Content acquisition method and device and computing equipment

Abstract

The invention discloses a content acquisition method and device and computing equipment. The method comprises the following steps: determining a second object concerned by a first object; obtaining associated information of the first object, and generating an extraction duration matched with the first object based on the associated information; determining an extraction time period corresponding to the first object according to the extraction duration and the current time; and obtaining published content of the second object with the published time in the extraction time period. According to the scheme, the quantity of the obtained published contents can be effectively reduced, the published content obtaining efficiency is improved, and the phenomena of long-time waiting of the first object and service timeout are reduced; the extraction time period is determined according to the current time and the extraction duration, and the extraction duration is generated according to the associated information of the first object, so that the extraction time period of the first object is highly matched with the first object, and the rationality of the extraction time period is improved; and the problems of content release efficiency reduction caused by too long extraction time and insufficient content release amount caused by too short extraction time are avoided.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a content acquisition method, apparatus, computing device, computer storage medium, and computer program product. Background Art

[0002] With the continuous development of science and technology and society, the emergence of various Internet services has greatly enriched people's work and life. Among them, some Internet services provide dynamic display functions, through which the objects can be shown the content (i.e., dynamics) published by other objects (hereinafter referred to as the objects of interest) that they follow.

[0003] However, the inventors found in the implementation process that the prior art has the following defects: when obtaining the published content of the follower of any object (such as object A), the prior art obtains a certain number of published content recently published by each follower, aggregates and sorts the obtained published content of each follower, and then provides it to object A. However, when object A has many followers, the total amount of published content obtained by the follower is large, and the aggregation and sorting time is long, so object A needs to wait for a long time, or it is easy to cause server timeout, thereby reducing the experience of object A. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a content acquisition method, apparatus, computing device, computer storage medium and computer program product that overcome the above problems or at least partially solve the above problems.

[0005] According to a first aspect of the present application, a content acquisition method is provided, comprising:

[0006] determining a second object of interest to the first object;

[0007] Acquire association information of the first object, and generate an extraction duration matching the first object based on the association information;

[0008] Determine an extraction time period corresponding to the first object according to the extraction duration and the current time;

[0009] Acquire the published content of the second object whose publishing time is within the extraction period.

[0010] In an optional implementation, the acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes:

[0011] Obtain the number of attention objects of the first object, and calculate the attention amount index of the first object using the number of attention objects;

[0012] An extraction duration matching the first object is generated based on the attention volume indicator.

[0013] In an optional implementation, the acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes:

[0014] Acquire the publishing frequency of the objects associated with the first object, and calculate the frequency index of the first object using the publishing frequency;

[0015] An extraction duration matching the first object is generated based on the frequency index.

[0016] In an optional implementation, the acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes:

[0017] Obtaining an activity function and a current time of the platform where the first object is located, and calculating a time period index of the first object in combination with the activity function and the current time;

[0018] An extraction duration matching the first object is generated based on the time period indicator.

[0019] In an optional implementation, the calculating the time period index of the first object in combination with the activity function and the current time includes:

[0020] Constructing an integral function of the activity function over a variable time interval; wherein the starting point of the variable time interval is a starting point variable, and the ending point of the variable time interval is the current time;

[0021] Establishing an equation relationship between the integral function and a preset integral value;

[0022] Solving the equation relationship to obtain the starting point variable value;

[0023] The time period index of the first object is calculated according to the difference between the current time and the starting point variable value.

[0024] In an optional implementation, generating an extraction duration matching the first object based on the association information includes:

[0025] Calculate at least one indicator of the first object based on the association information;

[0026] A correction constant is generated, and an extraction duration matching the first object is generated by combining the correction constant and the at least one indicator.

[0027] In an optional implementation, after acquiring the published content of the second object whose publishing time is within the extraction period, the method further includes:

[0028] Determine the amount of published content to obtain;

[0029] Count the number of times the number of published content within a preset time window does not reach a preset number threshold;

[0030] The correction constant is adjusted according to the number of times.

[0031] According to a second aspect of the present application, a content acquisition device is provided, including:

[0032] An object determination module, used to determine a second object that the first object pays attention to;

[0033] A duration generation module, configured to obtain the associated information of the first object, and generate an extraction duration matching the first object based on the associated information;

[0034] A time period determination module, used to determine an extraction time period corresponding to the first object according to the extraction duration and the current time;

[0035] The content acquisition module is used to acquire the published content of the second object whose publishing time is within the extraction period.

[0036] In an optional implementation, the duration generation module is used to: obtain the number of attention objects of the first object, and calculate the attention amount index of the first object using the number of attention objects;

[0037] An extraction duration matching the first object is generated based on the attention volume indicator.

[0038] In an optional implementation, the duration generation module is used to: obtain the publishing frequency of the objects associated with the first object, and calculate the frequency index of the first object using the publishing frequency;

[0039] An extraction duration matching the first object is generated based on the frequency index.

