the later position in the adjacent node pair.

[0093] In this embodiment, there are at most 2 directed edges between two nodes in the directed graph, and when there are 2 directed edges between two nodes in the directed graph, the directions of the 2 directed edges are opposite.

[0094] S330, obtain the first weight corresponding to each directed edge in the directed graph; the first weight corresponding to any directed edge is the occurrence probability from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node; and the occurrence probability from the behavior corresponding to the tail node of any directed edge to the behavior corresponding to the head node is the ratio of the number of times that the adjacent node pair corresponding to the directed edge appears in the historical behavior path of the similar entity to the number of times that the tail node of the directed edge appears as the tail node.

[0095] In this embodiment, any directed edge corresponds to a pair of adjacent nodes that satisfy a first condition, and the first condition is that the node included in the first position is the tail node of the directed edge and the node included in the second position is the head node of the directed edge.

[0096] S340, obtaining a second weight corresponding to each directed edge in the directed graph; the second weight corresponding to any directed edge is a time interval between the occurrence of the behavior corresponding to the tail node and the behavior corresponding to the head node of the directed edge; the time interval between the occurrence of the behavior corresponding to the tail node and the behavior corresponding to the head node of any directed edge is the mean value of the weight of the edge corresponding to the directed edge in the historical behavior path of the similar entity.

[0097] S350, determining the finally obtained directed graph as the target directed graph.

[0098] Based on S310-S350, the embodiment converts the discrete behavior paths of different similar entities into a graph structure, quantifies the relevance and timeliness between behaviors through the sending probability and the occurrence time interval, and thus, the association and time regularity between behaviors can be obtained based on the target directed graph, which provides data support for subsequent accurate push information.

[0099] S400, determining the push information of the target entity according to the target behavior of the target entity and the target directed graph.

[0100] In this embodiment, the target behavior is a behavior that has occurred to the target entity. For example, the target behavior is to purchase a toothbrush or to apply for a high-tech enterprise.

[0101] As a specific implementation, as shown in Figure 7 determining the push information of the target entity according to the target behavior of the target entity and the target directed graph includes:

[0102] S410, locating a node matching the target behavior of the target entity in the target directed graph.

[0103] In this embodiment, the node matching the target behavior of the target entity refers to a node whose corresponding behavior is the target behavior.

[0104] S420, if the location is successful, obtaining a head node set of the matching node in the target directed graph.

[0105] In this embodiment, the head node set of the matching node in the target directed graph is the head node of all directed edges in the target directed graph with the matching node as the tail node.

[0106] S430, for any head node in the set of head nodes, obtaining a first weight of a directed edge between the matched node and the head node, and if the first weight is greater than or equal to a preset weight threshold, determining the push information of the target entity according to a behavior corresponding to the head node.

[0107] Optionally, the preset weight threshold is an empirical value, for example, the weight threshold is 0.7.

[0108] As an optional embodiment, the push information of the target entity determined according to the behavior corresponding to the head node includes an object name in the behavior corresponding to the head node. For example, in a user shopping scenario, the target behavior is to purchase a toothbrush, the behavior corresponding to the head node is to purchase toothpaste, and the push information of the target entity determined according to the behavior corresponding to the head node includes toothpaste. The time for sending the push information to the target entity is the second weight of the directed edge from the node corresponding to the purchase of the toothbrush to the node corresponding to the purchase of the toothpaste.

[0109] As an optional embodiment, the push information of the target entity determined according to the behavior corresponding to the head node includes the behavior corresponding to the head node and a time interval of occurrence corresponding to the head node, and the time interval of occurrence corresponding to the head node is the second weight of the directed edge corresponding to the head node, and the directed edge corresponding to the head node is a directed edge with the matched node as a tail node and the head node as a head node. For example, in a scenario of declaring a policy project of an enterprise, the target behavior is to declare a high-tech enterprise, and the behavior corresponding to the head node is to declare a small giant enterprise. The push information of the target entity determined according to the behavior corresponding to the head node includes declaring a small giant enterprise and a time interval of 1 year for declaration.

[0110] As an embodiment, if the first weights of the directed edges between the matched node and multiple head nodes in the set of head nodes are all greater than or equal to the preset weight threshold, the push information of the target entity is determined according to the behaviors corresponding to the multiple head nodes, or the push information of the target entity is determined according to the behaviors corresponding to the first largest number of head nodes in the multiple head nodes.

[0111] The embodiment obtains a historical behavior path set of a sample entity, and any historical behavior path of the sample entity can reflect behavior information of the sample entity and a time interval between behaviors; based on the historical behavior path of the sample entity, the embodiment can obtain a time sequence feature and a logical relationship of behavior of the sample entity. On this basis, the embodiment compares a preset type attribute value of a target entity with the sample entity, obtains a similar entity, and then constructs a target directed graph suitable for the target entity based on a historical behavior path of the similar entity, can obtain an occurrence order relationship and a time interval relationship between different behaviors of the similar entity based on the directed graph, and can push more matched and more accurate personalized information to the target entity through the directed graph and a target behavior of the target entity, thereby improving satisfaction of the target entity to the pushed information.

