Method, apparatus, device and storage medium for information processing

Abstract

Embodiments of the present disclosure relate to methods, apparatuses, devices and storage media for information processing. The method presented herein comprises: generating a target graph based on activity data of at least one user, the target graph comprising a plurality of nodes and edges connecting the plurality of nodes, each node indicating a related entity corresponding to the activity data, and each edge indicating a jump from one entity to another entity; determining at least one reference path associated with the at least one user from the target graph; and determining an importance degree of at least one target node in the target graph in performing a target activity based on at least the at least one reference path. In this way, the importance degree of the target node in performing the target activity can be more accurately and comprehensively evaluated to reasonably arrange various activities.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and more particularly to methods, apparatus, devices and computer-readable storage media for information processing. Background Technology

[0002] The internet provides access to a wide variety of resources. For example, it offers access to various applications, products, audio and video content, and more. With the rapid increase in both quantity and variety, finding resources of interest from a vast amount of information is extremely difficult for the audience. Resource providers, on the other hand, also hope their resources will attract the attention of their target audience. Therefore, recommendation systems are used to recommend resources that meet the needs of a user group. When providing recommendations related to specific resources, the goal is typically to provide content that the user needs in the appropriate context, making the recommendations more aligned with the user's requirements. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for information processing is provided. The method includes: generating a target graph based on activity data of at least one user, the target graph including multiple nodes and edges connecting the multiple nodes, each node indicating a related entity corresponding to the activity data, and each edge indicating a jump from one entity to another; determining at least one reference path associated with at least one user from the target graph; and determining the importance of at least one target node in the target graph in performing a target activity, based at least on at least one reference path.

[0004] In a second aspect of this disclosure, an apparatus for information processing is provided. The apparatus includes: a target graph generation module configured to generate a target graph based on activity data of at least one user, the target graph including a plurality of nodes and edges connecting the plurality of nodes, each node indicating a related entity corresponding to the activity data, and each edge indicating a jump from one entity to another; a reference path determination module configured to determine at least one reference path associated with at least one user from the target graph; and a node importance determination module configured to determine the importance of at least one target node in the target graph in performing a target activity, based at least on at least one reference path.

[0005] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of this disclosure, a computer program product is provided. The computer program product is tangibly stored in a computer storage medium and includes computer-executable instructions that, when executed by a device, cause the device to perform the method of the first aspect.

[0008] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram is shown of an example environment in which embodiments of the present disclosure may be implemented;

[0011] Figure 2 An interactive diagram illustrating an example process for information processing according to some embodiments of the present disclosure is shown;

[0012] Figure 3 Example target diagrams according to some embodiments of the present disclosure are shown;

[0013] Figure 4 An interactive diagram illustrating an example process for information processing according to some embodiments of the present disclosure is shown;

[0014] Figure 5 A flowchart of an example process for information processing according to some embodiments of the present disclosure is shown;

[0015] Figure 6 A schematic structural block diagram of an example apparatus for information processing according to some embodiments of the present disclosure is shown; and

[0016] Figure 7 A block diagram of an electronic device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0019] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] The term "in response to" as used herein refers to a state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of subsequent actions performed in response to such event or condition is not necessarily strongly correlated with the time when the event occurs or the condition is met. For example, in some cases, subsequent actions may be performed immediately upon the occurrence of the event or the fulfillment of the condition; while in others, they may be performed some time after the occurrence of the event or the fulfillment of the condition.

[0021] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0022] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (e.g., obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions. As an optional but non-restrictive implementation, in response to a user's active request, a prompt message may be sent to the user, for example, via a pop-up window, where the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0023] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs using multiple layers of processing units. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0024] A neural network is a machine learning network based on deep learning. A neural network processes input and provides a corresponding output, typically consisting of an input layer, an output layer, and one or more hidden layers between the input and output layers. Neural networks used in deep learning applications often include many hidden layers, thus increasing the network's depth. The layers of a neural network are connected sequentially, so that the output of the previous layer is provided as the input to the next layer. The input layer receives the input to the neural network, while the output layer's output serves as the final output. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each node processing the input from the layer above.

[0025] Machine learning typically comprises three phases: training, testing, and application (also known as inference). In the training phase, a given model is trained using a large amount of training data, iteratively updating parameter values ​​until the model can consistently generate inferences that meet the expected goals from the training data. Through training, the model can be considered to have learned the relationship between inputs and outputs (also known as an input-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing phase, test inputs are applied to the trained model to test whether it can provide the correct output, thus determining the model's performance. The testing phase can sometimes be integrated into the training phase. In the application or inference phase, the trained model can be used to process actual model inputs based on the trained parameter values ​​to determine the corresponding model output.