[0040] In an optional implementation, the duration generation module is used to: obtain an activity function of the platform where the first object is located and a current time, and calculate a time period index of the first object in combination with the activity function and the current time;

[0041] An extraction duration matching the first object is generated based on the time period indicator.

[0042] In an optional implementation, the duration generation module is used to: construct an integral function of the activity function over a variable time interval; wherein the starting point of the variable time interval is a starting point variable, and the ending point of the variable time interval is the current time;

[0043] Establishing an equation relationship between the integral function and a preset integral value;

[0044] Solving the equation relationship to obtain the starting point variable value;

[0045] The time period index of the first object is calculated according to the difference between the current time and the starting point variable value.

[0046] In an optional implementation, the duration generation module is used to: calculate at least one indicator of the first object based on the association information;

[0047] A correction constant is generated, and an extraction duration matching the first object is generated by combining the correction constant and the at least one indicator.

[0048] In an optional implementation, the device further includes an adjustment module, which is used to determine the quantity of published content acquired;

[0049] Count the number of times the number of published content within a preset time window does not reach a preset number threshold;

[0050] The correction constant is adjusted according to the number of times.

[0051] According to a third aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0052] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned content acquisition method.

[0053] According to a fourth aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned content acquisition method.

[0054] According to a fifth aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned content acquisition method.

[0055] The embodiment of the present application generates a corresponding extraction time period for the first object, and uses the extraction time period as a screening condition to screen the published content of the second object, thereby effectively reducing the number of published contents obtained, improving the efficiency of obtaining published content, and reducing the long waiting time and service timeout phenomenon of the first object; moreover, the extraction time period in the embodiment of the present application is determined according to the current time and the extraction duration, and the extraction duration is generated according to the associated information of the first object, so that the extraction time period of the first object is highly matched with the first object, improving the rationality of the extraction time period, avoiding the reduction of the efficiency of publishing content due to the extraction time period being too long and avoiding the insufficient number of published content due to the extraction time period being too short.

[0056] The embodiment of the present application generates an extraction duration that matches the first object based on an attention quantity index. The attention quantity index can reflect the number of objects followed by the first object. The number of objects followed by the first object will affect the number of published contents acquired per unit time. Therefore, generating an extraction duration that matches the first object based on the attention quantity index can improve the accuracy of determining the extraction duration.

[0057] The embodiment of the present application generates an extraction duration that matches the first object based on a frequency indicator. The frequency indicator can reflect the publishing frequency of objects associated with the first object. Different objects have different publishing frequencies, and the publishing frequency of an object will affect the amount of published content acquired per unit time. Therefore, generating an extraction duration that matches the first object based on the frequency indicator can make the extraction duration compatible with the actual object publishing frequency, thereby improving the accuracy of determining the extraction duration.

[0058] This application obtains the extraction duration of the first object match based on the time period indicator. The time period indicator can reflect the time required for the first object to obtain a certain amount of published content in the time period at the current time. Since the publication frequency of the object is different in different time periods, the number of published content obtained per unit time will also be different in different time periods, and the time required to obtain the same amount of published content will also be different. Therefore, obtaining the extraction duration of the first object match based on the time period indicator can improve the adaptability of the extraction duration to the current activity status and improve the accuracy of determining the extraction duration.

[0059] The embodiment of the present application determines the starting point variable value by solving the integral function of the activity function, and determines the time period index according to the difference between the current time and the starting point variable value, so that the determined time period index can be adapted to the actual activity distribution, thereby improving the determination accuracy of the time period index.

[0060] The embodiment of the present application combines a correction constant and at least one indicator to determine an extraction period, thereby improving the accuracy of determining the extraction period.

[0061] In the embodiment of the present application, when the number of published contents screened out based on the extraction period is less than a preset quantity threshold, the extraction period is expanded by adjusting the correction constant so that the number of published contents extracted meets the quantity requirement, thereby further ensuring that the number of published contents returned to the first object meets the quantity requirement on the basis of ensuring the content acquisition efficiency.

[0062] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0064] Figure 1 A schematic diagram of an operating environment provided for implementing at least one embodiment of the present application is shown;

[0065] Figure 2 A schematic diagram of a process of obtaining content provided in the first embodiment of the present application is shown;

[0066] Figure 3 A schematic diagram of a flow chart of a method for obtaining a feed stream provided in Embodiment 1 of the present application is shown;

[0067] Figure 4 A flow chart of a content acquisition method provided in Embodiment 2 of the present application is shown;

[0068] Figure 5 A schematic diagram of an activity curve provided in Example 2 of the present application is shown;

[0069] Figure 6 A schematic diagram showing a flow chart of a content acquisition method provided in Embodiment 3 of the present application is shown;

[0070] Figure 7 A schematic diagram of the structure of a content acquisition device provided in Embodiment 4 of the present application is shown;

[0071] Figure 8 A schematic diagram of the structure of a computing device provided in Embodiment 5 of the present application is shown. DETAILED DESCRIPTION

[0072] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0073] It should be noted that the object information and object data involved in the embodiments of the present application (such information and data include but are not limited to information and data used for analysis, information and data stored, information and data displayed, etc.) are all authorized or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entrances are provided for selection of authorization or rejection.

