TW201938993A - Operation path navigation - Google Patents

Abstract

Provided in an embodiment of the present description are a method and a device for user operation path navigation. The method comprises: first acquiring an operation sequence and user feature data of a current user, and then employing a path navigation model to predict a target operation page of the current user on the basis of the operation sequence and the user feature data; next, recommending the target operation page to the current user; determining a modification to the path navigation model depending on whether the current user accepts the recommended target operation page. As such, the present invention helps a user to quickly reach a target operation page.

Description

Method and device for operating path navigation

One or more embodiments of the present specification relate to the technical field of computers, and in particular, to methods and devices for operating path navigation.

With the development of Internet technology, people often use a variety of client software, such as mobile phone APPs, for various transactions, including shopping, payment, social networking, and financial management. This makes the interaction between the client software and the user very important. Complex client-side software has many functions. If the interaction design is not reasonable enough, users cannot reach and perform target operations in a simple and fast way, which will reduce the user experience and reduce the service capabilities of the product. In particular, for some operations that require more timeliness, such as funds and stock trading operations in some wealth management software products, if the target operation cannot be performed quickly, it may even cause the user's property loss. And, in another aspect, different users have different operating habits and demands. This puts forward higher requirements for user interaction design of client software.
Therefore, it is hoped that there can be improved solutions to help users perform target operations quickly and efficiently through effective operation path navigation.

One or more embodiments of the present specification describe a method and a device that help a user quickly perform a target operation by navigating through an operation path.
According to a first aspect, a user-operated path navigation method is provided, which includes: obtaining a current user's operation sequence and user characteristic data; and adopting a path navigation model based on the operation sequence and user characteristic data to predict all The target operation page of the current user is described; the target operation page is recommended to the current user; and the modification of the route navigation model is determined according to whether the current user accepts the recommended target operation page.
In one embodiment, the above-mentioned path navigation model is configured to establish a correspondence relationship between a plurality of user groups and an operation mode based on a plurality of user historical operation data sets and user characteristic data sets.
According to a possible design, the target user's target operation page is predicted in the following ways: determining a specific user group corresponding to the current user according to the user characteristic data; and multiple user groups established according to a path navigation model Correspondence with the operation mode to determine a specific operation mode corresponding to a specific user group; according to the specific operation mode, determine a specific operation path that matches the operation sequence of the current user; and determine the specific operation path according to the specific operation path Describe the target operation page of the current user.
According to one possible design, the target operation page is provided in one of the following ways:
Providing a floating layer icon on the original interface, the floating layer icon pointing to the target operation page;
Adding an icon to the interface, the added icon pointing to the target operation page;
Providing the target operation page as the next operation page to the current user.
According to an embodiment, determining the modification to the route navigation model may include: determining a recommendation accuracy of the route navigation model according to whether the current user accepts the recommended target operation page; and when the recommendation accuracy is lower than a preset threshold In the case, the route navigation model is modified.
Further, in one embodiment, modifying the route navigation model includes: recording subsequent operations of the current user; forming a target operation path according to the operation sequence and the subsequent operations; and according to the target operation path, Update the correspondence between multiple user groups and operation modes established by the route navigation model.
According to a second aspect, a device for user-guided path navigation is provided, including: an acquisition unit configured to acquire a current user's operation sequence and user characteristic data; a prediction unit configured to adopt a path navigation model based on The operation sequence and user characteristic data to predict a target operation page of the current user; a recommendation unit configured to recommend the target operation page to the current user; and a determination unit configured to be based on whether the current user accepts The recommended target operation page determines the modification of the route navigation model.
According to a third aspect, a computer-readable storage medium is provided on which a computer program is stored, and when the computer program is executed in the computer, the method for causing the computer to execute the first aspect.
According to a fourth aspect, a computing device is provided, including a memory and a processor, wherein the memory stores executable code, and the processor implements the first state when the processor executes the executable code. Kind of method.
Through the methods and devices provided by the embodiments of the present specification, a path navigation model is used to recommend a target operation page to the user, and the model is continuously trained and improved according to user feedback, so that the model's recommendation accuracy is continuously improved, thereby helping to a greater extent Users quickly reach the target page, simplifying user operations and improving user experience.

