Application recommendation method and device, electronic equipment and storage medium

Abstract

The invention discloses an application recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of information recommendation. Determining at least one historical time period corresponding to a first time period to which the current event belongs, determining a second time period before the current time, determining a first application program set used in the at least one historical time period, and determining corresponding use times of the first application program set in the at least one historical time period, and determining a second application program set used in the second time period, determining corresponding final use time of the second application program set in the second time period, and performing application recommendation on the first application program set based on the use frequency and the final use time. According to the application recommendation method and device, application recommendation is performed by fusing the recently used application and the application habitually used in each time period, so that the use of a single and fixed recommendation strategy can be avoided, the accuracy of application recommendation is improved, and the requirements of users are better met.

Description

Technical Field

[0001] The present application relates to the technical field of information recommendation, and more specifically, to an application recommendation method, device, electronic device and storage medium. Background Art

[0002] With the development of science and technology, electronic devices are used more and more widely and have more and more functions. They have become one of the necessities in people's daily life. At present, electronic devices can predict the applications that users may use and recommend them. However, the current strategy for predicting the applications that users may use is relatively simple and single, and it is impossible to accurately predict and recommend the applications that users may use, which does not meet the needs of users. Summary of the invention

[0003] In view of the above problems, the present application proposes an application recommendation method, device, electronic device and storage medium to solve the above problems.

[0004] In a first aspect, an embodiment of the present application provides an application recommendation method, the method comprising: determining at least one historical time period corresponding to a first time period to which a current time belongs, and determining a second time period before the current time, wherein the end time of the second time period is the current time; determining a first application set consisting of applications used in the at least one historical time period, and determining the number of times each application included in the first application set is used in the at least one historical time period; determining a second application set consisting of applications used in the second time period, and determining the last time of use of each application included in the second application set in the second time period; and making application recommendations for the applications included in the first application set based on the number of uses and the last time of use.

[0005] In a second aspect, an embodiment of the present application provides an application recommendation device, the device comprising: a time period determination module, used to determine at least one historical time period corresponding to a first time period to which a current time belongs, and to determine a second time period before the current time, wherein the end time of the second time period is the current time; a usage count determination module, used to determine a first application set consisting of applications used in the at least one historical time period, and to determine the number of uses corresponding to each application included in the first application set in the at least one historical time period; a last use time determination module, used to determine a second application set consisting of applications used in the second time period, and to determine the last use time corresponding to each application included in the second application set in the second time period; an application recommendation module, used to make application recommendations for applications included in the first application set based on the number of uses and the last use time.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory is coupled to the processor, the memory stores instructions, and when the instructions are executed by the processor, the processor executes the above method.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a program code is stored, and the program code can be called by a processor to execute the above method.

[0008] The application recommendation method, device, electronic device and storage medium provided in the embodiments of the present application first determine at least one historical time period corresponding to the first time period to which the current event belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time, and then determine a first application set consisting of applications used in at least one historical time period, and determine the number of times each application included in the first application set is used in at least one historical time period, determine a second application set consisting of applications used in a second time period, and determine the last time each application included in the second application set is used in the second time period, and finally, based on the number of times and the last time of use, recommend applications to the applications included in the first application set, so that application recommendations are made by integrating the most recently used applications and the applications habitually used in each time period, thereby avoiding the use of a single and fixed recommendation strategy, improving the accuracy of application recommendations, and better meeting user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 A flow chart of an application recommendation method provided in an embodiment of the present application is shown.

[0011] Figure 2 A schematic diagram showing the division of time periods provided in an embodiment of the present application is shown;

[0012] Figure 3 A schematic diagram showing the application usage count record provided in an embodiment of the present application is shown;

[0013] Figure 4 A schematic diagram showing a flow chart of an application recommendation method provided by an embodiment of the present application is shown;

[0014] Figure 5 This application shows Figure 4 The flowchart of step S250 of the application recommendation method shown is as follows;

[0015] Figure 6 This application shows Figure 4 The flowchart of step S260 of the application recommendation method shown is as follows;

[0016] Figure 7 A schematic diagram showing a flow chart of an application recommendation method provided by an embodiment of the present application is shown;

[0017] Figure 8 A module block diagram of an application recommendation device provided by an embodiment of the present application is shown;

[0018] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application for executing an application recommendation method according to an embodiment of the present application is shown;

[0019] Fig.10 A storage unit for storing or carrying program codes for implementing the application recommendation method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0021] At present, most electronic devices are equipped with functions such as smart sidebar, floating menu, and smart recommendation. Among them, application recommendation is one of the main recommendation contents. The current main recommendation strategies are as follows:

[0022] A: Recommend recently used applications or applications set by the user;

[0023] B: Recommended for applications that are used more frequently.

[0024] It is clear that the strategy of predicting the applications that users may use in the above manner is relatively simple and single, and is unable to accurately predict and recommend applications that users may use, and does not meet the needs of users.

[0025] In response to the above problems, the inventors have discovered after long-term research and proposed the application recommendation method, device, electronic device and storage medium provided in the embodiments of the present application. By integrating the recently used applications and the applications that are habitually used in different time periods for application recommendation, it is possible to avoid using a single and fixed recommendation strategy, improve the accuracy of application recommendation, and better meet the needs of users. The specific application recommendation method will be described in detail in the subsequent embodiments.

[0026] See also Figure 1 , Figure 1 The flowchart of the application recommendation method provided by an embodiment of the present application is shown. The method is used to recommend applications by integrating recently used applications and applications that are habitually used in different time periods, which can avoid using a single and fixed recommendation strategy, improve the accuracy of application recommendations, and better meet the needs of users. In a specific embodiment, the application recommendation method is applied to Figure 8 The application recommendation device 200 and the electronic device 100 equipped with the application recommendation device 200 are shown in FIG. Fig. 9 ). The following will take an electronic device as an example to illustrate the specific process of this embodiment. Of course, it can be understood that the electronic device used in this embodiment may include a smart phone, a tablet computer, a wearable electronic device, etc., which is not limited here. Figure 1 The process shown is described in detail, and the application recommendation method may specifically include the following steps:

[0027] Step S110: Determine at least one historical time period corresponding to the first time period to which the current time belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time.

[0028] In this embodiment, the current time can be determined. Optionally, the electronic device can have a built-in clock, and the current time can be determined by the built-in clock of the electronic device. Optionally, the current time can be in hours, such as 9:00 a.m.; the current time can be in minutes, such as 9:30 a.m.; the current time can be in seconds, such as 9:30 a.m. 10 seconds, etc., which are not limited here.

