Association degree acquisition method, electronic equipment and storage medium

Abstract

The invention provides an association degree obtaining method, electronic equipment and a storage medium, and relates to the field of computer science and technical field, and the method comprises the steps: for a target user, obtaining a target association APP list of a target APP A0 in a target time period, obtaining a target jump time list of the target APP A0 and a target jump score list corresponding to the target jump time list, and obtaining a preset weight list, and determining the association degree of the target APP A0 and the target associated APP based on the target jump score list P and the preset weight list W. According to the method, the associated APP is obtained through the active time point, and the association degree of the associated APP is accurately obtained based on the association degree of the associated APP.

Description

Technical Field

[0001] The present invention relates to the field of computer science and technology, and particularly to a method for obtaining an association degree, an electronic device, and a storage medium. Background Art

[0002] With the development of technology and user needs, data collection and analysis have become more efficient and accurate. A large amount of APP usage data, user behavior data, etc. can be collected, and through data analysis, the competitive relationships, associations, and upstream and downstream and partnership relationships in the industry can be mined. For example, based on data such as the switching frequency and usage time distribution of users between different APPs, it can be determined which APPs have competitive relationships and which APPs have complementary cooperation relationships.

[0003] In the prior art, by collecting the usage data of users on different APPs, the user overlap degree is calculated. If a large number of users use two APPs simultaneously, it indicates that they may have a competitive relationship, etc., and the usage duration and usage frequency of users on each APP are analyzed.

[0004] However, the above method also has the following technical problems:

[0005] Judging the competitive relationship only through the user overlap degree does not consider the specific scenarios, purposes, and behavioral details of users using the APP, and it is difficult to define the association degree. Summary of the Invention

[0006] In view of the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] According to a first aspect of the present invention, a method for obtaining an association degree is provided. The method is used to obtain the association degree between two APPs, and the method includes the following steps:

[0008] S100, for a target user, obtain a target associated APP list A = {A1, A2,..., A r ,..., A s} of a target APP A0 in a target time period. The target associated APP A r is the rth preset APP associated with the target APP A0, where the value range of r is from 1 to s, and s is the number of preset APPs associated with the target APP A0 in the target time period; wherein, the difference between the active time points of A r-1 and A r is within a preset time interval [a, b];

[0009] S200, obtain a target jump time list T = {T1, T2,..., T r ,..., T s} and the target jump score list P corresponding to T = {P1, P2,..., P r ,..., P s}, T r is A r-1 The time interval to jump to A r ;

[0010] Among them, if T r is equal to (b - a) / 2, the target jump score P r corresponding to T r is the preset maximum score M;

[0011] If T r is greater than (b - a) / 2, the target jump score P r corresponding to T r = k × (T r - (b - a) / 2) + M;

[0012] If T r is not greater than (b - a) / 2, the target jump score P r corresponding to T r = (-k) × (T r - (b - a) / 2) + M, where k is the preset descent slope and k < 0;

[0013] S300, obtain the preset weight list W = {W1, W2,..., W r ,..., W s},W r is A r-1 The preset weight to jump to A r , where W r is greater than W r+1 ;

[0014] S400, based on the target jump score list P and the preset weight list W, determine the association degree between the target APP A0 and the target associated APP.

[0015] According to the second aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement the foregoing method.

[0016] According to the third aspect of the present invention, there is provided an electronic device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor, and the processor implements the foregoing method when executing the computer program.

[0017] The present invention has at least the following beneficial effects: In summary, for the target user, obtain the list of target associated APPs of the target APP A0 in the target time period, obtain the target jump time list of the target APP A0 and the target jump score list corresponding to the target jump time list, obtain the preset weight list, and determine the association degree between the target APP A0 and the target associated APP based on the target jump score list P and the preset weight list W. The present invention obtains the associated APP through the active time point, and accurately obtains the association degree of the associated APP based on the association degree of the associated APP, thereby improving the observation efficiency of the enterprise for the target user. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0019] Figure 1 It is a flowchart of a method for obtaining the association degree provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar tasks, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0022] An embodiment of the present invention provides a method for obtaining the association degree, as Figure 1 shown. The method is used to obtain the association degree between two APPs, and the method includes the following steps:

[0023] S100. For a target user, obtain a list A of target associated apps of target app A0 in a target time period, where A = {A1, A2, …, A r , …, A s}, and target associated app A r is the r-th preset app associated with target app A0, where r ranges from 1 to s, and s is the number of preset apps associated with target app A0 in the target time period; among them, the difference in the active time points of A r-1 and A r is within a preset time interval [a, b].