The solutions provided in this specification are described below with reference to the drawings.
FIG. 1 is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification. As shown in the figure, each user can form user historical operation information by using client software, such as Alipay App, Ant Wealth App, etc. A computing platform, such as the server of the above-mentioned client software, can build a path navigation model based on these user historical operation information and user characteristic information. The route navigation model obtains the usage habits of various users by analyzing the historical operation information and user characteristic information. Then, while using the route navigation model for user-guided route navigation, the model can be tested and trained. When the operation sequence in which the user A enters the client software for operation is obtained, the captured operation sequence and the user characteristic information of the user A are input into the path navigation model. The route navigation model predicts the next target operation page of the user A based on the previous analysis of various user usage habits. Then, the computing platform recommends the target operation page to the user. Then, according to whether the user A has selected the recommended target operation, the prediction accuracy of the target operation is judged, and the prediction accuracy is used as a feedback to modify the route navigation model. Through such a process, while using the model to recommend the target operation to the user, the model is modified according to the user's selection as a feedback result, so as to continuously improve the accuracy of the model recommendation and better recommend the target operation for the user. Help users reach their target operations efficiently. The specific implementation steps of the above process are described below.
According to an embodiment described in this description, in a first stage, a route navigation model is established for a user historical operation data set and a user characteristic information data set. In the modeling phase, at least two kinds of information should be obtained: the user historical operation information data set and the user characteristic information data set. A path navigation model is established for the two kinds of information, and the model is used to analyze and learn the historical user operation information and user characteristic information to obtain the operation habits of various users.
The user history operation information includes the user history operation track, or the history operation path. In order to obtain such information, generally, when a user performs an operation on a terminal interface, the user's operation behavior and related pages are captured. The user's operation behavior may include various operations performed by the user on the page, such as clicking, sliding, dragging, typing characters, zooming in or out, and so on. The above pages may be web pages, web application pages, App pages, or other pages that can interact with users. The user's operation can be captured by burying points in the terminal application. For example, each interactive module in the page, such as a button, a scroll bar, a banner (banner advertisement), and the like is numbered in advance to facilitate recording of user operations.
FIG. 2 is a schematic diagram showing a user's operation path in one example. In FIG. 2, Alipay is used as an example to show the user's operation on the page. As shown in FIG. 2, the user starts from the main Alipay page A1 and clicks “Ant Wealth” (perform operation P1) to enter the page A2. On this A2 page, click "Finance" (perform operation P2) to enter the wealth management page A3. Various financial management items are further displayed on the A3 page. Assume that the user clicks "funds" on the A3 page (performs operation P3), thereby entering a page A4, which shows various fund choices. In this page A4, the user clicks on "Fund Ranking" (perform operation P4), and then enters page A5. On this page A5, by clicking "valuation ranking" (perform operation P5), page A6 is displayed. After the user browses the content of page A6, the client is closed. In this way, A1.P1-A2.P2-A3.P3-A4.P4-A5.P5-A6 shows a user's operation path OP1.
In one embodiment, while capturing user operations, related time information is also recorded. In this way, the user's historical operation information also includes time parameter information such as operation time and dwell time. For example, for the operation path OP1 shown in FIG. 3: A1.P1-A2.P2-A3.P3-A4.P4-A5.P5-A6, it is also possible to record that the user performs each operation P1-P2-P3-P4- The time of P5, such as T1-T2-T3-T4-T5, and the user's time on each page, such as ST1-ST2-ST3-ST4-ST5-ST6. Information such as the time interval of the operation can also be determined based on the operation time and / or dwell time.
In one embodiment, the above historical operation information of the user may be recorded in various forms such as a sequence, a vector, and a matrix. For example, in one example, an operation path is recorded as a vector, and each vector element corresponds to the number of an interactive module targeted by a user operation. In one example, the operation path and the time information corresponding to the operation path are arranged into sequences of the same length, and these sequences are arranged into a matrix form. Understandably, the user's historical operation information can also be recorded in other formats or forms.
In addition to the user's historical operation information, in another aspect, the user's characteristic information is also obtained. In one embodiment, the user characteristic information includes basic attribute information of the user, such as attribute information included in the registration information, such as age, gender, region, and the like. In one embodiment, the user characteristic information further includes other attribute information of the user related to the client software function. For example, for financial client software, such as Alipay and Ant Wealth, information such as the user's balance, total fund value, and transaction frequency can also be obtained as its characteristic information.