[0074] Figure 1 The present invention is applicable to an application environment including, but not limited to, a client 2 , a server 4 , and a network 6 .

[0075] in:

[0076] The server 4 may be composed of a single or multiple computing devices. The multiple computing devices may include virtualized computing instances. Virtualized computing instances may include virtual machines, such as simulations of computer systems, operating systems, servers, etc. The computing device may load a virtual machine based on a virtual image and / or other data defining specific software (e.g., operating system, dedicated application, server) for simulation. As the demand for different types of processing services changes, different virtual machines may be loaded and / or terminated on one or more computing devices. A hypervisor may be implemented to manage the use of different virtual machines on the same computing device.

[0077] The server 4 may be configured to communicate with the client 2 and the like via a network 6. The network 6 includes various network devices, such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and / or the like.

[0078] The network 6 may include physical links, such as coaxial cable links, twisted pair cable links, optical fiber links, combinations thereof, etc., or wireless links, such as cellular links, satellite links, Wi-Fi links, etc.

[0079] The server 4 can provide storage, reading, downloading, writing, querying, deleting and other services, such as providing static resource download services to clients through multiple domain names.

[0080] Client 2 can be running Windows, AndroidTM ) or IOS and other operating systems, such as smart phones, tablet devices, laptop computers, virtual reality devices, gaming devices, set-top boxes, car terminals, smart TVs. Based on the above operating systems, various applications, such as browsers, can be run.

[0081] Embodiment 1

[0082] Figure 2 The flowchart of a content acquisition method provided in the first embodiment of the present application is shown. The content acquisition method provided in the embodiment of the present application can be executed on a server or the like.

[0083] Specifically, Figure 2 As shown, the method comprises the following steps:

[0084] Step S210: determining a second object that the first object is interested in.

[0085] The attention mentioned in this application is specifically content subscription, that is, establishing a content subscription relationship through corresponding operations or strategies, so that the content published by the producer is provided to the subscriber. For example, object A follows object B, indicating that object A has established a content subscription relationship with object B, and the content published by object B will be provided to object A. Among them, the operation includes but is not limited to: touching the "Follow" button, touching the "Favorite" button, touching the "Like" button, etc.; or, establishing a content subscription relationship through corresponding subscription strategies, for example, the system delivers the content published by object B to object A through the corresponding content delivery strategy, thereby establishing a content subscription relationship in which object A follows object B.

[0086] Among them, the first object is the object to which the published data of the object of its attention is to be displayed, and it can also be the end that initiates the request to obtain the content published by the object of attention. The object followed by the first object is the second object. The first object can also be called a subscriber, and the second object can also be called a publisher or producer. For example, in a video platform, object A follows three objects B, C, and D. A initiates a dynamic acquisition request for the objects of attention, then A is the first object, and B, C, and D are the second objects. In the actual implementation process, all or part of the second objects followed by the first object can be determined.

[0087] In an optional implementation, when the number of objects followed by object A is small, the data volume of the published content generated by all the objects followed by object A is small, and the processing efficiency of aggregation, sorting, etc. of the published content will not be significantly reduced. Therefore, this implementation selects the first object whose number of followed objects reaches a set threshold (such as 500) from the object that initiates the request to obtain the published content, and executes subsequent steps. That is, this implementation optimizes the process of obtaining the published content for the first object with a large number of followed objects, and for objects with a small number of followed objects (such as objects whose number of followed objects does not reach the set threshold), the processing method in the prior art can still be used.

[0088] Step S220: Acquire the association information of the first object, and generate an extraction duration matching the first object based on the association information.

[0089] The prior art is to extract the most recently published N (e.g., 20) pieces of content for each second object in a fixed manner. Different from the prior art, the present application generates an extraction duration that matches the first object for each first object. The matching extraction durations may also differ depending on the association information of the first object. The extraction duration matched by the first object limits the length of the extraction period for subsequent published content. The longer the extraction duration, the longer the extraction period for the published content; conversely, the shorter the extraction duration, the shorter the extraction period for the published content. Among them, the association information of the first object is information related to the first object. For example, the association information of the first object may include the number of followers of the first object, etc.

[0090] Step S230: determining an extraction time period corresponding to the first object according to the extraction duration and the current time.

[0091] Specifically, the time period length of the extraction period is consistent with the extraction duration, and the end time of the extraction period is consistent with the current time. Therefore, the extraction period is the most recent time period, and the time period length is equal to the extraction duration.

[0092] Step S240: Acquire the published content of the second object whose publishing time is within the extraction period.

[0093] The extraction period corresponding to the first object is used as a screening condition, and the published content with a publishing time within the extraction period is screened out from any published content of the second object.