[0024] In an embodiment of the present invention, the target time period is the current day. Specifically, a is the lower limit of the preset time interval, and b is the upper limit of the preset time interval.

[0025] Furthermore, S100 further includes: if s is less than a preset minimum association quantity threshold, expand the preset time interval [a, b] to [a, 2b], and re-obtain the list of target associated apps of target app A0 in the target time period. It can be understood that when s is less than the preset minimum association quantity threshold, it is considered that the number of target associated apps is too small. At this time, expanding the upper limit to twice the original is equivalent to considering the target associated apps in adjacent time slices.

[0026] Specifically, any method of obtaining the list of target associated apps in the prior art belongs to the protection scope of the present invention. The preferred method of the present invention is: in the target time period, obtain the active time points of target app A0 and the active time points of preset apps. If the difference between the active time point of A0 and the active time point of a preset app is within [a, b], associate the preset app with A0, and continue to find the associated apps of this preset app, so as to obtain the target association link of target app A0, and obtain the list A of target associated apps based on the target association link of target app A0.

[0027] It can be understood that the list of target associated apps starts with the target app, from A0 switching to A1, …, A r switching to A r+1 , …, A s-1 switching to A s , and the difference in each switch is within [a, b]. The present invention sets the lower limit a to avoid dirty data such as apps that are active at the same time during the collection process.

[0028] S200. Obtain a list T of target jump times of target app A0, where T = {T1, T2, …, T r , …, T sThe list of target jump scores P corresponding to T is P = {P1, P2, …, P r , …, P s}, and T r is the time interval for jumping to A r-1 . r

[0029] Among them, if T r is equal to (b - a) / 2, the target jump score P r corresponding to T r is the preset maximum score M

[0030] If T r is greater than (b - a) / 2, the target jump score P r corresponding to T r = k × (T r - (b - a) / 2) + M

[0031] If T r is not greater than (b - a) / 2, the target jump score P r corresponding to T r = (-k) × (T r - (b - a) / 2) + M, where k is the preset descent slope and k < 0

[0032] Specifically, the present invention sets the preset time interval [a, b], and when the target jump time is (b - a) / 2, it is the preset maximum score M. When the target jump time is other values within [a, b], the target jump score decreases linearly

[0033] S300. Obtain the preset weight list W = {W1, W2, …, W r , …, W s}, and W r is the preset weight for jumping to A r-1 . Among them, W r is greater than W r . r+1

[0034] In an embodiment of the present invention, ∑ s r＝1 W r = 1. In another embodiment of the present invention, obtain the specified weight list R = {R1, R2, …, R r , …, R s , …, R s+x}, x > 0, R1 + …… + R s + …… + R s + …… + R s+x = 1, and W r = R r .​​

[0035] S400 determines the degree of association between the target APP A0 and the target associated APP based on the target jump score list P and the preset weight list W.

[0036] Specifically, S400 further includes: the degree of association B between the target APP A0 and the target associated APP A r is r = W r × P r .

[0037] Furthermore, if the number of target associated APP lists of the target APP A0 in the target time period is greater than 1, execute S100 - S400 for each target associated APP list to obtain the degree of association between the target APP A0 and the target associated APP A r and sum up the degrees of association between the target APP A0 and the target associated APP A in all target associated APP lists as the final degree of association between the target APP A0 and the target associated APP A. r r r In summary, for the target user, obtain the target associated APP list of the target APP A0 in the target time period, obtain the target jump time list of the target APP A0 and the target jump score list corresponding to the target jump time list, obtain the preset weight list, and determine the degree of association between the target APP A0 and the target associated APP based on the target jump score list P and the preset weight list W. The present invention obtains the associated APP through the active time point and accurately obtains the degree of association of the associated APP based on the degree of association of the associated APP, improving the observation efficiency of the enterprise for the target user.