In one embodiment, the user characteristic information includes user portrait information. User portrait information is a labeled user model that is abstracted based on the user's basic attribute data. There are already some methods based on the user's basic attribute data (such as registration information, consumption records, etc.) to give users some tags, and the collection of these tags constitutes user portrait information. For example, in a specific example, the user portrait information includes: "registered newcomer", "financial banker", "saving expert", "medium anti-risk level", "full-time mom" and other label information, including "registered newcomer" The tags of are determined according to the registration time in the user registration information, and the tags about wealth management, savings, and anti-risk ability are comprehensively determined based on the user's information such as balance, wealth management income, savings level, consumption record and other information.
In one embodiment, the basic attribute data of the user is obtained, and then the above method is used to determine the user portrait information based on the basic attribute data. In another embodiment, the client software has drawn a user portrait for the user for other functional purposes. In such a case, such user portrait information is directly acquired as the user characteristic information.
It can be understood that in order to analyze the operating habits of various user groups, it is necessary to obtain a large amount of historical operation information and user characteristic information of the users. A path navigation model is established for the two kinds of information, and the model is used to analyze and learn the historical user operation information and user characteristic information to obtain the operation habits of various users.
According to one embodiment, a clustering algorithm is used in the above-mentioned path navigation model, so as to classify and analyze a large amount of user information and improve the rapid regression of the model. For example, user characteristic information may be clustered, so that a large number of users are divided into multiple user groups according to a certain granularity, so as to construct an operation behavior model for each user group. It is also possible to perform cluster analysis on the user's historical operation information, thereby processing complex user operation behaviors into some operation behavior modes.
In the clustering process, direct clustering methods such as K-means, hierarchical clustering algorithms such as BIRCH algorithm, CURE algorithm, etc., and density-based clustering algorithms such as DBSCAN algorithm, OPTICS algorithms, etc., and other possible clustering algorithms.
According to an embodiment, the above path navigation model may adopt a neural network algorithm and structure, such as a convolutional neural network CNN, a recurrent neural network RNN, and the like. The path navigation model can use the above-mentioned neural network algorithm and structure to perform deep learning on input data, that is, user historical operation information and user characteristic information of a large number of users.
Through the use of various algorithms, the path navigation model is based on a large amount of user historical operation information and user characteristic information, and analyzes and learns the operating habits of each user group, that is, establishes the correspondence between multiple user groups and operation modes. This analysis and learning process may specifically include counting the frequency and operating time of various historical operating paths, forming a variety of operating modes based on historical operating paths and operating time parameters, and counting the correspondence between user groups and operating modes, and thus for each use Group of people to build operational behavior models. More specifically, the user group may be a group set according to various granularities. For example, in one example, the division of the user group may correspond to the crowd label included in the user ’s portrait; in another example, The division of the user group may correspond to the combination of various crowd tags in the user portrait. For example, the user group 1 corresponds to users who have tags such as "financial banker" and "saving expert", and the user group 2 corresponds to At the same time users with "financial master", "stock market entry" tags. In yet another example, the division of user groups may be based on independent division rules. The granularity of user groups can be set according to business requirements. In one example, a user group can even contain only one user.
In another aspect, the operation mode may include a historical operation path whose frequency exceeds a certain threshold. Further, the operation mode may further include time-related parameters corresponding to the historical operation path, such as operation time, dwell time, and the like.
