28 results about "Clickstream" patented technology

The invention discloses an advertisement click flow analysis and anomaly recognition system and method, and relates to the technical field of internet advertisements, and the method comprises the steps: obtaining daily advertisement click rate data in a preset time period in real time; determining the daily advertisement click rate data in the current preset time period and comparing the daily advertisement click rate data with the daily advertisement click rate data in the same time period in the historical data; the advertisement click data volume with the fluctuation amplitude exceeding a preset threshold value is marked as abnormal data based on the comparison result, the abnormal data comprises determined abnormal data and to-be-determined abnormal data, the data meeting all abnormal feature judgment standards are determined abnormal data, the data meeting only part of abnormal feature judgment standards are to-be-determined abnormal data, and the data meeting only part of abnormal feature judgment standards are to-be-determined abnormal data. According to the method, through real-time acquisition and accurate comparison with historical data, the abnormal fluctuation of the click rate can be quickly found, the determined abnormal data and the undetermined abnormal data can be strictly distinguished, and co-occurrence feature combinations are mined by means of correlation analysis, so that misjudgment and missed judgment are avoided, and the accuracy of abnormal recognition is remarkably improved.

The present invention relates to the field of advertising delivery technology, specifically an advertising delivery optimization system and method for product promotion, comprising: a data alignment unit, the data alignment unit is used to obtain multi-source interaction data of users, wherein the multi-source interaction data includes click stream data, transaction data and social behavior data, and performs heterogeneous data spatiotemporal alignment on the multi-source interaction data to obtain aligned data corresponding to the multi-source interaction data; a feature update unit, the feature update unit is used to perform cross-modal attention fusion on the aligned data to obtain attention features. The present invention performs spatiotemporal alignment on multi-source interaction data and utilizes cross-modal attention fusion technology to update user features, which can accurately capture the user's multi-dimensional interest changes and behavior patterns, thereby accurately matching user needs during the advertising delivery process and improving advertising effectiveness.

1. Name of the designed product: main body of medical information graphical user interface for electronic device. 2. Use of the designed product: for electronic device. 3. Design points of the designed product: the interface content of the solid line part in the graphical user interface. 4. Picture or photo best indicating the design points: design 1 front view. 5. No design points, omit other views of the design. 6. Design 1 is designated as the basic design. 7. Other circumstances needing explanation: use of the graphical user interface: for displaying medical information, for example. 8. Human-computer interaction mode of the graphical user interface: the user clicks the parameter label in the interface of the front view of each design, for example, clicks "Flow", "Leak" or "Pressure", to select the data of the parameter of interest to be displayed. 9. The part shown by the dotted line in each view is not within the protection.

1. The name of the design product: electronic device with flow visualization of aerodynamic characteristics calculation graphical user interface. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. Omitted views: the electronic device is a common design, and other views are omitted. 6. The use of the graphical user interface: to help users evaluate, optimize, and understand the aerodynamic characteristics of components through flow visualization technology. 7. The human-computer interaction mode of the graphical user interface: click "movable component" in the view, import the model, and enter interface change state diagram 1; in interface change state diagram 1, click the "flow visualization" button, and enter interface change state diagram 2. 8. Other circumstances that need to be explained: "X" in the interface represents replaceable text, numbers, letters, or symbols.

To provide a user interface method for visualizing enterprise business processes as process menus, configured to improve availability of ERP solution by presenting the number of tasks to be processed as well as intuitively presenting flows of process operations to a user.SOLUTION: A user interface 120 is configured to visualize enterprise business processes in a computer system that supports Enterprise Resource Planning (ERP), as process menus 121, 122, 123, 127, 128. A process menu screen is implemented on an ERP system, thereby allowing a user who is in charge of ERP operation, who is not a developer, to directly and easily form or correct business processes in the solution. The user clicks a process menu to simply execute an event, such as inquiry or input.SELECTED DRAWING: Figure 4

