An information processing method and apparatus, and an electronic device and a storage medium

Abstract

This application discloses an information processing method, apparatus, electronic device, and storage medium. The method includes: acquiring first interaction data of target information, the first interaction data being used to characterize the interaction frequency of the target information; determining second interaction data of the target information based on an information set; the second interaction data being used to characterize the interaction features of the target information relative to the information set; determining target interaction data of the target information based on the first interaction data and the second interaction data; and determining whether the target information can be classified into the information set based on the target interaction data.

Description

Technical Field

[0001] This application relates to the field of computer information processing technology, and in particular to an information processing method, apparatus, electronic device and storage medium. Background Technology

[0002] With the increasing amount of online information, newly released online information often lacks or has low levels of interaction data in its initial stages. Consequently, when determining the characteristic data of online information based on interaction data, significant errors can easily occur, leading to inaccurate predictions. Summary of the Invention

[0003] In a first aspect, embodiments of this application provide an information processing method, including:

[0004] Acquire first interaction data of the target information, wherein the first interaction data is used to characterize the interaction frequency of the target information;

[0005] The second interactive data is determined based on the information set; the second interactive data is used to characterize the interactive features of the target information relative to the information set.

[0006] Based on the first interaction data and the second interaction data, determine the target interaction data of the target information; based on the target interaction data, determine whether the target information can be categorized into the information set.

[0007] In some embodiments, the second interactive data for determining the target information based on the information set includes:

[0008] Determine the association between the target information and the information set;

[0009] The second interactive data is determined based on the aforementioned association.

[0010] In some embodiments, determining the association between the target information and the information set includes:

[0011] Obtain the first basic data of the target information and the second basic data of the information set;

[0012] The association relationship is determined based on the first basic data and the second basic data.

[0013] In some embodiments, determining the second interaction data based on the association includes:

[0014] Obtain the average interaction data of the information set;

[0015] The second interaction data is obtained based on the aforementioned relationship and the average interaction data.

[0016] In some embodiments, determining whether the target information can be categorized into the information set based on the target interaction data includes:

[0017] Based on the target interaction data and the first basic data, the first feature data of the target information is obtained;

[0018] Based on the first feature data, determine whether the target information meets the conditions of the information set.

[0019] In some embodiments, the target interaction data for determining the target information based on the first interaction data and the second data interaction includes:

[0020] Obtain the duration of the publication of the target information;

[0021] Based on the publication duration, a preset algorithm is used to combine the first interaction data and the second interaction data to obtain the target interaction data.

[0022] In some embodiments, the method further includes:

[0023] The interaction interval time is obtained based on the first interaction data;

[0024] Obtain the average interaction interval time of the information set, and obtain the attenuation coefficient based on the interaction interval time and the average interaction interval time;

[0025] The target interaction data is updated according to the attenuation coefficient, so as to update the first feature data based on the updated target interaction data.

[0026] Secondly, embodiments of this application also provide an information processing apparatus, including:

[0027] The acquisition module is configured to acquire first interactive data of target information, wherein the first interactive data is used to characterize the interaction frequency of the target information.

[0028] The determination module is configured to determine second interactive data of the target information based on an information set; the second interactive data is used to characterize the interactive features of the target information relative to the information set.

[0029] The data processing module is configured to determine target interaction data of the target information based on the first interaction data and the second interaction data; and to determine whether the target information can be classified into the information set based on the target interaction data.

[0030] Thirdly, this application also provides an electronic device, which includes at least a memory, a processor, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, any one of the method steps in the information processing method provided in any of the above embodiments is implemented.

[0031] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any one of the method steps in the information processing method provided in any of the above embodiments.

[0032] This application embodiment obtains first interaction data of target information and second interaction data of the target information relative to the information set, then determines the target interaction data of the target information based on the first interaction data and the second interaction data, and determines whether the target information can be classified into the information set based on the target interaction data, so that target information that meets the conditions of the information set can be quickly classified into the information set based on its own interaction frequency and interaction characteristics relative to the information set. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart of an information processing method provided in this application is shown;

[0035] Figure 2 A schematic diagram of the structure of an information processing apparatus provided in this application is shown;

[0036] Figure 3 A schematic diagram of the structure of an electronic device provided in this application is shown. Detailed Implementation

[0037] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0038] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0039] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0040] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0041] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0042] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0043] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0044] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0045] This application provides an information processing method that can be applied to electronic devices such as computer terminals and executed by the processor of the electronic device.