In a specific example, the user user1 frequently (for example, exceeds a threshold, 10 times) executes the operation path OP1 shown in FIG. 2: A1.P1-A2.P2-A3.P3-A4.P4-A5. P5-A6, and another operating path OP2: A1.P1-A2.P2-A3.P7-A8.P8-A9.P9-A10. Such information is recorded as historical operation information. The route navigation model first analyzes the characteristic information of the user user1, and divides the user user1 into the user group G1. In addition, the path navigation model can find the high-frequency operation paths OP1 and OP2 of the user user1 by analyzing historical operation information, especially by counting the frequency of occurrence of various operation paths. Such a high-frequency operation path can form an operation mode M1. More specifically, by analyzing the operation time of the user user1, the path navigation model can be found that the user user1 performs the above-mentioned operation path OP1 five times at about 10 o'clock every day, and the above-mentioned operation path OP2 is performed 6 times at about noon on average every day The above-mentioned operation path OP1 is performed on average 7 times a day around 11 pm. Based on such information, the route navigation model can establish the correspondence between the user group G1 to which the user user1 belongs and the operation mode M1, which includes the above-mentioned high-frequency operation paths OP1 and OP2. Further, in one embodiment, the operation mode M1 may further include time-related parameters of each operation path, such as a high-frequency execution period (OP1: 10:00, 23:00; OP2: 12:00), a dwell time, and the like.
In this way, the route navigation model establishes a correspondence relationship between multiple user groups and operation modes based on a large number of historical operation data sets and user characteristic data sets of users. The path navigation model constructed in this way can be used for path prediction and navigation for user operations. FIG. 3 shows a flowchart of a method for user-guided route navigation according to an embodiment. As shown in FIG. 3, the method flow includes the following steps: step 31, obtaining the operation sequence and user characteristic data of the current user; step 32, using a path navigation model, predicting the location based on the operation sequence and user characteristic data The target operation page of the current user is described; step 33, the target operation page is recommended to the current user; step 34, the modification of the path navigation model is determined according to whether the current user accepts the recommended target operation page. . The specific execution process of the above steps is described below.
First, in step 31, an operation sequence and user characteristic data of a current user are acquired. In order to distinguish from the description of the foregoing modeling phase, in the following example, a specific user and user A are used as the current user. However, it can be understood that the current user A may be a historical user included in the historical data set based on the path navigation model modeling stage, or a new user not included in the historical data set.
Similar to the acquisition of historical operation information, the operation sequence SA of the user A can be captured by way of embedding a page. The operation sequence SA may include only one operation or a plurality of consecutive operations. These operations can include clicking, swiping, dragging, typing characters, zooming in or out, etc. on interactive modules in the page. In a specific example, it is captured in step 31 that the current user A starts from Alipay main page A1, clicks "Ant Wealth" (perform operation P1), enters page A2, and then on the A2 page, clicks "finance" Operation P2), enter the wealth management page A3. The operation sequence SA is thus formed: A1.P1-A2.P2-A3. In another example, the user A continues the operation, and the operation sequence SA is updated as: A1.P1-A2.P2-A3.P3-A4.
In another aspect, the user characteristic data of the current user A is also obtained.
In one embodiment, the attribute data of the current user A is obtained as the user characteristic data. The attribute data may include data in the registration information, such as age, gender, registration time, etc., and other information related to the main functions of the user terminal. Attribute data, such as the balance and income in the wealth management client.
In another embodiment, the obtained attribute data of the user A is converted into user portrait data as user characteristic data. Specifically, first obtain the attribute values (such as age, registration time, balance, income, etc.) of user A's attributes, and determine the population tags (such as "registered newcomer", "financial management" White "," saving expert ", etc.), according to the crowd tag, the portrait data of the user A is determined as the user characteristic data.
In yet another embodiment, the client software has drawn user portraits for various users for other functional purposes. In such a case, the portrait data of the current user A is directly acquired as its user characteristic information.
Next, in step 32, a path navigation model is used to predict the target user's target operation page based on the captured user A's operation sequence and user characteristic data. As mentioned above, a route navigation model has been constructed, which is based on the historical operation data set and user characteristic data set of multiple users, and establishes the correspondence between multiple user groups and operation modes. In this way, such a path navigation model can be used to predict the target operation page of the current user A based on the established correspondence relationship.
FIG. 4 shows a flowchart of a prediction target operation page, that is, a sub-step of step 32 according to an embodiment. As shown in FIG. 4, in step 321, a specific user group corresponding to the current user is determined according to the user characteristic data of the current user.
Specifically, for the current user A, if the historical data set based on the path navigation model modeling stage already includes the user (for example, user A is actually equal to the aforementioned user user1), then the user can directly The user group is divided to determine the user group to which the user A belongs, that is, the user group G1.