[0026] As briefly discussed above, the internet provides access to a wide variety of resources. For example, it offers access to various applications, products, audio and video content, and more. With the rapid increase in both quantity and variety, finding resources of interest from a vast amount of information is extremely difficult for the audience, while resource providers also hope their resources will attract the attention of their target audience. Therefore, recommendation systems are used to recommend resources that meet user needs to a specific group of users. When providing recommendations related to specific resources, the goal is usually to provide content that users need in appropriate contexts, making the recommendations more aligned with user requirements. Some solutions use attribution algorithms to evaluate the effectiveness of information processing strategies. However, real-world scenarios are complex and diverse, and existing attribution algorithms are not suitable for such complex and diverse scenarios.

[0027] To better illustrate the above phenomenon, let's take internet information delivery as an example scenario. In operation, the platform can deliver information to various applications and / or individual pages within those applications. Users, through their respective devices, can access these applications and freely switch between pages. During this process, users receive various types of information recommended by the platform. Taking application recommendations as an example, application download links can be delivered to various applications and / or individual pages within those applications. Users may ultimately download the application. To better process this information, the platform needs to analyze whether the user's download behavior is related to the previous application recommendation strategy. Furthermore, if application recommendation information is delivered to multiple applications and / or multiple pages, the platform further aims to understand which pages or sections of the page the user visited that triggered the recommendation.

[0028] To determine the aforementioned causal relationships, various schemes involving channel classification and user behavior analysis were proposed. Some schemes propose a single-touchpoint attribution approach, for example, attributing user behavior to the channel of the first / last interaction. Other schemes utilize time decay models, Ultra Virtual (UV) models, etc., to analyze the contribution rate of each channel. Still other schemes propose a multi-touchpoint attribution approach. In this approach, the channels involved are considered as a single touchpoint, and the contribution of each touchpoint is considered. Specifically, various tracking technologies are used to track user touchpoints on different channels. The user touchpoint data is aggregated and transformed into system-recognizable data. Next, based on experience, a preset weight is assigned to each channel touchpoint, and the contribution of each channel in executing the target activity is calculated based on the weight. This multi-touchpoint attribution approach requires experience to assign preset weights to each channel touchpoint. In other words, its accuracy is significantly affected by subjective judgment and cannot capture the true correlation between users and various channels.

[0029] As briefly discussed above, real-world scenarios are complex and diverse. Specifically, the number of internet users is increasing rapidly, the number of platform applications is also increasing dramatically, and the content within applications is becoming increasingly rich, resulting in highly complex user behavior patterns. In this context, existing attribution algorithms are not applicable to complex and diverse scenarios. Therefore, there is an urgent need to provide an efficient and accurate information processing solution.

[0030] Embodiments of this disclosure propose a user information processing scheme. In this scheme, a target graph is generated based on the activity data of at least one user. The target graph includes multiple nodes and edges connecting the multiple nodes. Each node indicates a related entity corresponding to the activity data, and each edge indicates a jump from one entity to another. At least one reference path associated with at least one user is determined from the target graph. Based on at least one reference path, the importance of at least one target node in the target graph in performing the target activity is determined.

[0031] In this way, according to the embodiments of this disclosure, the importance of target nodes in the execution of target activities can be assessed more accurately and comprehensively so as to rationally arrange various activities.

[0032] The following section provides a detailed description of various example implementations of this scheme, with reference to the accompanying drawings.

[0033] Example Environment

[0034] Figure 1A schematic diagram of an example environment 100 in which embodiments of this disclosure can be implemented is shown. One or more content providing entities 120-1, 120-2, 130-3 (collectively or individually referred to as content providing entity 120 for ease of discussion) can distribute the content they provide via an internal server 110. Content providing entity 120 can be an individual entity or an organizational entity, without limitation herein. One or more terminal devices 130-1, 130-2, 130-3, etc. (collectively or individually referred to as terminal device 130 for ease of discussion) are associated with content distribution platform 110 and can access various types of content provided on content distribution platform 110, for example, based on requests from corresponding users 132-1, 132-2, 132-3, etc. (collectively or individually referred to as user 132 for ease of discussion). Content distribution platform 110 can also provide corresponding content to target groups based on corresponding policies.

[0035] Terminal device 130 can deploy applications. These applications can provide user 132 with integration of multiple applications or components. These applications can function as application modules within an application. In some embodiments, applications can be downloaded and installed on terminal device 130. In some embodiments, application 120 can also be accessed in other ways, such as via a web page.

[0036] Application 120 can be any suitable type of application capable of providing media content, examples of which may include, but are not limited to: local life service applications, social applications, audio / video applications, media item playback applications, broadcast applications, etc., and the embodiments of this disclosure are not limited in this respect. Figure 1 In environment 100, if the application is active, terminal device 130 can present an interactive page through the application. The interactive page can be any suitable type of page, supporting user 132 input of any suitable type of data and presenting media items of any media type to user 132. The interactive interface can include various interfaces provided by the application.

[0037] Terminal device 130 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, handheld computers, portable gaming terminals, VR / AR devices, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 130 may also support any type of user-facing interface (such as "wearable" circuitry).