[0094] In an optional implementation, for any second object, the published contents with publishing time within the extraction period are first screened out. If the number of published contents screened out by a second object exceeds a preset number N, the N published contents with the latest publishing time are obtained.

[0095] In another optional implementation, for any second object, a preset number N of published contents recently published by the second object are first extracted, and the published contents with a publishing time within the extraction period are screened out from the N published contents.

[0096] After obtaining the published content of each second object, the published content of each second object can be aggregated, and the published content can be sorted in order of publishing time from late to early, and the corresponding published content of the first object is returned according to the sorting result. It can be seen that the application can effectively reduce the published content obtained from the second object by using the extraction time period as the screening condition, reduce the processing volume of subsequent aggregation and sorting of published content, and improve the efficiency of obtaining published content.

[0097] like Figure 3 As shown in the figure, the acquisition process of the feed stream (the information stream that continuously updates and presents content to users) is taken as an example: when it is necessary to provide the feed stream to the object U (i.e. the first object), a page of the feed stream needs to be pulled and provided to the object U. The one-page feed stream contains a first amount of dynamic data. Then the UP owner followed by the object U (i.e. the second object, which is the producer of the published content, Figure 3 The object A is UP A, UP B...UP N), and the time period P (the extraction period corresponding to the object A) is calculated by implementing steps S220 and S230. The dynamic data released by UP A, UP B...UP N in the time period P is further extracted, such as extracting the dynamic data A1 and A2 released by UP A; extracting the dynamic data B09 released by UP B; extracting the dynamic data N1 released by UP N, etc. After that, the aggregated and extracted dynamic data is sorted by release time and paginated (e.g., 20 dynamics are divided into one page) and returned to the object U.

[0098] It can be seen that in the content acquisition method provided in the embodiment of the present application, a corresponding extraction time period is generated for the first object, and the published content of the second object is screened based on the extraction time period as a screening condition, thereby effectively reducing the number of published content acquired, improving the efficiency of obtaining published content, and reducing the long waiting time and service timeout phenomenon of the first object; moreover, the extraction time period in the embodiment of the present application is determined according to the current time and the extraction duration, and the extraction duration is generated according to the associated information of the first object, so that the extraction time period of the first object is highly matched with the first object, thereby improving the rationality of the extraction time period, avoiding the reduction in the efficiency of publishing content due to the extraction time period being too long, and avoiding the insufficient number of published content due to the extraction time period being too short.

[0099] Embodiment 2

[0100] Figure 4The flowchart of a content acquisition method provided in the second embodiment of the present application is shown. The content acquisition method provided in the embodiment of the present application can be executed on a server or the like.

[0101] Specifically, Figure 4 As shown, the method comprises the following steps:

[0102] Step S410: determining a second object that the first object is interested in.

[0103] In the embodiment of the present application, the association information of at least one dimension of the first object is obtained, and at least one indicator of the first object is generated according to the association information of the at least one dimension, and then the extraction duration of the first object matching is determined according to the at least one indicator.

[0104] Step S420, obtaining the number of attention objects of the first object, and using the number of attention objects to calculate the attention quantity index of the first object.

[0105] The associated information in this step is specifically the number of objects of attention of the first object, which is also the number of second objects that the first object pays attention to. The number of objects of attention is used to calculate the attention index of the first object, and the attention index is positively correlated with the number of objects of attention. In the actual implementation process, the number of objects of attention that the first object pays attention to can be directly used as the attention index of the first object, or the number of objects of attention can be processed accordingly (such as normalization, rounding the number of objects of attention to a preset value, etc.) to obtain the attention index.

[0106] Step S430: Obtain the publication frequency of the objects associated with the first object, and calculate the frequency index of the first object using the publication frequency.

[0107] The associated information in this step is specifically the publishing frequency of the associated objects of the first object. The associated objects of the first object are objects that have an associated relationship with the first object, for example, they can be the second object that the first object follows, or other objects on the platform where the first object is located, etc. The publishing frequency of the object is specifically the frequency with which the object publishes corresponding dynamic content, for example, the publishing frequency can be the number of dynamic content published per unit time.

[0108] In an optional implementation, the frequency index may be calculated by combining one or more of the following methods:

[0109] Calculation method one: obtain the publishing frequency of each second object that the first object follows, calculate the first average of the publishing frequency of each second object, and generate the frequency index of the first object based on the first average. Specifically, the publishing frequency of each object can be counted and stored in advance. After determining the second object that the first object follows, this step extracts the publishing frequency of each second object and calculates the average of the publishing frequencies of the extracted second objects. This average is the first average. The first average can reflect the content publishing frequency of the objects of the first object's attention as a whole, and then generate the frequency index of the first object based on the first average. For example, the first average can be directly used as the corresponding frequency index, or the first average can be processed accordingly to obtain the corresponding frequency index. In short, the frequency index is positively correlated with the first average.