If the historical data set does not include the user A, the user group to which the user A belongs may be determined based on the user characteristic data of the user A and the user group division rules in the modeling stage of the route navigation model. For example, the division of the user group in the modeling phase may correspond to the crowd tags included in the user portrait, or may correspond to a combination of various crowd tags in the user portrait. In such a case, first obtain user portrait data of user A based on user characteristic data of user A, thereby obtaining a crowd label of user A, and determine the usage to which user A belongs according to these crowd labels and user group division rules. Person group. In other examples, the modeling phase may also be based on other user group division rules. It is assumed here that it is determined that the user A belongs to the user group G1 according to the user characteristic data of the user A and the user group division rule.
After the specific user group to which the user A belongs is determined, in step 322, a specific operation mode corresponding to the specific user group is determined according to the correspondence relationship between the multiple user groups and the operation modes established by the route navigation model. .
As mentioned above, the route navigation model establishes the correspondence between multiple user groups and operation modes by analyzing and learning historical user data sets and user characteristic data sets of a large number of users. After determining the user group G1 to which the user A belongs in step 321, the operation mode M1 corresponding to the user group G1 can be determined through the established correspondence relationship.
Next, in step 323, a specific operation path matching the operation sequence of the current user is determined according to the specific operation mode. Generally, the operation mode may include a historical operation path whose frequency exceeds a certain threshold. Further, the operation mode may further include time-related parameters corresponding to the historical operation path, such as operation time, dwell time, and the like. In one embodiment, in step 323, the historical operation path included in the specific operation mode is obtained, and an operation path matching the operation sequence of the current user is determined as the specific operation path. The operation path matching the operation sequence of the current user may be an operation path including the operation sequence. In one embodiment, time-related parameters of each historical operation path included in the operation mode may be further considered. For example, in a case where multiple historical operation paths in a specific operation mode all include the current operation sequence, the current operation time of the current operation sequence and the historical execution time of each historical operation path recorded in the operation mode may be determined, and the historical The historical operation path whose execution time is closest to the current operation time is determined as the specific operation path that matches the current operation sequence.
For the user group G1 in the foregoing example, the corresponding operation mode M1 includes at least the historical high-frequency operation path OP1: A1.P1-A2.P2-A3.P3-A4.P4-A5.P5-A6, and OP2: A1.P1-A2.P2-A3.P7-A8.P8-A9.P9-A10.
In one example, it is assumed that the current operation sequence SA of the user A includes, A1.P1-A2.P2-A3.P3-A4. By comparing the elements of the current operation sequence with the historical operation path, it can be seen that the historical operation path OP1 contains the current operation sequence SA, so OP1 can be used as a specific operation path that matches the SA.
In another example, it is assumed that the current operation sequence SA of the user A includes A1.P1-A2.P2-A3. It can be seen that the historical operation paths OP1 and OP2 included in the operation mode M1 both include the current operation sequence SA, so both OP1 and OP2 can be used as alternative operation paths. Further, reference may be made to time-related parameters in the operation mode M1, such as a high-frequency execution period. It is assumed that the operation time of the current operation sequence SA is 10:05, and the historical execution period recorded in the operation mode M1 includes: OP1: 10: 00 and 23:00, and OP2: 12: 00. The current operation time of 10:05 is closer to the high-frequency execution time of the historical operation path OP1. Therefore, OP1 is determined as a specific operation path that matches the current operation sequence SA.
Next, in step 324, a target operation page of the current user is determined according to the specific operation path determined above. Specifically, the end page in the specific operation path is used as the target operation page of the current user. In the foregoing example, assuming that OP1 is determined to be a specific operation path, the end page A6 of OP1 (the page corresponding to the “evaluation ranking” in FIG. 2) can be used as the target operation page of user A.
In this way, in the manner shown in FIG. 4, in step 32, a route navigation model is used to predict the target operation page of the current user A. Returning to FIG. 3, next, at step 33, the determined target operation page is recommended to the current user.
In one embodiment, the determined target operation page is provided to the current user as the next operation page. For example, it is assumed that the specific operation path has been determined to be OP1, the end page A6 of OP1 is the target operation page of user A, and the current operation page of user A is A3. If the operation performed by user A on page A3, for example, clicking "fund" (perform operation P3) on page A3, continues to conform to the specific operation path OP1, then skip the intermediate pages A4 and A5 and directly display the target operation for user A Page A6.
In one embodiment, in order to avoid disturbing the operation of the user, an icon is added to the user interface, and the newly added icon points to the target operation page. FIG. 5 shows a schematic diagram of adding an icon according to an embodiment. As shown in FIG. 5, an icon “Quick” is added to the operation icon in the bottom row of the “Alipay” client terminal, and the icon points to the target operation page. Assume that the page A6 is determined as the target operation page of the user A. In the case of FIG. 5, during the current operation sequence, the user A can directly jump to the page A6 by clicking the icon “Quick”, eliminating the intermediate Pages, such as A4 and A5.