The present invention provides a hierarchical attention cognitive diagnosis method based on a hybrid of knowledge association and abnormal behavior. The method uses graph neural network technology to aggregate heterogeneous test item attributes such as test item text, concepts, and difficulty, and obtains knowledge association through neighbor node update; adopts a short-term attention mechanism based on working memory capacity to aggregate the pre-order knowledge information associated in time series, realizing the first-order coupling of "knowledge-time series"; extracts key features reflecting students' guessing and carelessness from click stream features, sets guessing and carelessness functions to calculate the guessing index and carelessness index, realizing the first-order coupling of "behavior-time series"; based on the knowledge state and abnormal behavior index, sets a guessing gate and a carelessness gate to correct the cognitive state of students and obtain the comprehensive ability state of students, realizing the second-order coupling of "knowledge-time series-behavior"; finally, adopts a multi-task learning method and introduces an abnormal index prediction task to correct the prediction of learners' future answering performance.

One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.

A system and method for dynamic script generation for automated filing services is provided. In embodiments, a method includes: initiating a clickstream recording of an electronic document filing interface of a remote platform based on a triggering event; generating a clickstream recording of the electronic document filing interface, wherein the clickstream recording comprising a recording of a navigation of the electronic document filing interface through multiple steps of a document filing process, wherein the clickstream recording is in the form of scripts associated with each of the multiple steps of the document filing process; and generating automated filing instructions for the electronic document filing interface using the clickstream recording, the automated filing instructions enabling computer automated submission of one or more documents to the remote platform via the electronic document filing interface.

A group of recommendations related to an item, such as an item of content presented to a user in a page, can be ranked according to a probability distribution that is iteratively updated with each user interaction. For practical implementations, a click stream of interactions may be logged, and then applied in a batch process to update the probability distribution on any suitable schedule independent of the timing of incoming user interactions.

A vehicle data system receives a lead submission through a website supported by the vehicle data system and determines, utilizing a machine learning model, a user value for a lead associated with the lead submission. The user value represents a probability of the lead purchasing a vehicle from a dealer through the website. The vehicle data system determines a user lifetime value for the lead based at least on the user value for the lead. Subsequently, the vehicle data system obtains clickstream identifiers from a search engine and assigns a corresponding user lifetime value to each clickstream identifier. The vehicle data system aggregates the clickstream identifiers and corresponding user lifetime values in a single file and communicates the single file to a search server for consumption. The user lifetime values are utilized by the search engine in search engine marketing processes.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be fed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

Systems and methods for measuring impact of online search queries on user actions. The method includes capturing clickstream data entered via a website, the clickstream data including text-based queries associated with web searches, and clustering the queries to generate query clusters. The method also includes assigning each query cluster to an intent such that each assigned intent estimates a desired action behind the queries in the corresponding query cluster. The method further includes mapping each intent assigned to a query cluster to at least one action motivated by the intent. The method also includes computing metrics using the mapping to quantitatively measure the impact of the queries on the mapped actions by tracking performance of the actions within a predefined time period after the queries.

The invention discloses a multi-scene linkage data asset intelligent management method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the format conversion of real-time heterogeneous data streams collected by a preset business system, a preset Internet of Things device and an external API interface based on a sliding time window, and obtaining data asset information; transaction link tracking is carried out on the data asset information based on the tracking identifier, a cross-scene data relation network is constructed based on a target transaction link, and a target asset map is obtained; on the basis of a target asset map, online click stream data and offline passenger flow data are fused to determine a personalized management strategy, asset management is completed, and compared with a current data asset management method, a large amount of data resources are lack of effective utilization, data assets in multiple scenes cannot be effectively linked, and the asset management efficiency is improved. According to the method, more flexible strategy customization is realized, asset management is completed according to the personalized management strategy, data assets in multiple scenes are effectively linked, and the effectiveness of data management is improved.

Methods and systems are described herein for generating embeddings representing real-time interactions of a user device with a server to be used by a downstream model. For example, the downstream model may be trained to select and provide data to a user of the user device based on the embedding. In some embodiments, an artificial intelligence model may be trained using self-supervised learning to reconstruct real-time interactions of the user during a current user session. The artificial intelligence model, for example, an auto-encoder, may be trained using reference real-time interactions of the user to generate embeddings that can be mapped to predicted reconstructions of the real-time interactions. The artificial intelligence model can be trained by minimizing a loss computed from the reference real-time interactions and the reconstructions of the real-time interactions.