[0038]

[0039]

[0039] In an exemplary illustration of the present invention, the degree of association between the target user and a target APP and an associated APP is relatively high. The enterprise can focus on monitoring the target user and determine whether the target user needs personalized recommendation or whether the target user has switched to other enterprises based on the labels of the target APP and the associated APP.

[0040] Specifically, S100 further includes obtaining the preset time interval [a, b] through the following steps:

[0041] S001, obtain the normal distribution curve; wherein, the normal distribution curve is obtained based on the historical time difference of historical users, and the historical time difference is the difference between the active time point of the target APP A0 in the historical time period and the active time point of the preset APP opened adjacent to A0.

[0042] In an embodiment of the present invention, the historical time period is the previous week of the current time.

[0043] Specifically, those skilled in the art know that any method for obtaining adjacent opened APPs in the prior art falls within the protection scope of the present invention. For example, by setting a collection interval, the active situation of a preset APP is collected from historical users every collection interval to determine the active time point of the preset APP.

[0044] S002. Determine a confidence interval based on the area of the first preset normal distribution curve, and use the confidence interval as the preset time interval [a, b].

[0045] It can be understood that the difference between the active time point of the target APP A0 and the active time point of the preset APP opened adjacent to A0 is affected by multiple independent influencing factors. Therefore, in the case of a large number of samples, the difference conforms to a normal distribution. A confidence interval is determined based on the area and mean of the first preset normal distribution curve, and the confidence interval is used as the preset time interval [a, b], so that for most jumps of the target APP, except for special cases, they fall within the preset time interval [a, b].

[0046] Specifically, S100 further includes obtaining b through the following steps:

[0047] S003. Obtain a normal distribution curve; wherein, the normal distribution curve is obtained based on the historical time differences of historical users, and the historical time difference is the difference between the active time point of the target APP A0 and the active time point of the preset APP opened adjacent to A0 in a historical time period.

[0048] S004. Use the mean of the normal distribution curve as b. Specifically, in S002, using the confidence interval as the preset time interval only indicates that the adjacent opened APPs fall within the preset time interval, but the adjacent opening time may be too long and there may be no correlation between the adjacent opened APPs. Therefore, in S004, the mean of the normal distribution curve is used as b.

[0049] Further, after S004, it further includes:

[0050] S005. Determine a based on the area of the second preset normal distribution curve, where a is less than the mean of the normal distribution curve and the area formed by a and the mean of the normal distribution curve is equal to the area of the second preset normal distribution curve, and the area of the preset normal distribution curve is less than 50%. Specifically, the area of the second preset normal distribution curve is less than the area of the first preset normal distribution curve.

[0051] Further, after S004, it further includes:

[0052] S006. Obtain a preset minimum time threshold, and mark the historical time differences less than the preset minimum time threshold as intermediate time differences.

[0053] S007, obtain the average value of the intermediate time differences as a.

[0054] In summary, obtain a preset minimum time threshold, mark the historical time differences less than the preset minimum time threshold as intermediate time differences, and obtain the average value of the intermediate time differences as a, so as to more accurately obtain a.

[0055] Furthermore, after S400, it further includes:

[0056] S410, obtain the main label corresponding to the target APP A0 and the main label corresponding to the target associated APP.

[0057] In an embodiment of the present invention, the main label corresponding to the target APP A0 is a travel label, and the main label corresponding to the target associated APP is a search label.

[0058] S420, obtain the number of associations between A0 and the target associated APP. Specifically, it can be understood that the number of associations is the number of times the target associated APP appears in the target associated APP list.

[0059] S430, based on the degree of association and the number of associations between A0 and the target associated APP, determine the degree of association between the main label corresponding to A0 and the main label corresponding to the target associated APP.

[0060] In summary, obtain the main label corresponding to the target APP A0 and the main label corresponding to the target associated APP, obtain the number of associations between A0 and the target associated APP, and based on the degree of association and the number of associations between A0 and the target associated APP, determine the degree of association between the main label corresponding to A0 and the main label corresponding to the target associated APP, so as to improve the degree of personalized recommendation for users.

[0061] In an exemplary illustration of the present invention, the degree of association between the travel label and the search label is relatively high. After the user opens the APP corresponding to the travel label, the APP corresponding to the search label can be recommended to the user.

[0062] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store a computer program related to a method in the method embodiment. The computer program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0063] An embodiment of the present invention also provides an electronic device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method provided in the above embodiment is implemented.