In another embodiment, in order to further reduce the modification of the user interface, a floating layer icon is provided on the original interface, and the floating layer icon points to the target operation page. FIG. 6 shows a schematic diagram of a floating layer icon according to one embodiment. As shown in FIG. 6, on the basis of the original interface of "Alipay", a floating layer icon "Understand what you want" is added, and the icon points to the target operation page. In one example, the floating layer icon can be designed to be translucent to minimize the display of the original interface. In the foregoing example, assuming page A6 is determined as the target operation page of user A, in the case of FIG. 7, user A can click on the floating layer icon "understand you" under any page of the current operation sequence What you want "jumps directly to page A6, eliminating the intermediate page.
It can be understood that the determined target operation page can also be recommended to the user through other methods, such as a push method.
By recommending the target operation page to the user, the user can help the user perform the target operation quickly and efficiently, reach the target page, and improve the user experience.
At the same time, according to the user's acceptance of the recommended target page as feedback, the route navigation model can be further trained and modified to make it more consistent with the user's usage habits. That is, in step 34, the modification of the route navigation model is determined according to whether the current user accepts the recommended target operation page.
In one embodiment, it is first determined whether the current user has accepted the recommended target operation page. In the foregoing example, the target operation page A6 is recommended to the user A in various ways. If user A performs the exit operation within a short predetermined time (for example, 1s) after entering the recommended target operation page A6, for example, by clicking other icons to jump out of the page, or operating the return key to exit the page, or closing The entire client software, then it can be considered that User A has not selected or accepted the recommended target operation page. If user A stays for a reasonable time after performing the recommended target operation page, or performs operations such as browsing and further interaction, then user A can be considered to have selected or accepted the recommended target operation page.
According to whether the current user accepts the recommended target operation page, the recommendation accuracy of the route navigation model can be determined. The recommendation accuracy can be statistically calculated based on the feedback received by multiple current users on the recommended target operation page. For example, after recommending the target operation page to 100 users, assuming that 40 users have accepted the recommendation, the recommendation accuracy can be considered to be 40%. For each current user, the accuracy of the recommendation can be accumulated according to their acceptance of the recommendation, thereby dynamically updating the recommendation accuracy. For example, it is assumed that the current user is user A and has previously recommended the target operation page to 20 users, of which 9 users have accepted the recommendation, and the recommendation accuracy is 9/20. If the current user A accepts the recommended target operation page, the recommendation accuracy can be updated to 10/21; if the user A does not accept the recommended target operation page, the recommendation accuracy can be updated to 9/21.
In a case where the recommendation accuracy is lower than a preset threshold, the route navigation model may be modified. More specifically, a subsequent operation of the current user, that is, an operation after exiting from the recommended target operation page may be recorded. It can be understood that such subsequent operations are operations that reflect the actual goals of the current user. According to the previous operation sequence of the current user and the above-mentioned subsequent operations, a target operation path is formed, and then according to the target operation path, the correspondence relationship between multiple user groups and operation modes established by the path navigation model is updated. More specifically, in one embodiment, the target operation path is added to the historical operation data set to form an updated historical operation data set, and the path navigation model is re-analyzed and learned for the updated historical operation data set, thereby re-establishing Correspondence between user groups and operation modes. In another embodiment, according to the target operation path, the operation mode (for example, operation mode M1) of the user group (for example, user group G1) to which the current user (for example, user A) belongs is updated. Specifically, the target operation path is used as a newly added historical operation path, and statistics on the historical operation path are updated for a user group to which the current user belongs, thereby updating the content of the operation mode corresponding to the user group.
It can be understood that some conventional algorithms in the model training process, such as gradient transfer, can also be used to modify other parameters of the model to make the model return better and improve the recommendation accuracy based on user feedback.
In this way, closed-loop forward feedback is achieved through the path navigation model. While recommending the target operation page to the user, the model is continuously trained and improved, so that the recommendation accuracy of the model is continuously improved, thereby helping the user to reach the target page quickly, simplifying the user operation, and improving the user experience.