[0110] Calculation method two: obtain the publishing frequency of multiple objects on the platform where the first object is located, calculate the second average of the publishing frequency of the multiple objects, and generate the frequency index of the first object based on the second average. Specifically, in this generation method, the publishing frequency of the objects in the platform where the first object is located as a whole is calculated, that is, the second average is the average of the publishing frequencies of multiple objects in the platform (for example, all or most of the objects in the platform). The frequency index of the first object is further generated based on the second average. For example, the second average can be directly used as the corresponding frequency index, or the second average can be processed accordingly to obtain the corresponding frequency index. In short, the frequency index is positively correlated with the second average.

[0111] Among them, the frequency index obtained by calculation method one is more consistent with the first object than that by calculation method two, and the subsequent extraction duration generated based on the frequency index is more accurate; compared with calculation method one, calculation method two does not need to calculate the corresponding average values ​​for different first objects, so calculation method two can save system resources compared with calculation method one.

[0112] Step S440, obtaining the activity function of the platform where the first object is located and the current time, and calculating the time period index of the first object in combination with the activity function and the current time.

[0113] The associated information in this step is the activity function of the platform where the first object is located and the current time. The activity function is a function of the overall release frequency of objects in the platform over time within a period (for example, every day). Figure 5 As shown in the figure, we can use minutes as the time granularity and take the average number of releases or the average release frequency of objects in the platform at each time point in a day as the activity at that time point, so as to obtain Figure 5The activity curve shown is then fitted according to the activity at each time point (i.e., the activity curve is fitted) to obtain the activity function. The current time can be the time of executing this step, or it can be any time point in the execution process of this method, such as the time of obtaining the published content acquisition request sent by the first object.

[0114] In an optional implementation manner, the time period index of the first object may be calculated by combining one or more of the following methods:

[0115] Calculation method 1: Construct an integral function of the activity function over a variable time interval; wherein the starting point of the variable time interval is the time variable, and the ending point of the variable time interval is the current time; establish an equation relationship between the integral function and the preset integral value; solve the equation relationship to obtain the starting point variable value; calculate the time period index of the first object based on the difference between the current time and the starting point variable value. Specifically, the activity function describes the number of publications of the object as a whole in the platform at each time point, and the integral of the activity function over the variable time interval represents the cumulative value of the number of publications of the object as a whole in the platform within the variable time interval. A preset integral value is pre-configured, and the preset integral value can be determined based on the number of published content returned to the first object. For example, the preset integral value can be K times (such as 1 times or 1.5 times, etc.) the number of published content. Establish an equation relationship between the integral function and the preset integral value, and the equation relationship is shown in Formula 1:

[0116]

[0117] Where S represents the preset integral value; T0 represents the current time; T x represents the starting point variable; f(t) represents the activity function; Represents the integral function of the activity function over the variable time interval.

[0118] Solve formula 1 to obtain the starting point variable T x The specific value of is the starting point variable value. Further calculation of T0-T x , that is, the difference between the current time and the starting point variable value is obtained, which represents the difference between [T x , T0] to accumulate the time required for S published content. x Get the time period index. For example, the difference can be directly used as the time period index, or the difference can be processed accordingly to obtain the time period index. In short, the time period index is positively correlated with the difference. For example, if T0 is 0:54, and the integral function value of the activity function in the time period [0:00, 0:54] reaches S=20, then T0-T xis 54 (minutes), so the time period index can be 54; if T0 is 5:10, the integral function value of the activity function in the [4:00, 5:10] time period reaches 20, then T0-T x is 70 (minutes), so the time period indicator can be 70.

[0119] Calculation method 2: Divide the time period (such as every day) into multiple time periods. For any time period, determine the activity function of the time period, calculate the integral value of the activity function in the time period; establish a mapping function between the time period index and the integral value, and the time period index is negatively correlated with the integral value. Determine the time period in which the current time is located and the integral value of the time period, determine the time period index corresponding to the integral value according to the mapping function, and use the time period index as the final time period index of the first object.

[0120] Specifically, the time period can be equally divided into multiple time periods, or the inflection point of the activity function can be used as the dividing point to divide the time period into multiple time periods. Further, the activity function is integrated in each time period to obtain the integral value of each time period. For example, the following formula can be used:

[0121] Formula 2 calculates the integral value of each segmentation period:

[0122]

[0123] Among them, S i represents the integral value of the i-th segmentation period; T i Indicates the start time of the i-th segmentation period; T i+1 It represents the start time of the i+1th segmentation period and the end time of the ith segmentation period. f(t) represents the activity function.

[0124] The mapping function between the pre-built time period indicator and the integral value can be shown as Formula 3:

[0125] KT=m*1 / S i (Formula 3)

[0126] Among them, S i represents the integral value of the ith segmentation period; KT represents the period index; m represents a constant.