[0038] Server 130 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. Server 130 may include, for example, computing systems / servers such as mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0039] A communication connection can be established between server 130 and terminal device 125. This communication connection can be established via wired or wireless means. The communication connection may include, but is not limited to, Bluetooth, mobile network, Universal Serial Bus (USB), and Wireless Fidelity (WiFi) connections; the embodiments of this disclosure are not limited in this respect. In the embodiments of this disclosure, server 130 and terminal device 125 can achieve signaling interaction through their mutual communication connection.

[0040] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. Some exemplary embodiments of this disclosure will continue to be described below with reference to the accompanying drawings.

[0041] Example Interaction

[0042] As briefly discussed above, with the rapid development of computing technology, the number of internet users and platform applications has increased dramatically, and the content within these applications has become increasingly rich, leading to highly complex user behavior patterns. Traditional methods struggle to handle user behavior patterns across multiple channels and cannot achieve effective channel attribution. Furthermore, traditional solutions cannot provide a comprehensive analysis of the complex behaviors of users on different devices and / or platforms, nor can they integrate data scattered across different channels for in-depth analysis. In this situation, traditional solutions cannot accurately assess the contribution of different entities to the execution of target activities, thus failing to optimize resource allocation and adjust activity deployment strategies in a timely manner.

[0043] To address at least some or all of the aforementioned problems, an information processing scheme is proposed according to some embodiments of this disclosure. Compared to traditional information processing schemes, some embodiments of this disclosure propose a graph theory-based information processing method, and particularly disclose a graph theory-based attribution algorithm. According to the scheme of this disclosure, entities, users, behaviors, etc., involved in user activity data are constructed into a complex network model. Graph theory algorithms are used to deeply analyze and quantify the contribution (also known as importance) of each link involved in the information transformation process, and information processing strategies are optimized accordingly.

[0044] It should be understood that "user" in this disclosure refers to any user who can participate in various activities, including individuals, organizations, enterprises, and merchants. In short, this disclosure is not limited in terms of the specific type of user.

[0045] In various embodiments of this disclosure, the target activity refers to any activity that a user can participate in, such as a promotional activity, advertising campaign, sales promotion, etc. In short, this disclosure is not limited in the specific content of the target activity.

[0046] In various embodiments of this disclosure, nodes can be various elements involved in user activity data, including but not limited to users, user behavior, channels, deployed activities, etc. In some embodiments, each element can be identified as a node in the target graph, and the relationship between nodes can be represented as edges between nodes. Further, in some embodiments, the target node discussed in this disclosure can be a channel node.

[0047] For the purpose of better understanding the various embodiments discussed below, some example scenarios involved in this disclosure are briefly described below.

[0048] As an example scenario, the platform can promote various activities among merchants, such as Activity 1 and Activity 2. In operation, Activity 1 and Activity 2 can be deployed across various channels (examples of channels include a designated platform, a designated homepage, a designated online store, a designated pop-up, an activity square, etc.). For example, Activity 1 can be deployed on channels 1 and 2, and Activity 2 can be deployed on channels 1 and 3. Merchants can conduct various internet activities, including but not limited to activities on various channels and non-channels. Furthermore, users can participate in activities on a specific channel. Based on the information processing method disclosed herein, the importance of each channel in a user's execution of a target activity can be assessed.

[0049] As an example scenario, the platform can recommend content 1 and content 2 to a user. In operation, content 1 and content 2 can be deployed across various entities; for example, content 1 can be deployed in entities 1 and 2, and content 2 can be deployed in entities 1 and 3. Users can engage in various internet activities, including but not limited to activities on various entities and non-entities. Furthermore, users can view recommended content on a specific entity. Based on the information processing method disclosed herein, the importance of each entity in a user's activity of viewing recommended content can be assessed.

[0050] In other embodiments, the activity may also include other types, such as application promotion, website promotion, etc. In this case, the conversion of information into the desired action can be, for example, registration, clicking, downloading, browsing, etc.

[0051] It should be understood that the above example scenarios are only for a better understanding of the various embodiments of this disclosure and are not intended to limit this disclosure.

[0052] Next, we will combine Figure 2-4 The information processing schemes of various embodiments of this disclosure will be described below. For ease of discussion, they will be combined with... Figure 1 To describe Figure 2-4 First, refer to Figure 2 The diagram illustrates an interactive representation of an example process 200 for information processing according to some embodiments of the present disclosure. In operation, process 200 may be implemented by an electronic device, which may include... Figure 1 Server 110 and / or terminal device 130.

[0053] exist Figure 2 In example implementations, directed graph relationships can be constructed based on relevant user data. Furthermore, the contribution of nodes can be accurately evaluated based on algorithms such as shortest path algorithms in graph theory, graph centrality metrics, and node contribution allocation algorithms.