A method for generating cross-channel recommendations for a customer includes receiving customer data associated with the customer, content data, and clickstream data; encoding the content data using a text encoder, the encoding resulting in content embeddings; encoding the clickstream data using a clickstream encoder, the encoding resulting in clickstream embeddings; providing the content data, the clickstream data, and the clickstream embeddings as inputs to a hybrid latent model; causing execution of the hybrid latent model, the execution resulting in cross-system user and item interaction embeddings; retrieving application features corresponding to the clickstream data from an application feature store; providing the customer data, the content embeddings, the clickstream embeddings, the cross-system user and item interaction embeddings, and the application features as inputs to a recommendation model; causing execution of the recommendation model, the execution resulting in a plurality of ranked recommendations.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

The application discloses an advertisement click flow analysis and abnormality identification system and method, relates to the technical field of Internet advertisements, and comprises the following steps: acquiring daily advertisement click data in a preset time period in real time; comparing the daily advertisement click data in the preset time period with the daily advertisement click data in the same time period in historical data; and marking the advertisement click data whose fluctuation amplitude exceeds a preset threshold as abnormal data based on the comparison result, wherein the abnormal data comprises determined abnormal data and to-be-determined abnormal data, the determined abnormal data meets all abnormal feature judgment criteria, and the to-be-determined abnormal data only meets part of the abnormal feature judgment criteria. The application can quickly find abnormal click fluctuation by collecting data in real time and comparing the data with historical data, strictly distinguishes between determined abnormal data and to-be-determined abnormal data, and mines co-occurrence feature combinations by means of correlation analysis, which is beneficial to avoiding misjudgment and missing judgment and significantly improves the accuracy of abnormality identification.

1. The name of the design product: mode selection operation graphical user interface for electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: mode selection and parameter setting operation interface. 6. The change state description of the graphical user interface: the front view is the home page of the graphical user interface, clicking "mode setting" jumps to interface change state figure 1, clicking "flow rate mode" button in interface change state figure 1 can jump to interface change state figure 2; clicking the corresponding numbers of "flow rate" or "preset" jumps to interface change state figure 3 for setting, and after completion, clicking "confirm" enters interface change state figure 4. 7. Other circumstances that need to be explained: the text and / or numerical content shown in the front view and each interface change state figure are only for reference and can be replaced, which do not constitute a limitation on the protection scope of the design.

1. Name of the designed product: main body of medical information graphical user interface for electronic device. 2. Use of the designed product: for electronic device. 3. Design points of the designed product: the interface content of the solid line part in the graphical user interface. 4. Picture or photo best indicating the design points: design 1 front view. 5. No design points, omit other views of the design. 6. Design 1 is designated as the basic design. 7. Other circumstances needing explanation: use of the graphical user interface: for displaying medical information, for example. 8. Human-computer interaction mode of the graphical user interface: the user clicks the parameter label in the interface of the front view of each design, for example, clicks "Flow", "Leak" or "Pressure", to select the data of the parameter of interest to be displayed. 9. The part shown by the dotted line in each view is not within the protection.

1. Name of the designed product: main body of medical information graphical user interface for electronic device. 2. Use of the designed product: for electronic device. 3. Design points of the designed product: the interface content of the solid line part in the graphical user interface. 4. Picture or photo best indicating the design points: design 1 front view. 5. No design points, omit other views of the design. 6. Design 1 is designated as the basic design. 7. Other circumstances needing to be explained: the graphical user interface is used to display medical information, for example. 8. Human-computer interaction mode of the graphical user interface: the user clicks the time label in the interface of the front view of each design, for example, clicks "7 days", "30 days" or "Custom", to select the data of the date of interest to be displayed; and the user clicks the parameter label in the interface of the front view of each design, for example, clicks "Flow", "Leak" or "Pressure", to select the data of the parameter of interest to be displayed. 9. The part shown by the dotted line in each view is not within the protection.