[0127] In this step, the starting point variable value of calculation method one is obtained by solving the integral function, so that the time period index obtained based on the starting point variable value can be more accurate than that of calculation method two; the segmentation time periods in calculation method two and the integral values ​​of each segmentation time period can be obtained and stored in advance before the time period index is calculated, so that when calculating the time period index, the time period index can be quickly obtained by determining the segmentation time period and the mapping function in which the current time is located, thereby having higher processing efficiency than that of calculation method one.

[0128] Step S450: generating an extraction duration matching the first object based on the attention quantity indicator, the frequency indicator, and / or the time period indicator.

[0129] The extraction duration matching the first object may be generated according to a combination of one or more indicators among an attention amount indicator, a frequency indicator, and a time period indicator.

[0130] The attention volume index can reflect the number of objects followed by the first object. The number of objects followed by the first object will affect the number of published contents acquired per unit time. Therefore, generating an extraction duration matching the first object based on the attention volume index can improve the accuracy of determining the extraction duration.

[0131] The frequency index can reflect the publishing frequency of the objects associated with the first object. The publishing frequencies of different objects are different, and the publishing frequency of an object will affect the amount of published content obtained per unit time. Therefore, generating an extraction duration matching the first object based on the frequency index can make the extraction duration adapt to the actual object publishing frequency, thereby improving the accuracy of determining the extraction duration.

[0132] The time period indicator can reflect the length of time required for the first object to obtain a certain amount of published content within the time period at the current time. Since the publishing frequency of objects in different time periods is different, for example, the dynamic publishing frequency of objects on the platform in the evening period is higher than that in the morning period, the number of published content obtained per unit time will also be different in different time periods, and the length of time required to obtain the same number of published content will also be different. Therefore, obtaining the extraction duration of the first object match based on the time period indicator can improve the adaptability of the extraction duration to the current activity status and improve the accuracy of determining the extraction duration.

[0133] When the extraction time is generated by combining multiple indicators, the extraction time can be obtained based on the product or weighted sum of the multiple indicators. The present application does not limit the specific manner of generating the extraction time by combining multiple indicators.

[0134] Step S460: Acquire the published content of the second object whose publishing time is within the extraction period.

[0135] It can be seen that in the content acquisition method provided in the embodiment of the present application, an extraction duration matching the first object can be generated based on the attention index, frequency index, and / or time period index, thereby improving the adaptability of the generated extraction duration to the actual number of extracted published contents, and improving the accuracy of determining the extraction duration, so that the number of published contents obtained according to the extraction duration is within a reasonable range.

[0136] Embodiment 3

[0137] Figure 6 The flowchart of a content acquisition method provided in the third embodiment of the present application is shown. The content acquisition method provided in the embodiment of the present application can be executed on a server or the like.

[0138] Specifically, Figure 6 As shown, the method comprises the following steps:

[0139] Step S610: determining a second object that the first object is interested in.

[0140] Step S620: Acquire association information of the first object, and calculate at least one indicator of the first object based on the association information.

[0141] The index includes an attention amount index, a frequency index, and / or a time period index. The specific implementation process of generating at least one index based on the associated information can refer to the description in other embodiments, which will not be repeated here.

[0142] Step S630: Generate a correction constant, and combine the correction constant and at least one indicator to generate an extraction duration that matches the first object.

[0143] Specifically, the correction constant is used to correct at least one indicator to obtain an accurate extraction time. For example, the extraction time can be obtained by the following formula 4:

[0144] P=N*KP*KT*P0 (Formula 4)

[0145] Among them, P represents the extraction time; N represents the attention volume index; KP represents the frequency index; KT represents the time period index; P0 represents the correction constant.

[0146] Step S640: Acquire the published content of the second object whose publishing time is within the extraction period.

[0147] Step S650, determining whether the quantity of the obtained published content reaches a preset quantity threshold; if so, executing step S660; if not, executing step S670.

[0148] The preset number threshold is the minimum number of published contents returned to the first object at a time, for example, the preset number threshold may be 20, etc. If the number of published contents obtained reaches the preset number threshold, step S660 is executed; if the number of published contents obtained does not reach the preset number threshold, step S670 is executed.

[0149] Step S660: Process the obtained published content and return it to the first object.

[0150] The obtained published contents are aggregated and sorted and then returned to the first object for display in the first object.

[0151] Step S670, adjusting the correction constant.

[0152] If the number of published content obtained does not reach the preset number threshold, indicating that the current extraction period is short, the correction constant is adjusted, for example, the correction constant is increased, and step S630 is further executed to use the adjusted correction constant to re-calculate the new extraction period in combination with at least one indicator, and obtain the published content within the new extraction period. Optionally, during the adjustment process, the adjustment amplitude of this adjustment can be determined according to the number of adjustments of the current correction constant. The adjustment amplitude is positively correlated with the number of adjustments, that is, the more the current number of adjustments, the more adjustments have been made and the preset number threshold has not been reached. In this case, a larger adjustment amplitude can be used this time. For example, when adjusting the correction constant for the first time, it can be increased by 10%; when adjusting the correction constant for the second time, it can be increased by 20%, and so on.