[0054] In some embodiments, an electronic device may generate a target graph based on activity data of at least one user. In some embodiments, the activity data indicates that each of the at least one user performs a target activity via a corresponding access process. Specifically, the target graph may include multiple nodes and edges connecting the nodes, where each node indicates a related entity corresponding to the activity data (e.g., each node indicates an entity traversed during the access process), and each edge indicates a jump from one entity to another. Figure 2 As shown, during operation, the electronic device collects event data related to the user (Action 1). Examples of events include, but are not limited to, activation of controls, browsing of specified media content, dwell time on specified pages, etc. Based on the collected activity data, the target graph discussed in this disclosure can be constructed.

[0055] See Figure 3 Example 300 is shown as an example object diagram according to some embodiments of the present disclosure. Figure 3 In the example embodiments, the target graph may include the following five types of nodes. The target graph may include the first type of nodes (also called user nodes). As briefly discussed above, users can include various entities such as individuals and merchants. In some embodiments, user nodes may also record user attributes. For example, when the user is a merchant, user attributes may include merchant name, merchant type (e.g., e-commerce platform, offline physical store, etc.), merchant geographical location, merchant size (small, medium, large), sales volume, sales channel distribution, sales cycle, etc. When the user is an individual, user attributes may include the user's historical behavior data and personal information, etc. The target graph may include the second type of nodes (also called channel nodes), such as online channels (e.g., social media, media platforms), offline channels (e.g., physical stores), etc. As briefly discussed above, channel nodes may include various channels that can display and promote information.

[0056] The target graph can also include a third type of node (also known as behavior nodes or behavior tracking points). Behavior nodes can refer to specific actions taken by users during the execution of the target activity, such as registering, browsing, downloading, etc.

[0057] The target graph can also include a fourth type of node (also known as an activity node). Activity nodes refer to various activities deployed on channel nodes, which can be described by information, and users can participate in the deployed activities by activating the information.

[0058] The target graph can also include a fifth type of node (also known as other nodes). Other nodes refer to other nodes involved in the user's activities. As an example, a platform can deploy activity A on channel A (e.g., the homepage). When browsing the internet, users can interact on multiple other pages, such as an online store page. In this case, the online store page is part of the user behavior data but not part of the channel entity. In this scenario, the online store page can be marked as an other node. Furthermore, the nodes can be connected to each other through association relationships.

[0059] exist Figure 3 The example includes user AE, other nodes A and B, channel node AC, behavior nodes A and B, and activity nodes A and B. Figure 3 The example target graph is built based on real user activity (AE) data. To better illustrate how to build a target based on user activity data, the following example will be used.

[0060] In one example, user A's behavioral data is as follows: enter channel node A, enter other node A, enter other node B, enter channel node B, enter channel node A, perform behavior A at channel node A, and participate in activity A. This behavioral data can be constructed in the target graph as: User A -> Channel A -> Other node A -> Other node B -> Channel node B -> Channel node A -> Behavior node A -> Activity node A (for ease of discussion, this is abbreviated as User A's actual path 1).

[0061] In another example, user B's behavioral data is as follows: enter channel node B, enter channel node A, perform behavior A at channel node A, and register for activity A. This behavioral data can be constructed in the target graph as: User B -> Channel node B -> Channel node A -> Behavior node A -> Activity node A (for ease of discussion, this is abbreviated as User B's actual path 1).

[0062] In another example, user C's behavioral data is as follows: enters other node B, enters channel node B, performs behavior A on channel node B, and registers for activity A. This behavioral data can be constructed in the target graph as: User C -> Other node B -> Channel node B -> Behavior node A -> Activity node A (for ease of discussion, this is abbreviated as User C's actual path 1).

[0063] In another example, user C's behavioral data is as follows: enters other node B, enters channel node C, performs behavior B at channel node C, and registers for activity B. This behavioral data can be constructed in the target graph as: User C -> Other node B -> Channel node C -> Behavior node B -> Activity node B (for ease of discussion, this is abbreviated as User C's actual path 2).

[0064] In some embodiments, user A may enter channel node A multiple times or stay at channel node A for a relatively long time. In this case, the association between user A and channel node A can be measured by adjusting the weight of the edge between user A and channel node A. In yet other embodiments, one or more users may trigger behavior A at channel A. In this case, the weight of the edge between channel node A and behavior node A may increase with the number of times the behavior is triggered. In other words, the edges between nodes in the target graph can have importance, the value of which is determined by the association between the nodes.

[0065] In some embodiments, directed graph associations can be stored in the form of an adjacency matrix. Specifically, a directed graph association with M nodes can be represented by an M*M matrix. Each element in the matrix can associate two nodes among the M nodes. When an element is 1, it indicates that there is an edge from one node to another. Conversely, when an element is 0, it indicates that there is no association between the two nodes. Alternatively, in some embodiments, directed graph associations can be stored in the form of an edge list. Specifically, the edge list is actually a table, where each row corresponds to an edge in the directed graph association, for example, the identifiers of the two corresponding nodes and the optional edge weight.

[0066] Through the above process, complex operations in the real world can be transformed into computer-processable data while maximizing the preservation of operational information.