[0029] In this embodiment, when the current time is determined, the time period to which the current time belongs can be determined as the first time period, and at least one historical time period corresponding to the first time period can be determined. In some implementations, the electronic device can be pre-divided into multiple time periods. Based on this, when the current time is determined, the current time can be compared with the multiple pre-divided time periods to determine the time period to which the current time belongs from the multiple pre-divided time periods as the first time period. Afterwards, when the first time period is determined, at least one historical time period corresponding to the first time period before the first time period can be determined. Of course, the number of historical time periods can be one or more, and the specific number can be dynamically set according to demand, which is not limited here.

[0030] As an example, Figure 2 As shown, a day can be divided into N time periods P1, P2, P3...PN based on the 24 hours of a natural day. When the current time is determined, the time period to which the current time period belongs can be determined from the N time periods P1, P2, P3...PN as the first time period, and then at least one historical time period corresponding to the first time period is determined. For example, if it is determined that the first time period to which the current time T belongs is P3, the P3 time period of the current M days before can be determined as the historical time period, where M is an integer greater than or equal to 1. For example, if N is 4 and M is 5, the multiple time periods pre-divided by the electronic device can be 0 o'clock to 6 o'clock, 6 o'clock to 12 o'clock, 12 o'clock to 18 o'clock, and 18 o'clock to 24 o'clock. Based on this, if the current time T is 9 o'clock, 6 o'clock to 12 o'clock on the current day can be used as the first time period, and 6 o'clock to 12 o'clock in the previous five days can be used as the historical time period.

[0031] In this embodiment, when the current time period is determined, a second time period before the current time can be determined, wherein the end time of the second time period is the current time. In some embodiments, the electronic device can be pre-set with a retrospective duration, based on which, when the current time is determined, the second time period can be determined based on the current time and the retrospective duration. Of course, the retrospective duration can be fixed after being pre-set, or the retrospective duration can be dynamically updated during use. For example, if the application is used more frequently, it can be fixed, and if the application is used less frequently, the retrospective duration can be dynamically extended, etc., which is not limited here.

[0032] As an example, assuming that the current time is T and the backtracking time is D, then the time period corresponding to [TD, T] can be used as the second time period.

[0033] Step S120: Determine a first application set consisting of application programs used in the at least one historical time period, and determine the number of times each application program included in the first application set has been used in the at least one historical time period.

[0034] In this embodiment, when at least one historical time period is determined, it can be determined that the applications used in at least one historical time period constitute a first application set, and the number of times each application included in the first application set is used in at least one historical time period can be determined.

[0035] In some embodiments, for each of the multiple time periods pre-divided by the electronic device, the applications used in the time period and the number of times each application was used can be recorded and counted according to the set data statistics period (such as by day or by week). Subsequently, when the first time period corresponding to the current time is determined from the multiple time periods pre-divided, and at least one historical time period corresponding to the first time period is determined, the applications used in at least one historical time period can be determined to form a first application set based on the recorded applications used in each time period and the number of times each application was used, and the number of times each application included in the first application set is used in at least one historical time period can be determined.

[0036] As an example, suppose that for each of the multiple time periods pre-divided by the electronic device, the applications used in each time period and the number of times each application was used are counted according to the number of days. Then, each time a user uses an application, the time period P that the application falls into can be determined according to the application usage time T, and the number of times the application is used on that day in the time period P is increased by 1. After recording for several days, the following can be obtained: Figure 3 Data shown.

[0037] For example, if N is 4 and M is 5, the multiple time periods pre-divided by the electronic device may be 0:00-6:00, 6:00-12:00, 12:00-18:00, and 18:00-24:00. Based on this, if the current time T is 9:00, 6:00-12:00 of the current day can be used as the first time period, and 6:00-12:00 of the previous five days can be used as the historical time period. Based on this, it can be determined that the applications used between 6:00 and 12:00 of the previous five days (including the previous day, the previous two days, the previous three days, the previous four days, and the previous five days) constitute the first application set, and the number of uses of each application included in the first application set in the previous five days is determined. As an example, if an application is used once in the previous day, 3 times in the previous two days, 0 times in the previous three days, 4 times in the previous four days, and 1 time in the previous five days, it can be determined that the number of uses of the application in the previous five days is 10 times.

[0038] As a specific example, as shown in Table 1, it is assumed that the first application programs used in at least one historical time period include: WeChat, Alipay, Weibo, Douyin, Weather, and Camera, and the corresponding number of uses of WeChat in at least one historical time period is 23 times, the corresponding number of uses of Alipay in at least one historical time period is 14 times, the corresponding number of uses of Weibo in at least one historical time period is 9 times, the corresponding number of uses of Douyin in at least one historical time period is 15 times, the corresponding number of uses of Weather in at least one historical time period is 5 times, and the corresponding number of uses of Camera in at least one historical time period is 7 times.

[0039]

[0040] Table 1

[0041] As an implementable manner, the application being used in the historical time period may include: the application startup (including cold startup or hot startup) time is within the historical time period. Therefore, in this embodiment, it is possible to detect whether the application is started, wherein if the application startup is detected, the startup time of the application can be determined, and the historical time period to which the startup time of the application belongs can be determined, and the application is determined as an application used in the historical time period, and the number of times it is used in the historical time period is increased by 1.

[0042] As another practicable manner, the application being used in the historical time period may include: the running time of the application being in the historical time period. Therefore, in this embodiment, it is possible to detect whether the application is running, wherein if it is detected that the application is running, the running time of the application can be determined, and the historical time period to which the running time of the application belongs can be determined, and the application is determined as an application used in the historical time period, and the number of times the application is used in the historical time period is increased by 1.

[0043] Step S130: determining a second application set consisting of application programs used in the second time period, and determining the last usage time corresponding to each application program included in the second application set in the second time period.

[0044] In this embodiment, when the second time period is determined, it can be determined that the applications used in the second time period form a second application set, and the last use time corresponding to each application included in the second application set in the second time period is determined.

[0045] It is understandable that the application used in the second time period may be the same as or different from the application used in at least one historical time period. For example, if an application has been used in both at least one historical time period and the second time period, the application may be included in both the first application set and the second application set; if an application has only been used in at least one historical time period but not in the second time period, the application may only be included in the first application set; if an application has not been used in at least one historical time period but only in the second time period, the application may only be included in the second application set.