[0046] Figure 1 A flowchart illustrating the information processing method provided in this application embodiment. For example... Figure 1 As shown, the specific steps of the information processing method in this application embodiment include S100-S300.

[0047] S100, acquire first interaction data of the target information, the first interaction data being used to characterize the interaction frequency of the target information.

[0048] In this step, the target information can be newly published online information, such as newly published news, platform push notifications, public account articles, etc., which can be viewed or played by other non-publishing users via the internet. The first interaction data characterizes the frequency of interaction operations performed by non-publishing users on the target information. Here, interaction operations include the information interaction actions performed by non-publishing users on the target information at the current moment, such as browsing, liking, saving, commenting, sharing, etc., reflecting the degree of attention each user pays to the target information. In some specific applications, the first interaction data can be obtained by statistically analyzing the frequency of each interaction operation on the target information and assigning corresponding scores based on each interaction operation and its frequency.

[0049] It is understandable that when the target information in this embodiment is in a newly published state, the user does not interact with it and no interaction data is generated; or there are few interaction operations, few interaction data, and low interaction frequency.

[0050] S200, determine the second interactive data of the target information based on the information set; the second interactive data is used to characterize the interactive features of the target information relative to the information set.

[0051] In this step, the information set can be network information filtered according to preset conditions. These preset conditions can be set by technical personnel and are not limited here. For example, the frequency of user interactions such as browsing, liking, saving, commenting, and sharing network information reflects the level of user attention to that network information. Therefore, based on the frequency of user interactions with network information and the keyword attributes of the network information itself, the interaction status and basic data of each network information are scored. Network information that meets certain scoring conditions is then selected to form the information set. Then, the interaction data of the target information can be predicted based on the data in the information set to obtain the second interaction data of the target information relative to the information set. For example, when the information set is a group of network information with high scores, the basic data and interaction data of the network information in the information set can be obtained first. Then, based on these basic data and interaction data of the information set, combined with the basic data of the target information, the interaction status of the target information can be predicted to obtain the second interaction data of the target information.

[0052] In some embodiments, the second interactive data for determining the target information based on the information set in step S200 above can be implemented as steps S210-S220:

[0053] S210, determine the association between the target information and the information set;

[0054] S220, determine the second interactive data based on the association relationship.

[0055] In this embodiment, considering that the basic attribute data and interaction data of the network information in the information set can be obtained through data statistics and calculation, when determining the second interaction data of the target information, the association relationship between the target information and the information set can be determined first, and then the interaction data of the target information can be predicted based on the association relationship and the interaction data of each information set to obtain the second interaction data. In some practical applications, the association relationship in this embodiment can be determined based on the degree of content association between the target information and the network information in the information set, or it can be determined according to actual needs based on the information scope in the information set, the user information of the published information, the information demand classification, the target user group of the information, etc. This embodiment does not impose any limitations.

[0056] In some embodiments, to accurately determine the association between the target information and the information set, the following methods are included:

[0057] Obtain the first basic data of the target information and the second basic data of the information set;

[0058] The association relationship is determined based on the first basic data and the second basic data.

[0059] In this embodiment, the first basic data and the second basic data correspond to the target information and the network information in the information set, respectively. The first and second basic data can be obtained based on the content or attribute information of the network information; for example, they can be determined and obtained based on the content information, tag information, publisher's user information, geographical location information, category information, etc. Specifically, technicians can select the appropriate data according to actual needs. Then, based on the selected basic information of each dimension, corresponding scores and weights can be assigned according to the importance of each dimension to calculate the corresponding score for each piece of network information, thereby obtaining the first basic data corresponding to the target information and the second basic data corresponding to each piece of network information in the information set. In some specific applications, the second basic data of the information set may include the scores of each piece of network information, and may also include the average value calculated based on these scores, or other values ​​calculated based on these scores. This can be determined by technicians according to calculation needs and is not limited here.