[0067] In some embodiments, the electronic device can determine a reference path for each of multiple users to perform a target activity from a target graph. In some embodiments, the length of the reference path can satisfy a predetermined condition, such as the shortest path. The shortest path can simplify complex user behaviors, thereby obtaining the core data from user activity data.

[0068] In some embodiments, determining a reference path from the target graph for a user to perform a target activity includes: for each of a plurality of users, marking a start node and an end node in the target graph, the start node and end node indicating the starting entity and ending entity of the user's access process, respectively. Further, the electronic device utilizes a shortest path algorithm to determine, based on the start node and end node, the shortest path from the target graph for the user to perform the target activity, as a reference path.

[0069] exist Figure 2In an example embodiment, the electronic device can apply a shortest path algorithm based on the constructed directed graph associations. The node and edge information of the constructed directed graph associations is provided to the shortest path algorithm (Action 3). In some embodiments, the shortest path algorithm identifies and marks the start and end points of each user access process. Based on the marked start and end points, the shortest path between the start and end points is determined.

[0070] As an example, for user A's actual path 1, i.e., user A -> channel A -> other node A -> other node B -> channel node B -> channel node A -> behavior node A -> register for activity A, its shortest path can be user A -> channel A -> behavior node A -> activity node A. As an example, for user B's actual path 1, i.e., user B -> channel node B -> channel node A -> behavior node A -> activity node A, its shortest path can be user B -> channel node B -> channel node A -> behavior node A -> activity node A.

[0071] In some embodiments, the electronic device determines the node importance of each node in the target graph based on multiple reference paths determined for multiple users. Specifically, the electronic device determines the node importance of each node in the target graph based on the number of times each node appears in the multiple reference paths (also known as betweenness centrality). In some embodiments, edges in the target graph are labeled with edge importance (i.e., edge weights), and the edge importance of each edge is determined based at least on the number of jumps performed from the node connected to the edge during the access process indicated by the data; and wherein determining the node importance of each node in the target graph includes, for each node, also determining the node importance based on the edge importance of at least one edge connected to the node. In this way, the importance of each node in the target graph can be evaluated. Next, in conjunction with... Figure 2 and Figure 3 The importance of nodes will be explained in more detail.

[0072] exist Figure 2 In an example embodiment, the output of the shortest path algorithm can be used as input data to determine the betweenness centrality of each node (Action 4). In some embodiments, betweenness centrality can be used as a metric to measure the importance of a node. In some embodiments, betweenness centrality is primarily used to measure the mediating role of a node in the network. In some embodiments, betweenness centrality measures the importance of a node by calculating the frequency with which it appears in all shortest paths in the graph. For example, if a node appears in multiple shortest paths, its betweenness centrality is high, indicating that the node plays an important mediating role in the network.

[0073] In some embodiments, the electronic device 110 calculates the betweenness centrality of each node in the target graph (Action 5), and assigns weights to each node based on its betweenness centrality to assess its importance in the information processing process. In some embodiments, the importance of each node is comprehensively assessed by considering the weights of each node and the weights of the edges connected to each node. In operation, the betweenness centrality index and / or the weights of the edges of each node can be used as inputs to the node contribution allocation algorithm (Action 6).

[0074] For example, in Figure 3 In the example embodiment, there are two shortest paths involving channel node A, and channel node A is associated with multiple other nodes via multiple edges, each edge having a different weight. Furthermore, there are three shortest paths involving channel node B, and channel node B is associated with multiple other nodes via multiple edges, each edge having a different weight. Based on the above information, the importance of channel nodes A and B can be evaluated. Similarly, the importance of other nodes in the target graph can also be evaluated.

[0075] Next, based on the importance of nodes, the importance of each path can be further determined. In some embodiments, the electronic device, for each of multiple users, determines the path importance corresponding to multiple paths for the user to perform the target activity based on the node importance of each node in the target graph. Further, in some embodiments, the electronic device determines the importance of at least one target node in the target graph in the target activity based on the path importance of the multiple paths determined for the multiple users, where each target node indicates an entity of a predetermined type, and the importance of each target node in performing the target activity indicates the degree of contribution of the entity indicated by the target node in the process of the user performing the target activity. In some embodiments, determining the path importance of each of the multiple paths for the user to perform the target activity includes: further determining the path importance of each of the multiple paths for the user to perform the target activity based on the path length of each of the multiple paths.

[0076] exist Figure 2 In an example embodiment, the electronic device 110 can assign an attribution score to each path based on the model, according to the node contribution allocation algorithm (Action 7). Additionally, in some embodiments, the electronic device 110 can assign scores to each channel node based on the location and interaction of each channel node within the path. By accumulating and aggregating the attribution scores of all users, an overall contribution assessment of each channel can be formed. In some embodiments, the attribution scores can be recorded (Action 8).