[0046] In some implementations, during the use of each application, the last use time of each application may be recorded to obtain the last use time corresponding to each application. Based on this, when determining the second time period before the current time, the applications with the corresponding last use time within the second time period may be determined to form a second application set according to the recorded last use time corresponding to each application, and the last use time corresponding to each application included in the second application set within the second time period may be determined.

[0047] As a specific example, as shown in Table 2, it is assumed that the second application programs used in the second time period include: WeChat, NetEase News, Alipay, Weibo, Douyin, Weather, and Photo Album, and the last use time of WeChat in the second time period is: 2024-01-05 09:11:00, the last use time of NetEase News in the second time period is: 2024-01-05 09:51:00, the last use time of Alipay in the second time period is: 2024-01-05 09:51:00, the last use time of Weibo in the second time period is: 2024-01-05 07:45:00, the last use time of Douyin in the second time period is: 2024-01-05 09:55:00, the last use time of Weather in the second time period is: 2024-01-0508:11:00, and the last use time of Photo Album in the second time period is: 2024-01-05 09:58:00.

[0048] app Last used time WeChat 2024-01-05 09:11:00 NetEase News 2024-01-05 09:51:00 Alipay 2024-01-05 09:51:00 Weibo 2024-01-05 07:45:00 Tik Tok 2024-01-05 09:55:00 weather 2024-01-05 08:11:00 Photo Album 2024-01-05 09:58:00

[0049] Table 2

[0050] As an implementable manner, the last use time of the application in the second time period may include: the time when the application was last opened in the foreground in the second time period. Therefore, in this embodiment, in the case of determining the second application set composed of the applications used in the second time period, the time when the applications included in the second application set were last opened in the foreground in the second time period can be determined, and the time when the applications included in the second application set were last opened in the foreground can be determined as the last use time corresponding to the applications included in the second application set. That is, in this embodiment, it is possible to detect whether the application is opened in the foreground, wherein if it is detected that the application is opened in the foreground, it can be determined that the time when the application is opened in the foreground is recorded as the time when the application was last opened in the foreground. Of course, if it is detected that the application is opened in the foreground again later, the time when the application is opened in the foreground can be updated as the time when the application was last opened in the foreground. Based on this, in the case of determining the second time period, it can be determined that the applications in the second time period whose last time was opened in the foreground form the second application set, and the time when the applications included in the second application set were last opened in the foreground in the second time period can be determined as the last use time corresponding to the applications included in the second application set in the second time period.

[0051] As another practicable manner, the last use time of the application in the second time period may include: the time when the application last exited the foreground in the second time period. Therefore, in this embodiment, when determining the second application set composed of the applications used in the second time period, the time when the applications included in the second application set last exited the foreground in the second time period can be determined, and the time when the second application set last exited the foreground can be determined as the last use time corresponding to the applications included in the second application set. That is, in this embodiment, it is possible to detect whether the application exits the foreground, wherein if it is detected that the application exits the foreground, it can be determined that the time when the application exits the foreground is recorded as the time when the application last exits the foreground. Of course, if it is detected that the application exits the foreground again later, the time when the application exits the foreground can be updated as the time when the application last exits the foreground. Based on this, when determining the second time period, it can be determined that the applications in the second time period that last exited the foreground constitute the second application set, and the time when the applications included in the second application set last exited the foreground in the second time period can be determined as the last use time corresponding to the applications included in the second application set in the second time period.

[0052] Step S140: Based on the number of uses and the last use time, recommend applications included in the first application set.

[0053] In this embodiment, after determining the number of times each application included in the first application set is used within at least one historical time period, and determining the last time each application included in the second application set is used within the second time period, application recommendations can be made for the applications included in the first application set based on the number of times used and the last time used.

[0054] Optionally, making application recommendations for the application programs included in the first application program set may include: screening the application programs to be recommended from the application programs included in the first application program set, and making application recommendations for the screened application programs to be recommended.

[0055] Optionally, recommending applications included in the first application set may include: sorting the applications included in the first application set to obtain an application ranking, and recommending applications included in the first application set according to the application ranking.

[0056] In some embodiments, after determining the number of times each application included in a first application set is used within at least one historical time period, and determining the last time each application included in a second application set is used within the second time period, the applications included in the first application set can be scored based on the number of times used and the last time used to obtain scores corresponding to each application included in the first application set, and application recommendations can be made for the applications included in the first application set based on the scores corresponding to each application included in the first application set.

[0057] As an implementable manner, the electronic device may pre-set and store an application recommendation algorithm. When the number of uses and the last usage time are determined, the number of uses and the last usage time may be calculated by the application recommendation algorithm to obtain an application recommendation result, and based on the application recommendation result, application recommendations may be made for the applications included in the first application set.

[0058] As another feasible method, the electronic device may pre-set and store application recommendation rules, and after determining the number of times used and the last time used, the application recommendation rules, the number of times used and the last time used may be used to recommend the applications included in the first application set.

[0059] An application recommendation method provided by an embodiment of the present application determines at least one historical time period corresponding to a first time period to which a current event belongs, and determines a second time period before the current time, wherein the end time of the second time period is the current time, determines a first application set consisting of applications used in at least one historical time period, and determines the number of times each application included in the first application set is used in at least one historical time period, determines a second application set consisting of applications used in a second time period, and determines the last time each application included in the second application set is used in the second time period, and finally, based on the number of times and the last time of use, recommends applications to the applications included in the first application set, thereby making application recommendations by integrating recently used applications and applications habitually used in each time period, thereby avoiding the use of a single and fixed recommendation strategy, improving the accuracy of application recommendations, and better meeting user needs.

[0060] See also Figure 4 , Figure 4 The following is a flow chart of the application recommendation method provided by an embodiment of the present application. Figure 4 The process shown is described in detail, and the application recommendation method may specifically include the following steps:

[0061] Step S210: Determine at least one historical time period corresponding to the first time period to which the current time belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time.

[0062] Step S220: Determine a first application set consisting of application programs used in the at least one historical time period, and determine the number of times each application program included in the first application set has been used in the at least one historical time period.

[0063] Step S230: Determine a second application set consisting of application programs used in the second time period, and determine the last usage time corresponding to each application program included in the second application set in the second time period.

[0064] For the detailed description of step S210 to step S230 , please refer to step S110 to step S130 , which will not be repeated here.

[0065] Step S240: Based on the number of times used, determine a first recommendation value corresponding to each application included in the first application set, wherein the first recommendation value is positively correlated with the number of times used.