[0153] In addition, as an optional implementation, when the number of adjustments of the correction constant reaches a preset number, the object information of the first object (such as the object name, object identifier, etc.) is added to the whitelist, and the extraction period is not used for restriction, but the content acquisition method in the prior art is adopted, for example, a certain number of published contents are extracted for each second object. In the actual implementation process, before executing step S620, it is first determined whether the object information of the first object hits the whitelist. If so, a certain number of published contents are extracted for each second object; if the whitelist is not hit, step S620 is executed. Among them, the preset number of times can also be 0 times, that is, when it is initially determined that the number of published contents does not reach the preset number threshold, the restriction of the extraction period is abandoned, and the published content acquisition method in the prior art is adopted.

[0154] In an optional implementation, after processing a content acquisition request of a first object, the correction constant is rolled back to the value before processing, that is, the adjustment of the correction constant in the process of processing a content acquisition request is only valid for the process of this content acquisition request, and the present application refers to the correction constant in the single request as the in-request adjustment. In the actual implementation process, the number of times the number of published content does not reach the preset number threshold in the preset time window can be counted every preset period, and the correction constant is adjusted according to the number. For example, the more times, the greater the adjustment range of the correction constant. For example, in the past week, the number of published content obtained has not reached the preset number threshold for P times, then the adjustment range of the correction constant can be +(P / L)%, L is the set constant. Subsequently, the correction constant is changed to [1+(N / L)]%*original correction constant, and the changed correction constant is applied to the processing of subsequent content acquisition requests, that is, the adjustment of the correction constant is a global adjustment.

[0155] It can be seen that in the content acquisition method provided in the embodiment of the present application, the extraction time period is determined by combining a correction constant and at least one indicator to improve the accuracy of determining the extraction time period; when the number of published content screened out based on the extraction time period is less than the preset quantity threshold, the extraction time period is expanded by adjusting the correction constant so that the number of published content extracted meets the quantity requirement, thereby further ensuring that the number of published content returned to the first object meets the quantity requirement on the basis of ensuring the content acquisition efficiency.

[0156] Embodiment 4

[0157] Figure 7 FIG. 4 shows a schematic diagram of the structure of a content acquisition device provided in Embodiment 4 of the present application. Figure 7 As shown, the device 700 includes: an object determination module 710 , a duration generation module 720 , a time period determination module 730 , and a content acquisition module 740 .

[0158] An object determination module 710 is used to determine a second object that the first object is interested in;

[0159] The duration generation module 720 is used to obtain the associated information of the first object and generate an extraction duration matching the first object based on the associated information;

[0160] A time period determination module 730, configured to determine an extraction time period corresponding to the first object according to the extraction duration and the current time;

[0161] The content acquisition module 740 is used to acquire the published content of the second object whose publishing time is within the extraction period.

[0162] In an optional implementation, the duration generation module 720 is used to: obtain the number of attention objects of the first object, and calculate the attention amount index of the first object using the number of attention objects;

[0163] An extraction duration matching the first object is generated based on the attention volume indicator.

[0164] In an optional implementation, the duration generation module 720 is used to: obtain the publishing frequency of the objects associated with the first object, and calculate the frequency index of the first object using the publishing frequency;

[0165] An extraction duration matching the first object is generated based on the frequency index.

[0166] In an optional implementation, the duration generation module 720 is used to: obtain an activity function of the platform where the first object is located and a current time, and calculate a time period index of the first object in combination with the activity function and the current time;

[0167] An extraction duration matching the first object is generated based on the time period indicator.

[0168] In an optional implementation, the duration generation module 720 is used to: construct an integral function of the activity function over a variable time interval; wherein the starting point of the variable time interval is a starting point variable, and the ending point of the variable time interval is the current time;

[0169] Establishing an equation relationship between the integral function and a preset integral value;

[0170] Solving the equation relationship to obtain the starting point variable value;

[0171] The time period index of the first object is calculated according to the difference between the current time and the starting point variable value.

[0172] In an optional implementation, the duration generation module 720 is used to: calculate at least one indicator of the first object based on the associated information;

[0173] A correction constant is generated, and an extraction duration matching the first object is generated by combining the correction constant and the at least one indicator.

[0174] In an optional implementation, the device further includes an adjustment module (not shown in the figure) for determining the number of published contents acquired;

[0175] Count the number of times the number of published content within a preset time window does not reach a preset number threshold;

[0176] The correction constant is adjusted according to the number of times.

[0177] It can be seen that in the content acquisition device provided in the embodiment of the present application, a corresponding extraction time period is generated for the first object, and the published content of the second object is screened based on the extraction time period as a screening condition, thereby effectively reducing the number of published content acquired, improving the efficiency of obtaining published content, and reducing the long waiting time and service timeout phenomenon of the first object; moreover, the extraction time period in the embodiment of the present application is determined according to the current time and the extraction duration, and the extraction duration is generated according to the associated information of the first object, so that the extraction time period of the first object is highly matched with the first object, thereby improving the rationality of the extraction time period, avoiding the reduction in the efficiency of publishing content due to the extraction time period being too long, and avoiding the insufficient number of published content due to the extraction time period being too short.