[0060] In some embodiments, determining the second interaction data based on the association includes:

[0061] Obtain the average interaction data of the information set;

[0062] The second interaction data is obtained based on the aforementioned relationship and the average interaction data.

[0063] In this embodiment, to obtain the average interaction data of the information set, the interaction data corresponding to each piece of network information in the information set can be statistically calculated first, and then the average value can be calculated based on each piece of interaction data to obtain the average interaction data. For example, when determining the interaction data corresponding to each piece of network information, the frequency of interaction operations such as browsing, liking, collecting, commenting, and sharing by non-publishing users on each piece of network information at the current time can be used, and corresponding scores can be obtained.

[0064] S300, determine the target interaction data of the target information based on the first interaction data and the second interaction data; determine whether the target information can be classified into the information set based on the target interaction data.

[0065] In this step, after obtaining the first interaction data of the target information and its second interaction data relative to the information set, the target interaction data of the target information at the time of its release can be obtained. Since this target interaction data combines the second interaction data relative to the information set, if the first interaction data is low, the target information cannot be categorized into the information set based on the first interaction data. For example, when the information set is a group of highly popular online information, when the target information is newly released, user interactions such as browsing, liking, collecting, commenting, and sharing are few, and the first interaction data will be zero or very low. Therefore, the target information's popularity is low when determined based on the first interaction data, and it usually cannot be categorized into the information set. In this step, because the second interaction data has the interaction characteristics of the target information relative to the information set, after combining the first and second interaction data of the target information to obtain the target interaction data, even if the target information is newly released or has not been released for long, the target information that meets the conditions of the information set can be quickly categorized into the information set based on the target interaction data, and corresponding ranking and recommendations can be performed.

[0066] This application embodiment obtains first interaction data of target information and second interaction data of the target information relative to the information set, and then determines the target interaction data of the target information based on the first interaction data and the second interaction data. Based on the target interaction data, it determines whether the target information can be classified into the information set. This allows target information that meets the conditions of the information set to be quickly classified into the information set based on its own interaction frequency and interaction characteristics relative to the information set, thereby achieving the purpose of automatically and accurately recommending target information.

[0067] In some embodiments, determining whether the target information can be categorized into the information set based on the target interaction data includes:

[0068] Based on the target interaction data and the first basic data, the first feature data of the target information is obtained;

[0069] Based on the first feature data, determine whether the target information meets the conditions of the information set.

[0070] This embodiment aims to categorize eligible target information into the information set using target interaction data. In some specific applications, where the information set is network information filtered based on interaction status and basic data ratings, for target information, corresponding first feature data can be obtained by combining the target interaction data and the first basic data to obtain the target information's corresponding rating based on the interaction status and basic data. Then, it is determined whether the first feature data meets the conditions for filtering network information in the information set. If it does, the target information meets the conditions of the information set and can be categorized into the information set. For example, when filtering based on interaction status and basic data ratings, if network information with a score higher than 70 is selected to form the information set, and for target information, if the rating obtained based on the target interaction data and the first basic data is higher than 70, then the target information meets the conditions of the information set. Therefore, if the target information has just been published or has a short publication duration, it can be directly categorized into the information set, and subsequently, the target information can be recommended accordingly based on the display of the information set.

[0071] In some embodiments, the target interaction data for determining the target information based on the first interaction data and the second data interaction includes:

[0072] Obtain the duration of the publication of the target information;

[0073] Based on the publication duration, a preset algorithm is used to combine the first interaction data and the second interaction data to obtain the target interaction data.