[0064] Embodiments of the present invention also provide a computer program product, which includes program code. When the program product runs on an electronic device, the program code is used to cause the electronic device to execute the steps in the methods according to various exemplary embodiments of the present invention described above in this specification.

[0065] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and not for limiting the scope of the present invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for obtaining a degree of association, characterized in that: The method is used to obtain the degree of association between two apps, and the method includes the following steps: S100, for the target user, obtain the target associated APP list A of the target APP A0 in the target time period = {A1, A2, ..., A r , …, A s }, target associated APP A r is the rth preset APP associated with target APP A0, the value range of r is 1 to s, s is the number of preset APPs associated with target APP A0 in the target time period; r-1 and A r The difference of the active time points is within the preset time interval [a, b]; S200, obtain the target jump time list T of the target APP A0 = {T1, T2, ..., T r ,…,T s } and the target jump score list P corresponding to T = {P1, P2, ..., P r ,…,P s }, T r Yes A r-1 Jump to A r time interval; Among them, if T r = (ba) / 2, T r The corresponding target jump score P r is the preset maximum score M; If T r Greater than (ba) / 2, T r The corresponding target jump score P r =k×(T r -(ba) / 2)+M; If T r Not more than (ba) / 2, T r The corresponding target jump score P r =(-k)×(T r -(ba) / 2)+M, where k is a preset descending slope and k is less than 0; S300, obtaining a preset weight list W = {W1, W2, ..., W r , …, W s },W r Yes A r-1 Jump to A r The preset weights of r Greater than W r+1 ; S400: Based on the target jump score list P and the preset weight list W, determine the association degree between the target APP A0 and the target associated APP.

2. The method for obtaining the degree of association according to claim 1, characterized in that: S400 also includes: target APP A0 and target associated APP A r The degree of association B r =W r ×P r .

3. The method for obtaining the degree of association according to claim 1, characterized in that: S100 also includes obtaining a preset time interval [a, b] by the following steps: S001, obtaining a normal distribution curve; wherein the normal distribution curve is obtained based on the historical time difference of historical users, and the historical time difference is the difference between the active time point of the target APP A0 and the active time point of the preset APP opened adjacent to A0 in the historical time period; S002, determining a confidence interval based on the area of ​​the first preset normal distribution curve, and using the confidence interval as a preset time interval [a, b].

4. The method for obtaining the degree of association according to claim 1, characterized in that: S100 also includes obtaining b through the following steps: S003, obtaining a normal distribution curve; wherein the normal distribution curve is obtained based on the historical time difference of historical users, and the historical time difference is the difference between the active time point of the target APP A0 and the active time point of the preset APP opened adjacent to A0 in the historical time period; S004, take the mean of the normal distribution curve as b.

5. The method for obtaining the degree of association according to claim 4, characterized in that: After S004, it also includes: S005, based on the second preset normal distribution curve area, determine a, wherein a is less than the mean of the normal distribution curve and the area formed by a and the mean of the normal distribution curve is equal to the second preset normal distribution curve area, and the preset normal distribution curve area is less than 50%.

6. The method for obtaining the degree of association according to claim 4, characterized in that: After S004, it also includes: S006, obtaining a preset minimum time threshold, and marking a historical time difference that is less than the preset minimum time threshold as an intermediate time difference; S007, obtaining the average value of the intermediate time differences as a.

7. The method for obtaining the degree of association according to claim 1, characterized in that: S100 also includes: if s is less than a preset minimum association quantity threshold, expanding the preset time interval [a, b] to [a, 2b], and reacquiring a target associated APP list of the target APP A0 in the target time period.

8. The method for obtaining the degree of association according to claim 1, characterized in that: After S400, it also includes: S410, obtaining the main tag corresponding to the target APP A0 and the main tag corresponding to the target associated APP; S420, obtaining the number of associations between A0 and the target associated APP; S430 , based on the association degree and association times between A0 and the target associated APP, determine the association degree between the main tag corresponding to A0 and the main tag corresponding to the target associated APP.

9. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer program, which is loaded and executed by a processor to implement the method for obtaining the degree of association as described in any one of claims 1 to 8.

10. An electronic device, comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for obtaining the degree of association as described in any one of claims 1 to 8 when executing the computer program.

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