[0066] In this embodiment, when the number of times each application included in the first application set is used in at least one historical time period is determined, the first recommendation value corresponding to each application included in the first application set can be determined based on the number of times used. Optionally, the first recommendation value is positively correlated with the number of times used, that is, if the number of times a certain application is used in at least one historical time period is greater, the first recommendation value corresponding to the application is higher; if the number of times a certain application is used in at least one historical time period is less, the first recommendation value corresponding to the application is lower.

[0067] As an implementable manner, the electronic device may pre-set and store a method for calculating the first recommendation value. When determining the number of times each application included in the first application set is used within at least one historical time period, the first recommendation value calculation method may be used to calculate the number of times each application included in the first application set is used within at least one historical time period to obtain the first recommendation value corresponding to each application included in the first application set.

[0068] As another practicable manner, the electronic device may pre-set and store a first mapping relationship, wherein the first mapping relationship may include multiple usage times, multiple first recommended values, and a correspondence between multiple usage times and multiple first recommended values. Optionally, the correspondence between multiple usage times and multiple first recommended values ​​may include: one usage time corresponds to one first recommended value, or multiple usage times correspond to one first recommended value, etc., which are not limited here. Therefore, in this embodiment, when determining the usage times corresponding to each application included in the first application set within at least one historical time period, the first recommended value that has a correspondence with the usage times corresponding to each application included in the first application set within at least one historical time period may be determined based on the first mapping relationship as the first recommended value corresponding to each application included in the first application set.

[0069] As an example, the first recommendation value corresponding to an application may be consistent with the number of times the application has been used. Assume that the applications used in at least one historical time period include: WeChat, Alipay, Weibo, Douyin, Weather, and Camera, and the number of times WeChat has been used in at least one historical time period is 23 times, the number of times Alipay has been used in at least one historical time period is 14 times, the number of times Weibo has been used in at least one historical time period is 9 times, the number of times Douyin has been used in at least one historical time period is 15 times, the number of times Weather has been used in at least one historical time period is 5 times, and the number of times the camera has been used in at least one historical time period is 7 times. Then, it can be determined that the application recommendation score corresponding to WeChat is 23 points, the application recommendation score corresponding to Alipay is 14 points, the application recommendation score corresponding to Weibo is 9 points, the application recommendation score corresponding to Douyin is 15 points, the application recommendation score corresponding to Weather is 5 points, and the application recommendation score corresponding to Camera is 7 points.

[0070] Step S250: Based on the last usage time, determine the second recommendation value corresponding to each application included in the first application set, wherein the difference between the current time and the last usage time is negatively correlated with the second recommendation value.

[0071] In this embodiment, when the last usage time corresponding to each application included in the second application set within the second time period is determined, the second recommendation value corresponding to each application included in the first application set can be determined based on the last usage time. Optionally, the difference between the current time and the last usage time is negatively correlated with the second recommendation value, that is, if the difference between the last running time of a certain application and the current time is smaller, the closer the last running time of the application is to the current time, the larger the second recommendation value corresponding to the application is; if the difference between the last running time of a certain application and the current time is larger, the farther the last running time of the application is from the current time, the smaller the second recommendation value corresponding to the application is.

[0072] It can be understood that the applications used in the second time period may be the same as or different from the applications used in at least one historical time period, that is, the second application set and the first application set may include the same applications or different applications. Therefore, in this embodiment, when determining the last usage time corresponding to each application included in the second application set in the second time period, the second recommendation value corresponding to each application included in the first application set can be determined based on the different situations of the same applications or different applications included in the first application set and the second application set, and the last usage time corresponding to each application included in the second application set in the second time period.

[0073] In some embodiments, when determining the last usage time corresponding to each application included in the second application set within the second time period, the difference between the last usage time corresponding to each application included in the second application set within the second time period and the current time can be calculated to obtain the time difference corresponding to each application included in the second application set, and based on the time difference corresponding to each application included in the second application set, the second recommendation value corresponding to each application included in the first application set is determined.

[0074] As an implementable manner, the electronic device may pre-set and store a method for calculating the second recommendation value. When determining the last usage time corresponding to each application included in the second application set within the second time period, the second recommendation value calculation method may be used to calculate the second recommendation value for each application included in the second application set within the second time period, thereby obtaining the second recommendation value corresponding to each application included in the first application set. Specifically, when determining the last usage time corresponding to each application included in the second application set within the second time period, the difference between the last usage time corresponding to the applications included in the second application set within the second time period and the current time may be calculated to obtain the time difference corresponding to each application included in the second application set. The second recommendation value calculation method may be used to calculate the second recommendation value for the time difference corresponding to each application included in the second application set to obtain the second recommendation value corresponding to each application included in the first application set.

[0075] As another practicable manner, the electronic device may pre-set and store a second mapping relationship, wherein the mapping relationship may include multiple time difference values, multiple second recommended values, and a correspondence between multiple time difference values ​​and multiple second recommended values. Optionally, the correspondence between multiple time difference values ​​and multiple second recommended values ​​may include: one time difference value corresponds to one second recommended value, or multiple time difference values ​​correspond to one second recommended value, etc., which are not limited here. Therefore, in this embodiment, when determining the last usage time corresponding to each application included in the second application set within the second time period, the difference between the last usage time corresponding to the applications included in the second application set within the second time period and the current time may be calculated to obtain the time difference value corresponding to each application included in the second application set, and then based on the second mapping relationship, determine the second recommended value that has a corresponding relationship with the time difference value corresponding to each application included in the second application set as the second recommended value corresponding to each application included in the first application set.

[0076] As an example, as shown in Table 3, a rule for determining a second recommended value can be predefined. The principle for determining the second recommended value is that the closer the application's last usage time is to the current time, the higher the second recommended value, and the farther the application's last usage time is from the current time, the lower the second recommended value.

[0077] Current time - last used time (time difference) Second recommended value Less than 10 minutes 20 Greater than or equal to 10 minutes and less than 60 minutes 8 More than 60 minutes 5 NA (not in the most recently used list) 1

[0078] Table 3

[0079] See also Figure 5 , Figure 5This application shows Figure 4 The following is a flow chart of step S250 of the application recommendation method. Figure 5 The process shown is described in detail, and the method may specifically include the following steps:

[0080] Step S251: If there is an application included in the second application set in the first application set, determine a second recommendation value corresponding to the application included in the second application set according to the last usage time corresponding to the application included in the second application set.