[0178] Embodiment 5

[0179] Figure 8 A schematic diagram of the structure of a computing device provided in Embodiment 5 of the present application is shown. The specific embodiments of the present application do not limit the specific implementation of the computing device.

[0180] like Figure 8 As shown, the computing device may include: a processor (processor) 802 , a communication interface (Communications Interface) 804 , a memory (memory) 806 , and a communication bus 808 .

[0181] The processor 802, the communication interface 804, and the memory 806 communicate with each other via a communication bus 808. The communication interface 804 is used to communicate with other devices such as a client or other server network elements. The processor 802 is used to execute a program 810, which can specifically execute the relevant steps in the above-mentioned content acquisition method embodiment for a computing device.

[0182] Specifically, the program 810 may include program codes, which include computer operation instructions.

[0183] The processor 802 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0184] The memory 806 is used to store the program 810. The memory 806 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The program 810 can be specifically used to enable the processor 802 to perform the operations in the above method embodiment.

[0185] Embodiment 6

[0186] Embodiment 6 of the present application provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to execute operations corresponding to the content acquisition method in any of the above method embodiments.

[0187] Embodiment 7

[0188] Embodiment 7 of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to execute operations corresponding to the content acquisition method in any of the above method embodiments.

[0189] To summarize, according to the computing device, computer storage medium and computer program product provided in this embodiment, a corresponding extraction period is generated for the first object, and the published content of the second object is filtered based on the extraction period as a filtering condition, thereby effectively reducing the number of published content obtained, improving the efficiency of obtaining published content, and reducing the long waiting time and service timeout phenomenon of the first object; moreover, the extraction period in the embodiment of the present application is determined according to the current time and the extraction duration, and the extraction duration is generated according to the associated information of the first object, so that the extraction period of the first object is highly matched with the first object, thereby improving the rationality of the extraction period, avoiding the reduction of the efficiency of publishing content due to the extraction period being too long and avoiding the insufficient number of published content due to the extraction period being too short.

[0190] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0191] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0192] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0193] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0194] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0195] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0196] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A content acquisition method, characterized in that: include: determining a second object of interest to the first object; Acquire association information of the first object, and generate an extraction duration matching the first object based on the association information; Determine an extraction time period corresponding to the first object according to the extraction duration and the current time; Acquire the published content of the second object whose publishing time is within the extraction period.

2. The method according to claim 1, characterized in that The acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes: Obtain the number of attention objects of the first object, and calculate the attention amount index of the first object using the number of attention objects; An extraction duration matching the first object is generated based on the attention volume indicator.

3. The method according to claim 1, characterized in that The acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes: Acquire the publishing frequency of the objects associated with the first object, and calculate the frequency index of the first object using the publishing frequency; An extraction duration matching the first object is generated based on the frequency index.

4. The method according to any one of claims 1 to 3, characterized in that The acquiring the associated information of the first object and generating an extraction duration matching the first object based on the associated information includes: Obtaining an activity function and a current time of the platform where the first object is located, and calculating a time period index of the first object in combination with the activity function and the current time; An extraction duration matching the first object is generated based on the time period indicator.

5. The method according to claim 4, characterized in that Calculating the time period index of the first object in combination with the activity function and the current time includes: Constructing an integral function of the activity function over a variable time interval; wherein the starting point of the variable time interval is a starting point variable, and the ending point of the variable time interval is the current time; Establishing an equation relationship between the integral function and a preset integral value; Solving the equation relationship to obtain the starting point variable value; The time period index of the first object is calculated according to the difference between the current time and the starting point variable value.

6. The method according to any one of claims 1 to 5, characterized in that The step of generating an extraction duration matching the first object based on the association information includes: Calculate at least one indicator of the first object based on the association information; A correction constant is generated, and an extraction duration matching the first object is generated by combining the correction constant and the at least one indicator.

7. The method according to claim 6, characterized in that After acquiring the published content of the second object whose publishing time is within the extraction period, the method further includes: Determine the amount of published content to obtain; Count the number of times the number of published content within a preset time window does not reach a preset number threshold; The correction constant is adjusted according to the number of times.

8. A content acquisition device, characterized in that: include: An object determination module, used to determine a second object that the first object pays attention to; A duration generation module, configured to obtain the associated information of the first object, and generate an extraction duration matching the first object based on the associated information; A time period determination module, used to determine an extraction time period corresponding to the first object according to the extraction duration and the current time; The content acquisition module is used to acquire the published content of the second object whose publishing time is within the extraction period.

9. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the content acquisition method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the content acquisition method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute an operation corresponding to the content acquisition method according to any one of claims 1 to 7.

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