A method for associating a device with a user including the action of receiving from a first computerized device operated by a first user a first data content including a plurality of first data items, including clickstream data. An action of setting rules for computing scores representing similarity between a first data item received from the first computerized device and a second data item received from a second computerized device. An action of collecting the plurality of the first data items and the second data items, from the plurality of computerized devices of the plurality of computerized devices. An action of computing the score representing similarity between at least one pair of computerized devices respectively providing the first and the second data items. And an action of determining that the pair of computerized devices are operated by a same user if the score reaches a predetermine value.

The invention relates to the technical field of artificial intelligence and man-machine interaction, and discloses a multi-modal index interpretability enhancement method and system. The method comprises the following steps: acquiring a voice instruction, a DOM (Document Object Model) structure snapshot and front-end burying point data of a user in a Web system operation process; executing cross-modal attention fusion by adopting a modal guide residual error alignment mechanism; constructing an explaining causal chain graph and carrying out deviation detection; semantic perturbation mapping and structured repair generation are executed; and outputting the multi-modal interpretation content set. Compared with the prior art, the technical problem that cross-modal causal chain construction and structured code repair suggestions cannot be realized in abnormal scenes of single-modal indexes, especially multi-modal user behavior data including click streams, form filling and voice command driving is solved. Due to the fact that the modal guide residual error alignment mechanism is introduced, the interpretation reachability and the code repair performability of the model prediction result in the Web system are improved.

The invention relates to the technical field of education, in particular to a generative course adaptation system and method for personalized teaching of children with cognitive impairment, which comprises a student end, a teacher end and a background server, adopts a three-layer architecture including an application layer, a business layer and a working layer, realizes a complete closed loop from data acquisition to content generation, and improves the teaching efficiency. The student end is provided with an intelligent page and a wearable sensor, the teacher end performs operation through a background server, the business layer comprises a multi-mode course adaptive recommendation system, the working layer comprises a course adaptive recommendation model, and the model comprises a cognitive evaluation model, a click stream course recommendation model, a learning progress recommendation model and a multi-mode course recommendation model. The multi-modal course recommendation model comprises a visual teaching content encoder, an auditory teaching content encoder and a teaching content controller, the system generates suitable teaching content by establishing teaching preference data, cognitive competence and a teaching characteristic matrix of students, and all the parts work cooperatively, so that the operation efficiency and stability are improved.

The present invention relates to the field of e-commerce technology, and specifically to a cloud computing-based e-commerce promotion method and system, comprising the following steps: constructing user behavior sequences through log collection, extracting mutation points through dynamic time warping, generating dynamic parameters through segmented integration, reconstructing weight sets through grey correlation analysis and calculation of differences, flexibly scheduling advertising resource distribution, and generating delivery instructions. In the present invention, by collecting user clickstream data, constructing a behavior sequence model, capturing the continuous dynamic characteristics of user browsing trajectories, analyzing temporal correlations, aligning the fluctuation peaks of user behavior paths, capturing interest mutation nodes, identifying abnormal behavior patterns, quantifying implicit preferences and explicit feedback parameters in the interaction process, establishing a correlation feature analysis mechanism, resetting advertising weight parameters, matching real-time user needs with delivery strategies, mapping weights to distributed advertising nodes, dynamically scheduling computing resources, adapting display needs and resource allocation, and forming a closed-loop optimization link.