[0081] In some embodiments, for each application included in the first application set, it can be determined whether it is included in the second application set. In one practicable manner, each application included in the first application set can be compared with the applications included in the second application set to determine whether each application included in the first application set is the same as any application included in the second application set, wherein if the comparison result indicates that an application included in the first application set is the same as an application included in the second application set, it can be determined that the application included in the first application set is included in the second application set; if the comparison result indicates that an application included in the first application set is different from all applications included in the second application set, it can be determined that the application included in the first application set is not included in the second application set. For example, the application name / package name of each application included in the first application set can be compared with the application name / package name corresponding to each application included in the second application set to determine whether each application included in the first application set is the same as any application included in the second application set.

[0082] If it is determined that an application included in the first application set is included in the second application set, it can be considered that the application has been used recently (used within the second time period), and there is a corresponding last use time for the application within the second time period. Therefore, the second recommendation value corresponding to the application can be determined according to the last use time corresponding to the application. That is, if there is an application included in the second application set in the first application set, the second recommendation value corresponding to the application included in the second application set is determined according to the last use time corresponding to the application included in the second application set.

[0083] As an example, if the current time is determined to be 2024-01-05 10:00:00, if the last usage time of an application is 2024-01-05 09:11:00, then according to the corresponding relationship shown in Table 3, it can be determined that the second recommended value corresponding to the application is 8; if the last usage time of an application is 2024-01-05 09:51:00, then according to the corresponding relationship shown in Table 3, it can be determined that the second recommended value corresponding to the application is 20, and so on. No further details are given here.

[0084] Step S252: If there are applications in the first application set that are not included in the second application set, determine the second recommendation score corresponding to the application that is not included in the second application set, wherein the second recommendation value corresponding to the application included in the second application set is greater than the second recommendation value corresponding to the application that is not included in the second application set.

[0085] Among them, if it is determined that the application is not included in the second application set, it can be considered that the application has not been used recently (has not been used in the second time period), and there is no corresponding last use time for the application in the second time period. Therefore, the second recommendation value corresponding to the application can be set to a default second recommendation value, wherein the default second recommendation value is less than the second recommendation value determined according to the last use time corresponding to the application. That is, if there is an application in the first application set that is not included in the second application set, the second recommendation value (default second recommendation value) corresponding to the application that is not included in the second application set is determined, and the default score weighting coefficient is used as the second score weighting coefficient, that is, the first score weighting coefficient is greater than the second score weighting coefficient.

[0086] As an example, if the current time is determined to be 2024-01-05 10:00:00 and the second time period is 1 day, and if the last usage time of an application is 2024-01-03 09:11:00, then according to the corresponding relationship shown in Table 3, it can be determined that the second recommended value corresponding to the application is the default second recommended value 1, which is not repeated here.

[0087] Step S260: Based on the first recommendation value and the second recommendation value, make application recommendations for the application programs included in the first application program set.

[0088] In this embodiment, when the first recommendation value corresponding to each application included in the first application set and the second recommendation value corresponding to each application included in the first application set are obtained, the applications included in the first application set can be recommended based on the first recommendation value and the second recommendation value.

[0089] In some embodiments, the electronic device may be pre-set and stored with an application recommendation algorithm. When a first recommendation value corresponding to each application included in a first application set and a second recommendation value corresponding to each application included in the first application set are obtained, the first recommendation value and the second recommendation value may be calculated through the application recommendation algorithm to obtain an application recommendation result, and application recommendations may be made for the applications included in the first application set based on the application recommendation result.

[0090] As an implementable method, when obtaining the first recommendation value corresponding to each application included in the first application set and the second recommendation value corresponding to each application included in the first application set, the product of the first recommendation value corresponding to each application included in the first application set and the corresponding second recommendation value can be calculated to obtain an application recommendation result, and application recommendations can be made for the applications included in the first application set based on the application recommendation result.

[0091] As another feasible method, when obtaining the first recommendation value corresponding to each application included in the first application set and the second recommendation value corresponding to each application included in the first application set, the sum of the first recommendation value corresponding to each application included in the first application set and the corresponding second recommendation value can be calculated to obtain the application recommendation result, and application recommendations can be made for the applications included in the first application set based on the application recommendation result.

[0092] Optionally, the first recommendation value may include an application recommendation score, and the second recommendation value may include a score weighting coefficient, such as a score weighting multiple.

[0093] See also Figure 6 , Figure 6 This application shows Figure 4 The following is a flow chart of step S260 of the application recommendation method. Figure 6 The process shown is described in detail, and the method may specifically include the following steps:

[0094] Step S261: Based on the application recommendation score and the score weighting coefficient, determine the weighted application recommendation score corresponding to each application included in the first application set.

[0095] In some embodiments, when the application recommendation scores corresponding to each application included in the first application set and the score weighting coefficients corresponding to each application included in the first application set are obtained, the weighted application recommendation scores corresponding to each application included in the first application set can be determined based on the application recommendation scores and the score weighting coefficients.

[0096] As an implementable manner, when obtaining the application recommendation scores corresponding to each application included in the first application set and the score weighting coefficients corresponding to each application included in the first application set, the application recommendation scores corresponding to each application included in the first application set and the corresponding score weighting coefficients can be multiplied to obtain the weighted application recommendation scores corresponding to each application included in the first application set.

[0097] As an example, as shown in Table 4, if the current time is determined to be 2024-01-05 10:00:00, then, according to the number of uses corresponding to each application included in the first application set (determining the application recommendation score) and the last use time corresponding to each application included in the first application set (determining the score weighting coefficient), the weighted application recommendation score corresponding to each application included in the first application set can be determined. For example, if it is determined that WeChat is used 23 times in at least one historical time period, its application recommendation score can be determined to be 23, and the last use time of WeChat in the second time period is 2024-01-05 09:11:00, and the time difference between it and the current time of 2024-01-05 10:00:00 is greater than 10 minutes and less than 60 minutes, the score weighting coefficient corresponding to WeChat is determined to be 8, and then the weighted application recommendation score of WeChat is calculated to be 23*8=184; if it is determined that the camera is used 7 times in at least one historical time period, its application recommendation score can be determined to be 7, but WeChat has not been used in the second time period, the score weighting coefficient corresponding to the camera is determined to be 1, and then the weighted application recommendation score of the camera is calculated to be 7*1=7.

[0098]

[0099] Table 4

[0100] Step S262: Recommending applications to the application programs included in the first application program set in descending order of weighted application recommendation scores.

[0101] In some embodiments, when the weighted application recommendation scores corresponding to the applications included in the first application set are obtained, application recommendations can be made for the applications included in the first application set in descending order of the weighted application recommendation scores.