[0074] This embodiment aims to obtain target interaction data by smoothly combining the first and second interaction data based on the publication duration of the target information using a preset algorithm. Since the first interaction data reflects the actual interaction state at the current moment and increases with the publication duration, it cannot predict and reflect the true characteristic data of the target information when the publication duration is short. The second interaction data, on the other hand, is the predicted interaction state of the target information relative to the information set. Therefore, by combining the first and second interaction data, the characteristic data of the target information can be predicted more accurately to quickly determine whether the target information meets the conditions of the information set. Therefore, to ensure a smooth transition from the target interaction data to the actual first interaction data as the publication duration increases, a corresponding smoothing coefficient can be set based on Bayesian theory and the publication duration of the target information. Using this coefficient to combine the first and second interaction data to obtain the target interaction data avoids large discrepancies in the target interaction data, achieving a smooth numerical transition and improving the reliability of the target interaction data.

[0075] The following examples illustrate the calculation methods and processes of the data in this application. In this embodiment, to obtain the target interaction data S, first interaction data V for the target information and second interaction data relative to the information set can be determined first. When statistically determining the first interaction data V for the target information, a score can be assigned based on the frequency of user interactions such as browsing, liking, collecting, commenting, and sharing. In some practical applications, when scoring based on each interaction operation and its frequency, technicians can assign corresponding scores to each interaction operation based on experience or actual needs, and then assign corresponding scores based on the statistically obtained interaction operations and their frequency, thereby obtaining the first interaction data V based on the scores.

[0076] In some practical applications, the initial basic data a0 and publication duration t0 of the target information can be further obtained. When determining the initial basic data a0, technical personnel can identify multiple dimensions of the target information based on actual needs, such as the content of the target information (including its semantic information, domain, etc.), relevant tag information carried by the target information, user information, geographical location information, category information, etc. Based on these dimensions of the target information, corresponding scores and weights are assigned, and then the initial basic data a0 is obtained. The publication duration t0 of the target information can be calculated starting from its publication time. It can be understood that the longer the publication duration t0 of the target information, the larger its initial interaction data V, that is, the initial interaction data V increases with the increase of the publication duration t1 of the target information.

[0077] When determining the second interaction data, we can first calculate the average interaction data b0 of each network information in the information set and the second basic data. The second basic data can include the ratings of each network information and their average score a1. The ratings of each network information in the second basic data can be obtained by referring to the calculation method of the target information's basic data. Then, the minimum score a2 is determined based on the ratings of each network information. Based on the first basic data a0, the minimum score a2, and the average score a1 of each network information in the information set, the correlation between the target information and the information set can be determined. This correlation can be expressed as:

[0078] When calculating and determining the average interaction data b0 of each network information in the information set, the interaction data of each network information in the information set can be determined first, specifically by referring to the implementation method of the first interaction data of the target information mentioned above. Then, the average value is calculated based on the interaction data of each network information to obtain the average interaction data b0 of the information set.

[0079] Then, based on the correlation between the target information and the information set, and the average interaction data b0 of the information set, the second interaction data of the target information is obtained. The second interaction data can be represented as:

[0080] Then, the target interaction data can be determined based on the first interaction data V and the second interaction data. To ensure a smooth transition from the target interaction data to the actual first interaction data as the publication duration increases, a smoothing coefficient can be set based on Bayesian theory and the publication duration of the target information. Using this coefficient in conjunction with the first and second interaction data to obtain the target interaction data avoids large drops in the target interaction data, achieving a smooth numerical transition. The smoothing coefficient α can be obtained based on the publication duration t0 of the target information using the following formula:

[0081]

[0082] Wherein, T0 is the average time taken for the actual interaction data of each network information in the information set to reach the expected interaction data. The actual interaction data of each network information can be obtained by referring to the acquisition method of the first interaction data V of the target information in the embodiment of this application, and the expected interaction data can be obtained by referring to the second interaction data of the target information in the embodiment of this application.

[0083] Therefore, the target interaction data S can be obtained by the following formula:

[0084]

[0085] In this embodiment, the smoothing coefficient α is obtained based on the publication duration t0 of the target information and is positively correlated with the publication duration t0. The target interaction data S of the target information is equal to the second interaction data at the newly published moment. Subsequently, as the publication duration t0 of the target information increases, the target interaction data S is obtained by combining the first interaction data V and the second interaction data. The longer the publication duration t0, the smaller the proportion of the second interaction data and the larger the proportion of the first interaction data V. This achieves a smooth transition between the target interaction data S of the target information and the actual first interaction data, avoiding large data fluctuations when directly transitioning from the first interaction data to the actual interaction data, which would significantly affect the ranking of the target information in the set and improve the reliability of the target interaction data S.