According to another aspect of the embodiment, a device for user-guided route navigation is also provided. FIG. 7 shows a schematic block diagram of an operation path navigation device according to an embodiment. As shown in FIG. 7, the navigation device 700 includes: an obtaining unit 71 configured to obtain an operation sequence and user characteristic data of a current user; and a prediction unit 72 configured to adopt a path navigation model based on the operation sequence and the user Characteristic data to predict the target operation page of the current user; a recommendation unit 73 configured to recommend the target operation page to the current user; and a determination unit 74 configured to determine whether the current user accepts the recommended target Operate the page to determine the modification to the route navigation model.
In one embodiment, the obtaining unit 71 is configured to obtain an attribute value of at least one attribute of the current user, determine at least one crowd tag to which the current user belongs according to the attribute value of the at least one attribute, and The at least one crowd tag determines the portrait data of the current user as the user characteristic data.
According to an embodiment, the above-mentioned path navigation model is constructed to establish a correspondence relationship between a plurality of user groups and an operation mode based on a plurality of users' historical operation data sets and user characteristic data sets.
In one embodiment, the prediction unit 72 includes (not shown): a first determination module configured to determine a specific user group corresponding to the current user according to the user characteristic data; a second determination module A group configured to determine a specific operation mode corresponding to the specific user group according to the correspondence between the plurality of user groups and the operation mode; a third determination module configured to determine the corresponding operation mode according to the specific operation mode The specific operation path matching the operation sequence of the current user is described; and a fourth determination module is configured to determine the target operation page of the current user according to the specific operation path.
Further, in one embodiment, the third determining module is configured to determine a plurality of historical operation paths including the operation sequence from the specific operation mode, determine a current operation time of the operation sequence, and Historical execution time of each of the plurality of historical operation paths; determining a historical operation path whose historical execution time is closest to the current operation time as the specific operation path.
According to one embodiment, the recommendation unit 73 is configured to provide the target operation page through one of the following methods: providing a floating layer icon on the original interface, the floating layer icon pointing to the target operation page; adding a new interface to the interface Icon, the newly added icon points to the target operation page; the target operation page is provided as the next operation page to the current user.
In one embodiment, the above-mentioned determining unit 74 includes (not shown): an accuracy determining module configured to determine the recommended accuracy of the route navigation model according to whether the current user accepts the recommended target operation page; modify A module configured to modify the route navigation model when the recommended accuracy is lower than a preset threshold.
Further, in one embodiment, the modification module is configured to: record subsequent operations of the current user; form a target operation path according to the operation sequence and the subsequent operations; and according to the target operation path, Update the correspondence between multiple user groups and operation modes established by the route navigation model. Furthermore, in an embodiment, the modification module is further configured to: use the target operation path as a newly added historical operation path, and update statistics of historical operation paths for a specific user group to which the current user belongs. ; Update the operation mode corresponding to the specific user group according to the updated historical operation path statistics.
Through the above devices, the path navigation model is used to recommend the target operation page to the user, and the model is continuously trained and improved according to user feedback, so that the model's recommendation accuracy is continuously improved, thereby helping users to quickly reach the target page. , Simplifying user operations and improving user experience.
According to another aspect of the embodiment, a computer-readable storage medium is further provided, on which a computer program is stored, and when the computer program is executed in the computer, the computer is caused to execute the method described in conjunction with FIG. 3.
According to yet another aspect of the embodiment, a computing device is further provided, which includes a memory and a processor. The memory stores executable code. When the processor executes the executable code, the combination is implemented in combination with FIG. 3. The method described.
Those skilled in the art should appreciate that, in one or more of the above examples, the functions described in the present invention may be implemented by hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.
The specific embodiments described above further describe the objectives, technical solutions, and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. The scope of protection, any modification, equivalent replacement, or improvement made on the basis of the technical solution of the present invention shall be included in the scope of protection of the present invention.