[0077] As an example only, the actual path 1 and the shortest path for user A are described exemplarily. User A's actual path 1 is User A -> Channel A -> Other Node A -> Other Node B -> Channel Node B -> Channel Node A -> Behavior Node A -> Activity Node A, and user A's shortest path is User A -> Channel A -> A -> Behavior Node A -> Activity Node A. In operation, paths can be scored based on their length and the importance of each node involved. In some embodiments, a longer path results in a lower score, and / or a lower score for a path with less important nodes. When scoring paths, different calculation weights can be assigned to different determining factors to more reasonably determine the scores of each path. As an example, user A's actual path 1 can have a score of 2, and user A's shortest path can have a score of 5.

[0078] In some embodiments, the importance of at least one target node in the target graph in the target activity is determined by: assigning the path importance corresponding to each of the multiple paths to one or more target nodes contained in the path; and for each target node, determining the importance of the target node in performing the target activity by aggregating the path importance assigned to the target node.

[0079] In some embodiments, assigning the path importance corresponding to a path to one or more target nodes contained in the path for each of the plurality of paths includes: assigning the path importance corresponding to a path to one or more target nodes based at least on the position of one or more target nodes on the path for each of the plurality of paths.

[0080] As an example, user A's true path 1 can be scored as 2. Further, user A's true path 1 involves two target nodes: channel node A and channel node B. In some implementations, channel node A and channel node B can each be assigned 1 point. Further, in some embodiments, the positions of channel node A and channel node B in the path can be considered; for example, if channel node A is closer to behavior node A, then channel node A can be assigned 1.5 points, and channel node B can be assigned 0.5 points. User A's shortest path can be scored as 5, and user A's shortest path only involves channel node A. In this case, all 5 points can be assigned to channel node A.

[0081] After assigning path scores to each channel node, the sum of the scores for each channel node can be determined. In practice, the channel with the highest sum score can be considered the most important node in executing the target activity. For example, if channel node A has the highest score, the strategy can be adjusted to distribute more information on channel A. For instance, user A's actual path 1 has a score of 2, which can be assigned 1.5 to channel node A and 0.5 to channel node B. User A's shortest path has a score of 5, and it is entirely assigned to channel node A. In this case, the sum score for channel node A is 6.5, while the sum score for channel node B is 0.5. Therefore, channel node A can be considered to have made a higher contribution in executing the target activity.

[0082] In some embodiments, contribution scores (also known as attribution scores) can serve as the basis for real-time adjustments (Action 9). In operation, electronic device 110 collects activity and user behavior data in real time, analyzes and predicts user behavior using machine learning algorithms, dynamically adjusts attribution scores, and updates information strategies and resource allocations in real time based on attribution scores (Action 10).

[0083] In some embodiments, the updated policy determined by the real-time adjustment mechanism can be sent to the policy allocation module for application (Action 11). Optionally, in some embodiments, the adjusted information processing policy can be provided to various objects of the system (Action 12).

[0084] According to the information processing procedure of the above embodiments of this disclosure, more accurate identification of the importance of each target node in the execution of the target activity can be achieved through multi-channel attribution analysis. Furthermore, the complexity of attribution calculation caused by multiple touchpoints is simplified by utilizing shortest path algorithms and / or betweenness centrality algorithms.

[0085] Figure 4 An interactive diagram illustrating an example process of an information processing strategy according to some embodiments of this disclosure is shown. Figure 4 In the example implementation, real-time data streams can be acquired. In operation, the administrator can design and deploy a data acquisition system (Action 1). The data acquisition system can collect user behavior and activity data from different channels and use a stream processing framework to build data pipelines to achieve real-time data transmission and processing (Actions 2 and 3).

[0086] In some embodiments, the collected data can be analyzed in real time. For example... Figure 4 As shown, administrators can instruct applications to implement complex event handling techniques (Action 4). The event handling engine can monitor key events and behavioral patterns in real time (Action 5).

[0087] In some embodiments, based on real-time monitoring of key events and behavioral patterns, electronic device 110 can apply an online machine learning model to dynamically adjust attribution model parameters (Action 6).

[0088] In some embodiments, the electronic device 110 can obtain feedback information in real time and adjust the recommendation strategy. For example... Figure 4 As shown, in response to the design feedback mechanism from the administrator (Action 7), electronic device 110 can provide real-time feedback of the analysis results to the decision-making level (Action 8). The decision-making level can provide adjustment guidance (Action 9). Based on the feedback results, electronic device 110 can dynamically adjust strategies and resource allocation (Action 10).

[0089] In some embodiments, different algorithms can be tested to determine the preferred algorithm. For example... Figure 4 As shown, in response to the administrator's instruction to execute the optimization algorithm (Action 11), electronic device 110 can implement an online optimization algorithm to dynamically optimize resource allocation. Furthermore, electronic device 110 can perform tests (e.g., A / B testing) (Actions 12 and 13), and select the optimal strategy based on the test results (Action 14). Based on the submitted test results, the decision-making layer can send the determined preferred strategy to the administrator (Action 15).