[0112] Embodiment two

[0113] The embodiment provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0114] A historical behavior path set of a sample entity is obtained; the historical behavior path set of the sample entity includes historical behavior paths of a plurality of sample entities, any historical behavior path of the sample entity includes a plurality of nodes and edges connecting the nodes, any node corresponds to a behavior, and a weight of any edge is a time interval between two behaviors corresponding to two nodes connected by the edge; a behavior corresponding to a node located in front in any historical behavior path of the sample entity occurs earlier than a behavior corresponding to a node located behind.

[0115] A preset type attribute value of a target entity is compared with preset type attribute values of sample entities to obtain similar entities of the target entity; an attribute similarity between any similar entity and the target entity is greater than or equal to a preset attribute similarity threshold.

[0116] A historical behavior path of the similar entity is obtained from the historical behavior path set of the sample entity, and a target directed graph is obtained according to the historical behavior path of the similar entity; any node in the target directed graph corresponds to a behavior, a first weight corresponding to any directed edge in the directed graph is a behavior occurrence probability from a behavior corresponding to a tail node of the directed edge to a behavior corresponding to a head node, and a second weight corresponding to any directed edge in the directed graph is a behavior occurrence time interval from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node.

[0117] Push information of the target entity is determined according to a target behavior of the target entity and the target directed graph.

[0118] Embodiment three

[0119] The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0120] obtain a historical behavior path set of the sample entity; the historical behavior path set of the sample entity comprises historical behavior paths of a plurality of sample entities, each historical behavior path of the sample entity comprises a plurality of nodes and edges connecting the nodes, each node corresponds to a behavior, and a weight of each edge is an interval between occurrence times of two behaviors corresponding to two nodes connected by the edge; an occurrence time of a behavior corresponding to a node located in front in each historical behavior path of the sample entity is earlier than an occurrence time of a behavior corresponding to a node located behind.

[0121] compare a preset type attribute value of the target entity with preset type attribute values of the sample entities to obtain similar entities of the target entity; an attribute similarity between each similar entity and the target entity is greater than or equal to a preset attribute similarity threshold.

[0122] obtain a historical behavior path of each similar entity from the historical behavior path set of the sample entity, and obtain a target directed graph according to the historical behavior path of the similar entity; each node in the target directed graph corresponds to a behavior, a first weight corresponding to each directed edge in the directed graph is a probability from a behavior corresponding to a tail node of the directed edge to a behavior corresponding to a head node, and a second weight corresponding to each directed edge in the directed graph is an interval between occurrence times from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node.

[0123] determine push information of the target entity according to a target behavior of the target entity and the target directed graph.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0125] Although some specific embodiments of the present application have been described in detail by way of example with reference to the accompanying drawings, it is to be understood that the above examples are intended to be illustrative only and are not intended to limit the scope of the present application. Those skilled in the art will understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A history behavior path-based information push processing method, characterized by, The method comprises the following steps: acquiring a historical behavior path set of a sample entity; the historical behavior path set of the sample entity comprises historical behavior paths of a plurality of sample entities, any historical behavior path of a sample entity comprises a plurality of nodes and edges connecting the nodes, any node corresponds to a behavior, and the weight of any edge is the time interval between the two behaviors corresponding to the two nodes connected by the edge; the occurrence time of the behavior corresponding to the node located in front in any historical behavior path of a sample entity is earlier than the occurrence time of the behavior corresponding to the node located behind; comparing the preset type attribute value of the target entity with the preset type attribute value of the sample entity to acquire similar entities of the target entity; the attribute similarity between any similar entity and the target entity is greater than or equal to a preset attribute similarity threshold; acquiring the historical behavior path of the similar entity from the historical behavior path set of the sample entity and acquiring a target directed graph according to the historical behavior path of the similar entity; any node in the target directed graph corresponds to a behavior, the first weight corresponding to any directed edge in the directed graph is the occurrence probability from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node, and the second weight corresponding to any directed edge in the directed graph is the occurrence time interval from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node; determining the push information of the target entity according to the target behavior of the target entity and the target directed graph.