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In order to explain the technical solution of the embodiment of the present invention more clearly, the drawings used in the description of the embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. Those of ordinary skill in the art can obtain other drawings according to the drawings without paying creative labor.

FIG. 1 is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification; FIG.

FIG. 2 shows a schematic diagram of an operation path of a user in an example; FIG.

3 shows a flowchart of a method for user-guided path navigation according to an embodiment;

4 shows a flowchart of a prediction target operation page according to an embodiment;

FIG. 5 illustrates a schematic diagram of adding an icon according to an embodiment; FIG.

Figure 6 shows a schematic diagram of a floating layer icon according to one embodiment; and

FIG. 7 shows a schematic block diagram of an operation path navigation device according to an embodiment.

Claims (20)

A method for user to operate path navigation includes: Obtain the operation sequence and user characteristic data of the current user; Using a path navigation model to predict a target operation page of the current user based on the operation sequence and user characteristic data; Recommend the target operation page to the current user; and The modification of the route navigation model is determined according to whether the current user accepts the recommended target operation page. The method according to claim 1, wherein obtaining the user characteristic data comprises obtaining an attribute value of at least one attribute of the current user, and determining an attribute value of the current user according to the attribute value of the at least one attribute. At least one crowd tag, and the portrait data of the current user is determined as the user feature data according to the at least one crowd tag. The method according to claim 1, wherein the route navigation model is configured to establish a correspondence relationship between a plurality of user groups and an operation mode based on historical user data sets and user characteristic data sets of multiple users. The method according to claim 3, wherein the predicting a target operation page of the current user includes: Determining a specific user group corresponding to the current user according to the user characteristic data; Determining a specific operation mode corresponding to the specific user group according to the correspondence between the multiple user groups and the operation mode; Determining a specific operation path matching the operation sequence of the current user according to the specific operation mode; and Determining a target operation page of the current user according to the specific operation path. The method according to claim 4, wherein determining a specific operation path that matches the operation sequence of the current user according to the specific operation mode includes: Determining, from the specific operation mode, a plurality of historical operation paths including the operation sequence; Determining a current operation time of the operation sequence, and a historical execution time of each of the plurality of historical operation paths; and A historical operation path whose historical execution time is closest to the current operation time is determined as the specific operation path. The method according to claim 1, wherein recommending the target operation page to the current user includes providing the target operation page in one of the following ways: Providing a floating layer icon on the original interface, the floating layer icon pointing to the target operation page; Adding an icon to the interface, the added icon pointing to the target operation page; and Providing the target operation page as the next operation page to the current user. The method according to claim 3, wherein determining the modification of the route navigation model according to whether the current user accepts the recommended target operation page includes: Determining the recommendation accuracy of the route navigation model according to whether the current user accepts the recommended target operation page; and When the recommendation accuracy is lower than a preset threshold, the route navigation model is modified. The method according to claim 7, wherein modifying the route navigation model includes: Recording subsequent operations of the current user; Forming a target operation path based on the operation sequence and the subsequent operations; and According to the target operation path, the correspondence relationship between a plurality of user groups and operation modes established by the path navigation model is updated. The method according to claim 8, wherein updating a correspondence relationship between a plurality of user groups and operation modes established by the route navigation model includes: Using the target operation path as a newly added historical operation path, and updating statistics of the historical operation path for a specific user group to which the current user belongs; and Update the operation mode corresponding to the specific user group according to the updated historical operation path statistics. A device for user-operated path navigation includes: An obtaining unit configured to obtain an operation sequence and user characteristic data of a current user; A prediction unit configured to use a path navigation model to predict a target operation page of the current user based on the operation sequence and user characteristic data; A recommendation unit configured to recommend the target operation page to the current user; and The determining unit is configured to determine a modification to the route navigation model according to whether the current user accepts the recommended target operation page. The device according to claim 10, wherein the obtaining unit is configured to obtain an attribute value of at least one attribute of the current user, and determine at least one to which the current user belongs according to the attribute value of the at least one attribute A crowd tag, and the portrait data of the current user is determined as the user feature data according to the at least one crowd tag. The device according to claim 10, wherein the route navigation model is configured to establish a correspondence relationship between a plurality of user groups and an operation mode based on historical user data sets and user characteristic data sets of a plurality of users. The apparatus according to claim 12, wherein the prediction unit includes: A first determining module configured to determine a specific user group corresponding to the current user according to the user characteristic data; A second determining module configured to determine a specific operation mode corresponding to the specific user group according to the correspondence between the multiple user groups and the operation mode; A third determining module configured to determine a specific operation path matching the operation sequence of the current user according to the specific operation mode; and A fourth determining module is configured to determine a target operation page of the current user according to the specific operation path. The apparatus according to claim 13, wherein the third determining module is configured as: Determining, from the specific operation mode, a plurality of historical operation paths including the operation sequence; Determining a current operation time of the operation sequence, and a historical execution time of each of the plurality of historical operation paths; and A historical operation path whose historical execution time is closest to the current operation time is determined as the specific operation path. The device according to claim 10, wherein the recommendation unit is configured to provide the target operation page in one of the following ways: Providing a floating layer icon on the original interface, the floating layer icon pointing to the target operation page; Adding an icon to the interface, the added icon pointing to the target operation page; and Providing the target operation page as the next operation page to the current user. The apparatus according to claim 12, wherein the determining unit includes: An accuracy determination module configured to determine a recommendation accuracy of the route navigation model according to whether the current user accepts the recommended target operation page; and The modification module is configured to modify the route navigation model when the recommended accuracy is lower than a preset threshold. The device according to claim 16, wherein the modification module is configured as: Recording subsequent operations of the current user; Forming a target operation path based on the operation sequence and the subsequent operations; and According to the target operation path, the correspondence relationship between a plurality of user groups and operation modes established by the path navigation model is updated. The device according to claim 17, wherein the modification module is further configured to: Using the target operation path as a newly added historical operation path, and updating statistics of the historical operation path for a specific user group to which the current user belongs; and Update the operation mode corresponding to the specific user group according to the updated historical operation path statistics. A computer-readable storage medium stores a computer program thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 9. A computing device includes a memory and a processor, wherein an executable code is stored in the memory, and when the processor executes the executable code, the processor implements the function according to any one of claims 1 to 9. The method described.