1. The name of this design product: Graphical user interface with machine vision algorithm container for display screen panel. 2. Purpose of this design product: used for display interface. 3. The key design point of this appearance design product lies in the interface content. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Conventional design, omitting rear view, left view, right view, top view and bottom view. 6. Purpose of the graphical user interface: It is used to connect a series of tools through processes to complete the work. 7. Human-computer interaction mode of the graphical user interface: the main view is the main interface; in the main view, click "Process" to switch to the interface change state diagram 1; in the interface change state diagram 1, drag "Group" to the bottom of the main process to switch to the interface change state diagram 2; in the interface change state diagram 2, drag "IF" to the bottom of the main process to switch to the interface change state diagram 3; double-click "004-IF" in the interface change state diagram 3 to switch to the interface change state diagram 4; in the interface change state diagram 4, click "×" and then click the "+" icon on the right of "Process List" to switch to the interface change state diagram 5; double-click "Sub-process-1" in the interface change state diagram 5 to switch to the interface change state diagram 6; in the interface change state diagram 6, drag "Data-Numerical Calculation" to the right below "Sub-process-1" to switch to the interface change state diagram 7; double-click "002-Numerical Calculation" in the interface change state diagram 7 to switch to the interface change state diagram 8; click "Reference" in the interface change state diagram 8 to switch to the interface change state diagram 9; click "Add" in the interface change state diagram 9 to switch to the interface change state diagram 10; right-click in the interface change state diagram 10 Click "002-Numerical Calculation" to switch to the interface change state Figure 11; Click "Run Tool" to switch to the interface change state Figure 12; In the interface change state Figure 12, right-click "Sub-process-1" on the left and select "Modify Name" to switch to the interface change state Figure 13; In the interface change state Figure 13, click "OK" and drag the left "Sub-process-1" to the right below "Main Process" to switch to the interface change state Figure 14; In the interface change state Figure 14, click the second icon on the left of "Bmp" to switch to the interface change state Figure 15; In the interface change state Figure 15, click the "+" on the right of "Input" to switch to the interface change state Figure 16; In the interface change state Figure 16, enter the added information and click "×" to switch to the interface change state Figure 17; In the interface change state Figure 17, click the hexagon icon on the right of "005-Numerical Calculation" to switch to the interface change state Figure 18; In the interface change state Figure 18, click the drop-down menu on the right of "Input-1" to switch to the interface change state Figure 19; In the interface change state Figure 19, click "×" and change "VP Drag "2D Image Tool" to the bottom of the main process on the right to switch to the interface change state Figure 20; Double-click "007-VP Capture" in Interface Change State Figure 20 to switch to Interface Change State Figure 21; Click the second folder icon on the right side of "Mode" in Interface Change State Figure 21 to switch to Interface Change State Figure 22; Click the icon on the right side of "Folder" in Interface Change State Figure 22 to switch to Interface Change State Figure 23; Select an image in Interface Change State Figure 23 to switch to Interface Change State Figure 24; Click the triangle icon on the right side of "A0-007-VP Capture" in Interface Change State Figure 24 to switch to Interface Change State Figure 25; Click "×" in Interface Change State Figure 25 and click the second icon on the right side of "001-Process Start" to switch to Interface Change State Figure 26; Click the triangle icon in Interface Change State Figure 26 to switch to Interface Change State Figure 27;Click "Output" in the lower right corner of Figure 27 to switch to Figure 28. Click the three-small horizontal line icon in Figure 28 to switch to Figure 29. Click "Output" on the right side of "Design" in Figure 29 to switch to Figure 30. 8. Other situations that require explanation: This display panel is used for computers and industrial computers.