[0102] As an implementable manner, when the weighted application recommendation scores corresponding to each application included in the first application set are obtained, the weighted application recommendation scores corresponding to each application included in the first application set can be compared to determine the size relationship between the weighted application recommendation scores corresponding to each application included in the first application set, and based on the size relationship, the order of the weighted application recommendation scores from high to low is determined, and application recommendations are made for the applications included in the first application set in the order of the weighted application recommendation scores from high to low.

[0103] In some implementations, all applications included in the first application set may be recommended in descending order of weighted application recommendation scores. For example, as shown in Table 4, all applications are recommended in the order of TikTok, Alipay, WeChat, Weibo, Weather, and Camera.

[0104] In some embodiments, the applications included in the first application set that are ranked before a preset order may be determined and recommended in the order of the weighted application recommendation scores from high to low. As an implementable manner, the electronic device may pre-set and store a preset order, which is used as a basis for judging whether an application is recommended. Therefore, in this embodiment, when the order of the weighted application recommendation scores corresponding to the applications included in the first application set is obtained from high to low, the order of the weighted application recommendation scores corresponding to each of the applications included in the first application set may be compared with the preset order to determine whether the order of the weighted application recommendation scores corresponding to each application is before the preset order. If the order of the weighted application recommendation score corresponding to a certain application is before the preset order, the application may be recommended; if the order of the weighted application recommendation score corresponding to a certain application is not before the preset order, the application may not be recommended.

[0105] As an example, if the preset order is 3, as shown in Table 4, Douyin, Alipay and WeChat can be recommended, but Weibo, Weather and Camera are not recommended.

[0106] In some implementations, an application list may be generated based on the applications included in the first application set in descending order of the weighted application recommendation scores, and the application list may be displayed. That is, the applications included in the first application set may be recommended to the user in the form of an application list in descending order of the weighted application recommendation scores, which may improve the display and recommendation effects of the applications.

[0107] In some implementations, applications in the application blacklist can be filtered from the applications included in the first application set to obtain the filtered applications, and the filtered applications can be recommended in descending order of the weighted application recommendation scores to ensure that the recommended applications meet the requirements and improve the application recommendation effect. As an implementable method, the electronic device can be pre-set and stored with a preset blacklist, wherein the preset blacklist can include preset blacklist applications, such as system desktop, background services, black and gray production applications, etc., which are not limited here. Therefore, in this embodiment, when determining the applications included in the first application set, the applications included in the first application set can be compared with the preset blacklist applications in the application blacklist to determine whether each application is in the application blacklist. Among them, if an application is in the application blacklist, the application can be filtered, and subsequently, the application is not recommended; if an application is in the application blacklist, the application can be retained, and subsequently, the application can be recommended according to the weighted application recommendation score corresponding to the application.

[0108] An application recommendation method provided by an embodiment of the present application determines at least one historical time period corresponding to the first time period to which the current time belongs, and determines a second time period before the current time, wherein the end time of the second time period is the current time, determines a first application set consisting of applications used in at least one historical time period, and determines the number of times each application included in the first application set has been used in at least one historical time period, determines a second application set consisting of applications used in the second time period, and determines the last time each application included in the second application set has been used in the second time period, determines a first recommendation value corresponding to each application included in the first application set based on the number of times used, wherein the first recommendation value is positively correlated with the number of times used, determines a second recommendation value corresponding to each application included in the first application set based on the last time used, wherein the difference between the current time and the last time used is negatively correlated with the second recommendation value, and makes application recommendations for the applications included in the first application set based on the first recommendation value and the second recommendation value. Compared to Figure 1The application recommendation method shown in this embodiment also determines a first recommendation value based on the number of times used, and determines a second recommendation value based on the last time used. Application recommendations are made based on the first recommendation value and the second recommendation value, which can avoid using a single and fixed recommendation strategy, improve the accuracy of application recommendations, and better meet user needs.

[0109] See also Figure 7 , Figure 7 The following is a flow chart of the application recommendation method provided by an embodiment of the present application. Figure 7 The process shown is described in detail, and the application recommendation method may specifically include the following steps:

[0110] Step S310: Determine at least one historical time period corresponding to the first time period to which the current time belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time.

[0111] Step S320: Determine a first application set consisting of application programs used in the at least one historical time period, and determine the number of times each application program included in the first application set has been used in the at least one historical time period.

[0112] Step S330: Determine a second application set consisting of application programs used in the second time period, and determine the last usage time corresponding to each application program included in the second application set in the second time period.

[0113] For the detailed description of step S310 to step S320, please refer to step S110 to step S120, which will not be repeated here.

[0114] Step S340: Determine the recommendation reference factors corresponding to each of the applications included in the first application set, wherein the recommendation reference factors include one or a combination of commercialization weighting, festival weighting, and usage scenario weighting.

[0115] It is understandable that when recommending applications to electronic devices, this embodiment mainly selects applications from the applications that the electronic device is used to use (i.e., applications that have been used in the historical time period) for recommendation, and integrates the applications that the electronic device has recently used (i.e., applications that have been used in the second time period) as auxiliary judgment conditions for application recommendation. In addition, this embodiment can also set other recommendation reference factors as auxiliary judgment conditions for application recommendation to further improve the accuracy of application recommendation.

[0116] Optionally, the recommendation reference factors may include one or a combination of commercialization weighting, festival weighting and usage scenario weighting.

[0117] Commercial weighting can be understood as the correlation between an application and commercial interests. For example, some applications may be given a higher commercial weight because of their advertising revenue, number of paying users, or higher correlation with other commercial activities. Commercial weighting can enable these applications to obtain higher recommendation priority when making application recommendations.

[0118] Festival weighting can be understood as the relevance of an application to a specific festival or event. For example, during the Spring Festival, applications related to the Spring Festival (such as red envelope assistants, Spring Festival customs introductions, etc.) may be given a higher festival weight. Festival weighting can allow these applications to receive a higher recommendation priority when making application recommendations, increasing their chances of being recommended during the festival.

[0119] Among them, the usage scenario weighting can be understood as the degree of match between the application and the current user environment or needs. For example, when the user is traveling, travel-related applications (such as map navigation, hotel reservations, etc.) may be given a higher usage scenario weight. The usage scenario weighting can make these applications get a higher recommendation priority when making application recommendations.

[0120] In this embodiment, the application recommendation factors corresponding to the applications included in the first application set may be determined. For example, the commercial weighting corresponding to the applications included in the first application set may be determined, and the commercial weighting and holiday weighting corresponding to the applications included in the first application set may be determined, which are not limited here.