2. The history-behavior-path-based information push processing method according to claim 1, characterized by, Acquiring the target directed graph according to the historical behavior path of the similar entity comprises: traversing all the historical behavior paths of the similar entities, extracting all the nodes in each historical behavior path of each similar entity, and constructing a node set of the directed graph; constructing the edges of the directed graph according to the historical behavior paths of the similar entities, comprising: for each historical behavior path of each similar entity, sequentially processing adjacent node pairs in the order of node positions, adding a directed edge between the two nodes corresponding to each adjacent node pair, and adding a directed edge between the two nodes corresponding to each adjacent node pair, and the directed edge added between the two nodes corresponding to each adjacent node pair is directed from the node located in front in the adjacent node pair to the node located behind in the adjacent node pair; acquiring the first weight corresponding to each directed edge in the directed graph; the first weight corresponding to any directed edge is the occurrence probability from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node; the occurrence probability from the behavior corresponding to the tail node of any directed edge to the behavior corresponding to the head node is the ratio of the number of times that the adjacent node pair corresponding to the directed edge appears in the historical behavior path of the similar entity to the number of times that the tail node of the directed edge appears as a tail node; acquiring the second weight corresponding to each directed edge in the directed graph; the second weight corresponding to any directed edge is the occurrence time interval from the behavior corresponding to the tail node of the directed edge to the behavior corresponding to the head node; the occurrence time interval from the behavior corresponding to the tail node of any directed edge to the behavior corresponding to the head node is the mean of the weights of the edges corresponding to the directed edge in the historical behavior path of the similar entity; determining the finally obtained directed graph as the target directed graph.

3. The history-behavior-path-based information push processing method according to claim 2, characterized by, Determining the push information of the target entity according to the target behavior of the target entity and the target directed graph comprises: locating a node matching a target behavior of a target entity in the target directed graph; if the locating succeeds, obtaining a head node set of the matching node in the target directed graph; for any head node in the head node set, obtaining a first weight of a directed edge between the matching node and the head node, and if the first weight is greater than or equal to a preset weight threshold, determining a push information of the target entity according to a behavior corresponding to the head node.

4. The history-behavior-path-based information push processing method according to claim 3, characterized by, The push information of the target entity determined according to the behavior corresponding to the head node includes the behavior corresponding to the head node and a second weight of a directed edge corresponding to the head node, the second weight of the directed edge corresponding to the head node being an occurrence time interval corresponding to the head node.

5. The history-behavior-path-based information push processing method according to claim 1, characterized by, Comparing a preset type attribute value of the target entity with a preset type attribute value of a sample entity to obtain a similar entity of the target entity includes: obtaining a similarity between each preset type attribute value of the target entity and a corresponding preset type attribute value of a specified sample entity, and if the similarity between a certain preset type attribute value of the target entity and a corresponding preset type attribute value of a specified sample entity is greater than or equal to a preset first similarity threshold, determining that the preset type attribute is a similar attribute of the target entity and the specified sample entity; the specified sample entity being any sample entity; obtaining a number of similar attributes of the target entity and the specified sample entity, and determining a property similarity of the target entity and the specified sample entity as a ratio of the number of similar attributes to a preset type number; if the property similarity of the target entity and the specified sample entity is greater than or equal to a preset property similarity threshold, determining the specified sample entity as a similar entity of the target entity.

6. The history-behavior-path-based information push processing method according to claim 1, characterized by, Any behavior includes a behavior action and an object name, and a construction process of a historical behavior path set of a sample entity includes: obtaining a text corresponding to each candidate object name in a candidate object name set; any candidate object name corresponding text includes a judgment condition for judging whether the behavior corresponding to the candidate object name occurs; judging whether a specified sample entity has occurred a behavior corresponding to a specified candidate object name according to the text corresponding to the specified candidate object name, and if so, appending a node corresponding to the behavior corresponding to the specified candidate object name to a historical behavior path of the specified sample entity according to a time when the specified sample entity occurs the behavior corresponding to the specified candidate object name; the specified sample entity being any sample entity, and the specified candidate object name being any candidate object name.

7. The history-behavior-path-based information push processing method according to claim 6, characterized by, A process of obtaining the candidate object name set includes: obtaining an initial object name set; the initial object name set includes a plurality of initial object names; for any initial object name, obtaining a keyword and a corresponding part of speech in the initial object name; for any two initial object names, obtaining a similarity of the two initial object names according to the keywords and the corresponding parts of speech of the two initial object names, and if the similarity of the two initial object names is greater than or equal to a preset object name similarity threshold, deleting one of the two initial object names in the initial object name set. The updated initial object name set is determined as a candidate object name set.

8. The history-behavior-path-based information push processing method according to claim 7, characterized by, The similarity between the two initial object names is obtained according to keywords of the two initial object names and corresponding parts of speech. The keywords of the second initial object name are rearranged according to the part-of-speech arrangement order of the keywords of the first initial object name; the first initial object name is one of the two initial object names, and the second initial object name is the other one of the two initial object names; The semantic vector of the first initial object name is obtained according to the keyword order of the first initial object name; the semantic vector of the first initial object name is obtained by concatenating semantic vectors corresponding to the keywords of the first initial object name according to the keyword order; The semantic vector of the second initial object name is obtained according to the keyword order after the keywords of the second initial object name are rearranged; the semantic vector of the second initial object name is obtained by concatenating semantic vectors corresponding to the keywords of the second initial object name according to the keyword order after the keywords are rearranged; The similarity between the semantic vector of the first initial object name and the semantic vector of the second initial object name is determined as the similarity between the two initial object names.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the information push processing method based on a historical behavior path according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program implements the information push processing method based on a historical behavior path according to any one of claims 1 to 8 when executed by the processor.

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