1. The name of the design product: the graphical user interface of the target association analysis interaction of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the product interface of design 1-design 7 is the interface for target correlation analysis display and interaction; the interface displays the target hierarchy and correlation through the organization target correlation analysis graph, completes detailed target collaborative analysis, and promotes enterprise strategy collaboration and landing; the main view interface of design 1 is the interactive interface of the target correlation analysis entrance; in the main view interface of design 1, click the target correlation analysis to enter the target data list under the corresponding organizational structure, and display design 1 interface change state diagram 1; in the design 1 interface change state diagram 1 interface, click "funds completion rate", and display design 1 interface change state diagram 2; in the design 1 interface change state diagram 2 interface, click "customer orientation" to switch, and display design 1 interface change state diagram 3; the main view interface of design 2 is the interactive interface of the target correlation analysis entrance; in the main view interface of design 2, click the target correlation analysis to enter the target data list under the corresponding organizational structure, and display design 2 interface change state diagram 1; in the design 2 interface change state diagram 1 interface, click "funds completion rate", and display design 2 interface change state diagram 2; in the design 2 interface change state diagram 2 interface, click the first organization target analysis icon from left to right in the upper right corner of the funds completion rate pop-up window, and display design 2 interface change state diagram 3; in the design 2 interface change state diagram 3 interface, click the first full-screen icon from right to left in the upper right corner of the funds completion rate pop-up window, and display design 2 interface change state diagram 4; the main view interface of design 3 is the interactive interface of the target correlation analysis entrance; in the main view interface of design 3, click the target correlation analysis to enter the target data list under the corresponding organizational structure, and display design 3 interface change state diagram 1; in the design 3 interface change state diagram 1 interface, click "funds completion rate", and display design 3 interface change state diagram 2; in the design 3 interface change state diagram 2 interface, touch the third add other cross-organizational structure icon from left to right in the upper right corner of the funds completion rate pop-up window, and display design 3 interface change state diagram 3; in the design 3 interface change state diagram 3 interface, click the icon, and display design 3 interface change state diagram 4; in the design 3 interface change state diagram 4 interface, check the added organizational structure and click the confirm button, and display design 3 interface change state diagram 5; in the design 3 interface change state diagram 5 interface, slide the middle interface to the bottom of the interface to the left, and display design 3 interface change state diagram 6-7; the main view interface of design 4 is the interactive interface of the target correlation analysis entrance; in the main view interface of design 4, click the target correlation analysis to enter the target data list under the corresponding organizational structure, and display design 4 interface change state diagram 1; in the design 4 interface change state diagram 1 interface, click "switch" in the upper left corner of the interface, and display design 4 interface change state diagram 2; in the design 4 interface change state diagram 2 interface, touch and click the administrative organization, and display design 4 interface change state diagram 3-4; in the design 4 interface change state diagram 4 interface, click "funds completion rate", and display design 4 interface change state diagram 5;The design 5 main view interface is an interactive interface of a target correlation analysis entrance; in the design 5 main view interface, a target correlation analysis is clicked to enter a target data list under a corresponding organizational structure, and a design 5 interface change state diagram 1 is displayed; in the design 5 interface change state diagram 1 interface, after a “switch” in the upper left corner of the interface is clicked, a design 5 interface change state diagram 2 is displayed; in the design 5 interface change state diagram 2 interface, after an administrative organization is touched and clicked, a design 5 interface change state diagram 3-4 is displayed; in the design 5 interface change state diagram 4 interface, after a switch to a “second audit committee” is clicked, a design 5 interface change state diagram 5 is displayed; in the design 5 interface change state diagram 5 interface, after “process optimization” is clicked, a design 5 interface change state diagram 6 is displayed; the design 6 main view interface is an interactive interface of a target correlation analysis entrance; in the design 6 main view interface, a target correlation analysis is clicked to enter a target data list under a corresponding organizational structure, and a design 6 interface change state diagram 1 is displayed; in the design 6 interface change state diagram 1 interface, after a “switch” in the upper left corner of the interface is clicked, a design 6 interface change state diagram 2 is displayed; in the design 6 interface change state diagram 2 interface, after an AI project group is touched and clicked, a design 6 interface change state diagram 3-4 is displayed; in the design 6 interface change state diagram 4 interface, after “funds completion rate” is clicked, a design 6 interface change state diagram 5 is displayed; the design 7 main view interface is an interactive interface of a target correlation analysis entrance; in the design 7 main view interface, a target correlation analysis is clicked to enter a target data list under a corresponding organizational structure, and a design 7 interface change state diagram 1 is displayed; in the design 7 interface change state diagram 1 interface, after a “switch” in the upper left corner of the interface is clicked, a design 7 interface change state diagram 2 is displayed; in the design 7 interface change state diagram 2 interface, after an administrative organization is touched and clicked, a design 7 interface change state diagram 3-4 is displayed; in the design 7 interface change state diagram 4 interface, after a switch to a “financial AI project group” is clicked, a design 7 interface change state diagram 5 is displayed; in the design 7 interface change state diagram 5 interface, after “communication ability” is clicked, a design 7 interface change state diagram 6 is displayed.

User selections entered in the media application or any user input device behavior with user devices May be recorded as clickstream data. The clickstream data may be used to deduce information about the user or a media item being consumed. A user may be profiled based on his or her input device behavior using a plurality of time-stamped indicators. A degree of user interest may be determined based on a time period between the time-stamped indicators, the number of time-stamped indicators within a period of time, and / or a type of user action.