[0090] In some embodiments, the administrator can visualize the recommendation strategy from multiple dimensions. In some embodiments, attribution analysis results and recommendation effectiveness can be displayed through a browser / application interface. Additionally, in some embodiments, customized views based on different dimensions are supported.

[0091] In this way, users can intuitively view the execution results of the target activity through an interactive interface, improving convenience and interactivity. Furthermore, multi-dimensional visualization technology provides an intuitive view display method to help users quickly obtain the execution results of the target activity.

[0092] According to the above-described exemplary embodiments of this disclosure, efficient and effective information processing can be achieved through precise attribution analysis.

[0093] Example Method

[0094] Further reference Figure 5 The document illustrates a flowchart 500 of a user information processing procedure according to some embodiments of the present disclosure. For ease of discussion, reference will be made to... Figure 1 The process 500 is described using environment 100. Process 500 may be executed by electronic devices, which may include server 110 and / or terminal device 130.

[0095] In box 510, the electronic device generates a target graph based on the activity data of at least one user. The target graph includes multiple nodes and edges connecting the multiple nodes. Each node indicates the relevant entity corresponding to the activity data, and each edge indicates a jump from one entity to another.

[0096] In box 520, the electronic device determines at least one reference path associated with at least one user from the target map.

[0097] In block 530, the electronic device determines the importance of at least one target node in a target graph in performing a target activity, based at least one reference path. In some embodiments, determining the importance of at least one target node in a target graph in performing a target activity includes: the electronic device determining the node importance of each node in the target graph, based at least one reference path; determining the path importance of multiple paths associated with at least one user based on the node importance of each node in the target graph; and determining the importance of at least one target node in the target graph in performing the target activity based on the path importance of the multiple paths.

[0098] In some embodiments, determining at least one reference path associated with at least one user includes: for each of the at least one user, the electronic device marks a start node and an end node in a target graph; and using a shortest path algorithm, based on the start node and the end node, determines the shortest path from the target graph for the user to perform the target activity as a reference path.

[0099] In some embodiments, determining the node importance of each node in the target graph includes: determining the node importance of each node in the target graph based on the number of times each of the multiple nodes appears in at least one reference path.

[0100] In some embodiments, the edges in the target graph are labeled with edge importance; and determining the node importance of each node in the target graph includes: for each node, further determining the node importance based on the edge importance of at least one edge to which the node is connected.

[0101] In some embodiments, determining the path importance of multiple paths associated with at least one user includes: further determining the path importance of each of the multiple paths for the user to perform a target activity based on the path length of each of the multiple paths.

[0102] In some embodiments, the importance of at least one target node in the target graph in the target activity is determined by: assigning the path importance corresponding to each of the multiple paths to one or more target nodes contained in the path; and for each target node, determining the importance of at least one target node in the target graph in the target activity by aggregating the path importance assigned to the target node.

[0103] In some embodiments, assigning the path importance corresponding to a path to one or more target nodes contained in the path for each of the plurality of paths includes: assigning the path importance corresponding to a path to one or more target nodes based at least on the position of one or more target nodes on the path for each of the plurality of paths.

[0104] In some embodiments, the length of the reference path meets a predetermined condition.

[0105] Example devices and equipment

[0106] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 6 A schematic structural block diagram of an example device 600 for data querying according to certain embodiments of the present disclosure is shown. Device 600 may be implemented as or included in terminal device 125 (or server 130, or both terminal device 125 and server 130). Various modules / components in device 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0107] like Figure 6 As shown, the apparatus 600 includes: a target graph generation module 610 configured to generate a target graph based on activity data of at least one user, the target graph including multiple nodes and edges connecting the multiple nodes, each node indicating a related entity corresponding to the activity data, and each edge indicating a jump from one entity to another; a reference path determination module 620 configured to determine at least one reference path associated with at least one user from the target graph; and a node importance determination module 630 configured to determine the importance of at least one target node in the target graph in performing a target activity based at least on at least one reference path.

[0108] In some embodiments, the node importance determination module 630 is further configured to: determine the node importance of each node in the target graph based on at least one reference path; determine the path importance of multiple paths associated with at least one user based on the node importance of each node in the target graph; and determine the importance of at least one target node in the target graph in performing the target activity based on the path importance of the multiple paths.

[0109] In some embodiments, the node importance determination module 630 is further configured to: mark the start node and the end node from the target graph for each of at least one user; and determine the shortest path from the target graph for the user to perform the target activity based on the start node and the end node using a shortest path algorithm, as a reference path.

[0110] In some embodiments, the node importance determination module 630 is further configured to determine the node importance of each node in the target graph based on the number of times each of the multiple nodes appears in at least one reference path.

[0111] In some embodiments, the edges in the target graph are labeled with edge importance. The node importance determination module 630 is further configured to determine the node importance of each node based on the edge importance of at least one edge to which the node is connected.

[0112] In some embodiments, the node importance determination module 630 is further configured to determine the path importance of each of the multiple paths for which the user performs the target activity based on the path length of each of the multiple paths.