[0121] Step S350: Based on the number of uses, the last use time and the recommendation reference factor, recommend applications included in the first application set.

[0122] In this embodiment, after determining the number of times each application included in the first application set is used in at least one historical time period, determining the last time of use of each application included in the second application set in the second time period, and determining the recommendation reference factor corresponding to each application included in the first application set, application recommendations can be made for the applications included in the first application set based on the number of times used, the last time of use, and the recommendation reference factor.

[0123] In some embodiments, after determining the number of times each application included in the first application set is used within at least one historical time period, determining the last time each application included in the second application set is used within the second time period, and determining a recommendation reference factor corresponding to each application included in the first application set, the applications included in the first application set can be scored based on the number of times used, the last time used, and the recommendation reference factor to obtain a score corresponding to each application included in the first application set, and application recommendations can be made for the applications included in the first application set based on the score corresponding to each application included in the first application set.

[0124] As an implementable manner, the electronic device may pre-set and store an application recommendation algorithm. When the number of uses, the last use time, and the recommendation reference factors are determined, the number of uses, the last use time, and the recommendation reference factors may be calculated through the application recommendation algorithm to obtain the application recommendation result, and based on the application recommendation result, application recommendations may be made for the applications included in the first application set.

[0125] As another feasible method, the electronic device may pre-set and store application recommendation rules, and after determining the number of uses, the last use time and the recommendation reference factors, the application recommendation rules, the number of uses, the last use time and the recommendation reference factors may be used to make application recommendations for the applications included in the first application set.

[0126] An application recommendation method provided by an embodiment of the present application determines at least one historical time period corresponding to the first time period to which the current time belongs, and determines a second time period before the current time, wherein the end time of the second time period is the current time, determines a first application set consisting of applications used in at least one historical time period, and determines the number of times each application included in the first application set is used in at least one historical time period, determines a second application set consisting of applications used in a second time period, and determines the last time each application included in the second application set is used in the second time period, determines recommendation reference factors corresponding to each application included in the first application set, wherein the recommendation reference factors include one or a combination of commercialization weighting, holiday weighting, and usage scenario weighting, and makes application recommendations for applications included in the first application set based on the number of uses, the last time of use, and the recommendation reference factors. Compared to Figure 1 The application recommendation method shown in this embodiment also makes application recommendations based on commercialization weighting, festival weighting and / or usage scenario weighting, thereby further improving the accuracy of application recommendations.

[0127] See also Figure 8 , Figure 8 The module block diagram of the application recommendation device provided by an embodiment of the present application is shown below. Figure 8 As shown in the block diagram, the application recommendation device 200 includes: a time period determination module 210, a usage number determination module 220, a last usage time determination module 230 and an application recommendation module 240, wherein:

[0128] The time period determination module 210 is used to determine at least one historical time period corresponding to the first time period to which the current time belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time.

[0129] The usage count determination module 220 is configured to determine a first application set consisting of application programs used in the at least one historical time period, and determine the usage counts of each application program included in the first application set in the at least one historical time period.

[0130] The last use time determination module 230 is used to determine a second application set consisting of applications used in the second time period, and determine the last use time corresponding to each application included in the second application set in the second time period.

[0131] Furthermore, the last use time determination module 230 includes: a foreground opening time determination submodule and a last use time determination submodule, wherein:

[0132] The foreground opening time determination submodule is used to determine the time when each application included in the second application set was last opened in the foreground within the second time period.

[0133] The last use time determination submodule is used to determine the time when each application included in the second application set was last opened in the foreground as the last use time corresponding to each application included in the second application set.

[0134] The application recommendation module 240 is used to recommend the application programs included in the first application program set based on the number of uses and the last use time.

[0135] Furthermore, the application recommendation module 240 includes: a first recommendation value determination submodule, a second recommendation value determination submodule and a first application recommendation submodule, wherein:

[0136] The first recommendation value determination submodule is used to determine, based on the number of uses, a first recommendation value corresponding to each application included in the first application set, wherein the first recommendation value is positively correlated with the number of uses.

[0137] The second recommendation value determination submodule is used to determine the second recommendation value corresponding to each application included in the first application set based on the last usage time, wherein the difference between the current time and the last usage time is negatively correlated with the second recommendation value.

[0138] Furthermore, the second recommendation value determination submodule includes: a first determination unit and a second determination unit, wherein:

[0139] The first determination unit is used to determine a second recommendation value corresponding to an application included in the second application set according to a last usage time corresponding to the application included in the second application set if there is an application included in the second application set in the first application set.

[0140] A second determination unit is used to determine a second recommendation score corresponding to an application not included in the second application set if there is an application in the first application set that is not included in the second application set, wherein the second recommendation value corresponding to the application included in the second application set is greater than the second recommendation value corresponding to the application not included in the second application set.

[0141] The first application recommendation submodule is used to recommend applications included in the first application set based on the first recommendation value and the second recommendation value.

[0142] Furthermore, the first application recommendation submodule includes: a weighted application recommendation score determination unit and an application recommendation unit, wherein:

[0143] The weighted application recommendation score determining unit is used to determine the weighted application recommendation score corresponding to each application included in the first application set based on the application recommendation score and the score weighting coefficient.

[0144] The application recommendation unit is used to recommend the application programs included in the first application program set in descending order of weighted application recommendation scores.

[0145] Further, the application recommendation unit includes: a first application recommendation subunit, wherein:

[0146] The first application recommendation subunit is used to determine, in descending order of weighted application recommendation scores, applications ranked before a preset order from among the applications included in the first application set for application recommendation.

[0147] Furthermore, the application recommendation unit includes: an application list generation subunit and a second application recommendation subunit, wherein:

[0148] The application list generating subunit is used to generate an application list based on the application programs included in the first application program set in descending order of the weighted application recommendation scores.

[0149] The second application recommendation subunit is used to display the application list.

[0150] Further, the application recommendation unit includes: an application filtering subunit and a third application recommendation subunit, wherein:

[0151] The application filtering subunit is used to filter the applications in the application blacklist from the applications included in the first application set to obtain the filtered applications.

[0152] The third application recommendation subunit is used to make application recommendations to the filtered application programs in descending order of weighted application recommendation scores.

[0153] Furthermore, the application recommendation module 240 includes: a recommendation reference factor determination submodule and a second application recommendation submodule, wherein:

[0154] The recommendation reference factor determination submodule is used to determine the recommendation reference factors corresponding to each application included in the first application set, wherein the recommendation reference factors include one or a combination of commercialization weighting, festival weighting and usage scenario weighting.