[0086] In some embodiments, the information processing method further includes:

[0087] The interaction interval time is obtained based on the first interaction data;

[0088] Obtain the average interaction interval time of the information set, and obtain the attenuation coefficient based on the interaction interval time and the average interaction interval time;

[0089] The target interaction data is updated according to the attenuation coefficient, so as to update the first feature data based on the updated target interaction data.

[0090] In this embodiment, the first interaction data includes information on each user's interaction with the target information. The interaction interval time can be extracted based on the first interaction data to show the interval between users' interaction with the target information. For example, if one user performs an interaction such as browsing, liking, collecting, commenting, or sharing the target information, and another user performs the same or different interaction with the target information, the interval time between the two users' interaction operations can be obtained. Alternatively, the interval time between different interaction operations performed by the same user on the target information can also be obtained. That is, the interaction interval time can be obtained by obtaining the interval time between two adjacent different or the same interaction operations on the target information. An increase in the interaction interval time leads to a decrease in the interaction frequency, indicating that the user's attention to the target information is decreasing. When obtaining the average interaction interval time of the information set, the sub-interval time corresponding to each network information can be obtained first, and then the average value can be calculated based on the sub-interval time. Then, an attenuation coefficient is designed based on the interaction interval time of the target information and the average interaction interval time of the information set, so that the target interaction data and the first feature data of the target information attenuate as the social frequency decreases.

[0091] This embodiment determines the attenuation coefficient based on the interaction interval time of the target information. The attenuation coefficient can be used to adjust and update the target interaction data. When the interaction interval time of the target information is greater than the average interaction interval time of the information set, the attenuation coefficient is used to reduce the target interaction data, causing the first feature data to attenuate as the interaction interval time increases. Simultaneously, the first feature data of target information with shorter interaction interval times decreases more slowly, providing sufficient display time. For example, when determining the attenuation coefficient β based on the interaction interval time t1 of the target information and the average interaction interval time T1 of the information set, it can be expressed as the following formula:

[0092]

[0093] Here, γ is an attenuation constant, which can be set by technicians based on experience to adjust the attenuation coefficient.

[0094] When combined with the attenuation coefficient β, the target interaction data S can be calculated using the following formula:

[0095]

[0096] In this embodiment, the attenuation coefficient β is obtained based on the interaction interval time of the target information and the average interaction interval time of the information set. When the interaction interval time increases, it indicates a decrease in social frequency, so that the target interaction data S of the target information can attenuate as the social frequency decreases. In this way, after the target information is fully mined, its social frequency will gradually decrease, and the target interaction data S obtained based on the attenuation coefficient β will also gradually attenuate; thus, the first feature data obtained based on the target interaction data S will also decrease accordingly, until it no longer meets the conditions of the information set, at which point the target information is excluded from the information set.

[0097] Based on the same inventive concept, embodiments of this application also provide an information processing device, such as... Figure 2 As shown, it includes:

[0098] The acquisition module 10 is configured to acquire first interactive data of target information, wherein the first interactive data is used to characterize the interaction frequency of the target information;

[0099] The determining module 20 is configured to determine second interactive data of the target information based on the information set; the second interactive data is used to characterize the interactive features of the target information relative to the information set;

[0100] The data processing module 30 is configured to determine the target interaction data of the target information based on the first interaction data and the second interaction data; and to determine whether the target information can be classified into the information set based on the target interaction data.

[0101] The information processing device described in this embodiment, through its configured acquisition module 10, determination module 20, and data processing module 30, can implement the steps of the information processing method provided in any embodiment of this application. These steps will not be repeated here.

[0102] This application also provides an electronic device, including at least a memory 501, a processor 502, and a bus (not shown), wherein the structural schematic diagram of the electronic device can be as follows: Figure 3 As shown, the memory 501 stores machine-readable instructions that can be executed by the processor 502. When the electronic device is running, the processor 502 and the memory 501 communicate via a bus. When the machine-readable instructions are executed by the processor, the steps of the information processing method provided in any embodiment of this application are implemented.