[0113] In some embodiments, the node importance determination module 630 is further configured to: assign the path importance corresponding to each of the plurality of paths to one or more target nodes contained in the path; and for each target node, determine the importance of at least one target node in the target graph in performing the target activity by aggregating the path importance assigned to the target node.

[0114] In some embodiments, the node importance determination module 630 is further configured to: for each of the multiple paths, assign the path importance corresponding to the path to one or more target nodes based at least on the position of one or more target nodes on the path.

[0115] In some embodiments, the length of the reference path meets a predetermined condition.

[0116] like Figure 7 As shown, electronic device 700 is in the form of a general-purpose electronic device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 770, and one or more output devices 760. Processing unit 710 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 700.

[0117] Electronic device 700 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 720 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 730 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 700.

[0118] Electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 7 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 720 may include computer program product 725 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0119] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 700 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 700 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0120] Input device 750 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 760 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 700 can also communicate with one or more external devices (not shown) via communication unit 740 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 700, or with any device that enables electronic device 700 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0121] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0122] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0123] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0124] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0126] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for information processing, comprising: A target graph is generated based on the activity data of at least one user. The target graph includes multiple nodes and edges connecting the multiple nodes. Each node indicates the relevant entity corresponding to the activity data, and each edge indicates a jump from one entity to another. The nodes include user nodes, behavior nodes, channel nodes, or activity nodes. Determine at least one reference path associated with the at least one user from the target graph; as well as Based at least one reference path, determine the importance of at least one target node in the target graph in the execution of the target activity, wherein determining the importance of at least one target node in the target graph in the execution of the target activity includes: Based on at least one reference path, determine the node importance of each node in the target graph; Based on the node importance of each node in the target graph, the path importance of multiple paths associated with the at least one user is determined; and Based on the path importance of the multiple paths, the importance of at least one target node in the target graph in performing the target activity is determined, wherein each target node indicates an entity of a predetermined type, and the importance of the target node indicates the degree of contribution of the entity indicated by the target node in the process of the user performing the target activity.

2. The method of claim 1, wherein determining at least one reference path associated with the at least one user comprises: For each of the at least one user, mark the start node and the end node in the target graph; as well as Using the shortest path algorithm, based on the starting node and the ending node, the shortest path for the user to perform the target activity is determined from the target graph, and used as the reference path.

3. The method according to claim 1, wherein determining the node importance of each node in the target graph includes: The importance of each node in the target graph is determined based on the number of times each of the plurality of nodes appears in the at least one reference path.

4. The method of claim 1, wherein the edges in the target graph are labeled with edge importance; and wherein determining the node importance of each node in the target graph includes: For each node, the node importance is also determined based on the edge importance of at least one edge to which the node is connected.

5. The method of claim 1, wherein determining the path importance of multiple paths associated with the at least one user comprises: The path importance of each of the multiple paths is also determined based on the path length of each path.

6. The method according to claim 1, wherein determining the importance of at least one target node in the target graph in performing the target activity comprises: For each of the plurality of paths, the path importance corresponding to the path is assigned to one or more target nodes contained in the path; as well as For each target node, the importance of the at least one target node in performing the target activity is determined by aggregating the path importance assigned to the target node.

7. The method of claim 6, wherein assigning the path importance corresponding to each of the plurality of paths to one or more target nodes contained in the path comprises: For each of the plurality of paths, the path importance corresponding to the path is assigned to the one or more target nodes based at least on the position of the one or more target nodes on the path.

8. The method according to claim 1, wherein the length of the reference path satisfies a predetermined condition.

9. An apparatus for information processing, comprising: The target graph generation module is configured to generate a target graph based on the activity data of at least one user. The target graph includes multiple nodes and edges connecting the multiple nodes. Each node indicates the relevant entity corresponding to the activity data, and each edge indicates a jump from one entity to another. The nodes include user nodes, behavior nodes, channel nodes, or activity nodes. A reference path determination module is configured to determine at least one reference path associated with the at least one user from the target graph; as well as The node importance determination module is configured to determine the importance of at least one target node in the target graph in the execution of the target activity, based at least on the at least one reference path, wherein determining the importance of at least one target node in the target graph in the execution of the target activity includes: Based on at least one reference path, determine the node importance of each node in the target graph; Based on the node importance of each node in the target graph, the path importance of multiple paths associated with the at least one user is determined; and Based on the path importance of the multiple paths, the importance of at least one target node in the target graph in performing the target activity is determined, wherein each target node indicates an entity of a predetermined type, and the importance of the target node indicates the degree of contribution of the entity indicated by the target node in the process of the user performing the target activity.

10. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which, when executed by the at least one processing unit, cause the electronic device to perform the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 8.

12. A computer program product tangibly stored in a computer storage medium and comprising computer-executable instructions that, when executed by a device, cause the device to perform the method according to any one of claims 1 to 8.

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