[0155] The second application recommendation submodule is used to recommend the application programs included in the first application program set based on the number of uses, the last use time and the recommendation reference factor.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0157] In several embodiments provided in the present application, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0158] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0159] See also Fig. 9 , which shows a structural block diagram of an electronic device 100 provided in an embodiment of the present application. The electronic device 100 may be an electronic device capable of running applications, such as a smart phone, a tablet computer, an e-book, etc. The electronic device 100 in the present application may include one or more of the following components: a processor 110, a memory 120, and one or more applications, wherein the one or more applications may be stored in the memory 120 and configured to be executed by one or more processors 110, and the one or more programs are configured to execute the method described in the aforementioned method embodiment.

[0160] Among them, the processor 110 may include one or more processing cores. The processor 110 uses various interfaces and lines to connect various parts of the entire electronic device 100, and executes various functions and processes data of the electronic device 100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 120, and calling data stored in the memory 120. Optionally, the processor 110 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 110 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented separately through a communication chip.

[0161] The memory 120 may include a random access memory (RAM) or a read-only memory (ROM). The memory 120 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing functions (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (such as a phone book, audio and video data, chat record data) created by the electronic device 100 during use.

[0162] See also Fig.10 , which shows a structural block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable medium 300 stores program codes, which can be called by a processor to execute the method described in the above method embodiment.

[0163] The computer readable storage medium 300 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 300 includes a non-transitory computer-readable storage medium. The computer readable storage medium 300 has storage space for program code 310 that performs any method steps in the above method. These program codes can be read from or written to one or more computer program products. The program code 310 can be compressed, for example, in an appropriate form.

[0164] In summary, the application recommendation method, device, electronic device and storage medium provided in the embodiments of the present application first determine at least one historical time period corresponding to the first time period to which the current event belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time, and then determine a first application set consisting of applications used in at least one historical time period, and determine the number of times each application included in the first application set is used in at least one historical time period, determine a second application set consisting of applications used in the second time period, and determine the last time each application included in the second application set is used in the second time period, and finally, based on the number of times and the last time of use, recommend applications to the applications included in the first application set, so that application recommendations are made by integrating the most recently used applications and the applications habitually used in each time period, thereby avoiding the use of a single and fixed recommendation strategy, improving the accuracy of application recommendations, and better meeting user needs.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An application recommendation method, characterized in that: The method comprises: Determine at least one historical time period corresponding to the first time period to which the current time belongs, and determine a second time period before the current time, wherein the end time of the second time period is the current time; Determine a first application set consisting of application programs used in the at least one historical time period, and determine the number of times each application program included in the first application set is used in the at least one historical time period; Determine a second application set consisting of applications used in the second time period, and determine the last usage time corresponding to each application included in the second application set in the second time period; Based on the number of uses and the last use time, application recommendations are made for the application programs included in the first application program set.

2. The method according to claim 1, characterized in that The recommending application programs included in the first application program set based on the number of times of use and the last time of use includes: Based on the number of times of use, determining a first recommendation value corresponding to each application included in the first application set, wherein the first recommendation value is positively correlated with the number of times of use; Based on the last usage time, determining a second recommendation value corresponding to each application included in the first application set, wherein the difference between the current time and the last usage time is negatively correlated with the second recommendation value; Based on the first recommendation value and the second recommendation value, application recommendations are made for the application programs included in the first application program set.

3. The method according to claim 2, characterized in that The first recommendation value includes an application recommendation score, and the second recommendation value includes a score weighting coefficient.

4. The method according to claim 3, characterized in that The recommending application programs included in the first application program set based on the first recommendation value and the second recommendation value includes: Determining, based on the application recommendation score and the score weighting coefficient, a weighted application recommendation score corresponding to each application included in the first application set; The application programs included in the first application program set are recommended in descending order of the weighted application recommendation scores.

5. The method according to claim 4, characterized in that The recommending the applications included in the first application set according to the weighted application recommendation scores in descending order includes: According to the weighted application recommendation scores from high to low, an application program ranked before a preset order is determined from the applications included in the first application program set for application recommendation.

6. The method according to claim 4, characterized in that The recommending the applications included in the first application set according to the weighted application recommendation scores in descending order includes: Generating an application list based on the applications included in the first application set in descending order of weighted application recommendation scores; The application list is displayed.

7. The method according to claim 4, characterized in that The recommending the applications included in the first application set according to the weighted application recommendation scores in descending order includes: Filtering the applications in the application blacklist from the applications included in the first application set to obtain filtered applications; The filtered application programs are recommended in descending order of the weighted application recommendation scores.

8. The method according to any one of claims 2 to 7, characterized in that: The determining, based on the last usage time, a second recommendation value corresponding to each application program included in the first application program set includes: If there is an application included in the second application set in the first application set, determining a second recommendation value corresponding to the application included in the second application set according to the last usage time corresponding to the application included in the second application set; If there is an application in the first application set that is not included in the second application set, the second recommendation score corresponding to the application not included in the second application set is determined, wherein the second recommendation value corresponding to the application included in the second application set is greater than the second recommendation value corresponding to the application not included in the second application set.

9. The method according to any one of claims 1 to 7, characterized in that: The determining the last usage time corresponding to each application included in the second application set within the second time period includes: Determine the time when each of the applications included in the second application set was last opened in the foreground within the second time period; The time when each application included in the second application set was last opened in the foreground is determined as the last usage time corresponding to each application included in the second application set.

10. The method according to any one of claims 1 to 7, characterized in that: The recommending the application programs included in the first application program set based on the number of times of use and the last time of use further includes: Determining recommendation reference factors corresponding to each of the applications included in the first application set, wherein the recommendation reference factors include one or a combination of commercialization weighting, festival weighting, and usage scenario weighting; Based on the number of uses, the last use time, and the recommendation reference factor, application recommendations are made for the application programs included in the first application program set.

11. An application recommendation device, characterized in that: The device comprises: a time period determination module, configured to determine at least one historical time period corresponding to a first time period to which a current time belongs, and determine a second time period before the current time, wherein an end time of the second time period is the current time; A usage count determination module, configured to determine a first application set consisting of application programs used in the at least one historical time period, and determine a usage count corresponding to each application program included in the first application set in the at least one historical time period; A last use time determination module, configured to determine a second application set consisting of applications used in the second time period, and determine a last use time corresponding to each application included in the second application set in the second time period; The application recommendation module is used to recommend the application programs included in the first application program set based on the number of uses and the last use time.

12. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is coupled to the processor, the memory stores instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 10.

Images