[0103] Since the electronic device described in this application embodiment is an electronic device equipped with a memory for implementing the information processing method disclosed in this application embodiment, those skilled in the art can understand the structure and variations of the electronic device described in this application embodiment based on the information processing method described in this application embodiment, and therefore will not be described again here.

[0104] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any one of the method steps in the information processing method provided in any of the above embodiments.

[0105] The storage medium in this embodiment may be included in an electronic device; or it may exist independently and not be assembled into an electronic device. The storage medium carries one or more computer programs, which, when executed, implement the steps of the information processing method provided in the embodiments of this application.

[0106] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. Optionally, specific examples in this embodiment can refer to the examples described in any embodiment of this application, which will not be repeated here. Obviously, those skilled in the art should understand that the various modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular hardware and software combination.

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0109] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0110] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

[0111] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. An information processing method, wherein, include: Acquire first interaction data of the target information, wherein the first interaction data is used to characterize the interaction frequency of the target information; Second interactive data that determines the target information based on the information set; The second interactive data is used to characterize the interactive features of the target information relative to the information set; The second interaction data is obtained by predicting the interaction situation based on the basic data and interaction data of the information set, combined with the basic data of the target information; The target interaction data for determining the target information is based on the first interaction data and the second interaction data; Based on the target interaction data, determine whether the target information can be categorized into the information set; The second interactive data, which determines the target information based on the information set, includes: Determine the association between the target information and the information set; The second interactive data is determined based on the aforementioned association; The target interaction data for determining the target information based on the first interaction data and the second interaction data includes: Obtain the duration of the publication of the target information; Based on the release duration, a preset algorithm is used to combine the first interaction data and the second interaction data to obtain the target interaction data; The process of determining whether the target information can be categorized into the information set based on the target interaction data includes: Based on the target interaction data and the first basic data of the target information, the first feature data of the target information is obtained; Based on the first feature data, determine whether the target information meets the conditions of the information set.

2. The method of claim 1, wherein, Determining the association between the target information and the information set includes: Obtain the first basic data of the target information and the second basic data of the information set; The association relationship is determined based on the first basic data and the second basic data.

3. The method of claim 1, wherein, Determining the second interactive data based on the aforementioned association includes: Obtain the average interaction data of the information set; The second interaction data is obtained based on the aforementioned relationship and the average interaction data.

4. The method of claim 1, wherein, Also includes: The interaction interval time is obtained based on the first interaction data; Obtain the average interaction interval time of the information set, and obtain the attenuation coefficient based on the interaction interval time and the average interaction interval time; The target interaction data is updated according to the attenuation coefficient, so as to update the first feature data based on the updated target interaction data.

5. An information processing apparatus, comprising: include: The acquisition module is configured to acquire first interactive data of target information, wherein the first interactive data is used to characterize the interaction frequency of the target information. The determination module is configured to determine the second interactive data of the target information based on the information set; The second interaction data is used to characterize the interaction features of the target information relative to the information set; wherein, determining the second interaction data of the target information based on the information set includes: determining the correlation between the target information and the information set; determining the second interaction data according to the correlation; the second interaction data is obtained by predicting the interaction situation of the target information based on the basic data and interaction data of the information set, combined with the basic data of the target information. The data processing module is configured to determine target interaction data of the target information based on the first interaction data and the second interaction data; and to determine whether the target information can be categorized into the information set based on the target interaction data. Determining the target interaction data of the target information based on the first interaction data and the second interaction data includes: obtaining the publication duration of the target information; and combining the first interaction data and the second interaction data using a preset algorithm based on the publication duration to obtain the target interaction data. Determining whether the target information can be categorized into the information set based on the target interaction data includes: obtaining first feature data of the target information based on the target interaction data and first basic data of the target information; and determining whether the target information meets the conditions of the information set based on the first feature data.

6. An electronic device comprising at least a memory, a processor, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method as described in any one of claims 1-4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-4.

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