Method and apparatus for providing feedback, device, and medium

Abstract

Provided are a method and apparatus for providing feedback, a device, and a medium. The method comprises: providing a media item in an application; in response to determining an interaction between an object in the application and the media item, determining an association degree between the object and the media item on the basis of object information of the object and media information of the media item; and in response to determining that the association degree satisfies a predetermined association condition, providing a feedback control in the application, wherein the feedback control is used for providing feedback for the media item in the application. The feedback control can be automatically provided in the application, and the object may directly invoke the feedback control to provide feedback. In this way, a feedback control can be provided at an appropriate time point, thereby reducing the complexity of interaction and improving the performance of the application.

Description

Methods, apparatus, devices, and media for providing feedback Technical Field

[0001] Implementations of this disclosure generally relate to application management, and in particular to methods, apparatus, devices, and computer-readable storage media for providing feedback in applications. Background Technology

[0002] Applications can offer various media items that users can interact with and provide feedback on, such as comments. For example, an application can provide comment controls, allowing users to actively post comments. As users watch media items, their need to express feedback is growing, and they expect feedback to be provided in a more effective way within the application. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for providing feedback is provided. In this method, a media item is provided in an application. In response to determining an interaction between an object and a media item in the application, an affinity between the object and the media item is determined based on object information of the object and media information of the media item. In response to determining that the affinity satisfies a predetermined affinity condition, a feedback control is provided in the application, the feedback control being used to provide feedback to the media item in the application.

[0004] In a second aspect of this disclosure, an apparatus for providing feedback is provided. The apparatus includes: a providing module configured to provide a media item in an application; a determining module configured to, in response to determining an interaction between an object and a media item in the application, determine a correlation degree between the object and the media item based on object information of the object and media information of the media item; and a feedback module configured to, in response to determining that the correlation degree meets predetermined correlation conditions, provide a feedback control in the application, the feedback control being used to provide feedback for the media item in the application.

[0005] In a third aspect of this disclosure, an electronic device is provided. The electronic device includes: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to a first aspect of this disclosure when executed by the at least one processing unit.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, causes the processor to implement the method according to a first aspect of this disclosure.

[0007] In a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0008] It should be understood that the content described in this content section is not intended to limit the key or essential features of the implementation of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] In the following detailed description, the above and other features, advantages, and aspects of the various implementations of this disclosure will become more apparent, taken in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 shows a block diagram of an application environment according to one implementation of the present disclosure;

[0011] Figure 2 shows a block diagram for providing feedback according to some implementations of this disclosure;

[0012] Figure 3 shows a block diagram illustrating the provision of feedback controls in an application according to some implementations of this disclosure;

[0013] Figure 4 shows a block diagram illustrating the provision of comments in an application according to some implementations of this disclosure;

[0014] Figure 5 shows a block diagram illustrating the provision of multiple feedback controls in an application according to some implementations of this disclosure;

[0015] Figure 6 shows a block diagram for determining the degree of association according to some implementations of this disclosure;

[0016] Figure 7 shows a block diagram of a method for determining and deploying a machine learning model according to some implementations of this disclosure;

[0017] Figure 8 shows a block diagram of a method for providing feedback using a machine learning model according to some implementations of this disclosure;

[0018] Figure 9 shows a flowchart of a method for providing feedback according to some implementations of this disclosure;

[0019] Figure 10 shows a block diagram of an apparatus for providing feedback according to some implementations of this disclosure; and

[0020] Figure 11 shows a block diagram of a device capable of implementing various implementations of the present disclosure. Detailed Implementation

[0021] Implementations of this disclosure will now be described in more detail with reference to the accompanying drawings. While some implementations of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the implementations set forth herein. Rather, these implementations are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and implementations of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] In the description of the implementation methods disclosed herein, the term "comprising" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one implementation" or "the implementation" should be understood as "at least one implementation". The term "some implementations" should be understood as "at least some implementations". Other explicit and implicit definitions may also be included below. As used herein, the term "model" can represent the relationships between various data. For example, the aforementioned relationships can be obtained based on various currently known and / or future-developed technical solutions.

[0023] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and user authorization should be obtained.

[0025] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0026] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, for example, via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose whether to "agree" or "disagree" to provide personal information to the electronic device.

[0027] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0028] The term "in response to" as used herein refers to a state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of subsequent actions performed in response to such event or condition is not necessarily strongly correlated with the time when the event occurs or the condition is met. For example, in some cases, subsequent actions may be performed immediately upon the occurrence of the event or the fulfillment of the condition; while in others, they may be performed some time after the occurrence of the event or the fulfillment of the condition.

[0029] Example Environment

[0030] The application can provide various media items, and users within the application can interact with these media items in various ways and provide feedback such as comments. Referring to Figure 1, which illustrates a block diagram 100 of an application environment according to one implementation of this disclosure, a media item 120 can be provided to an object (e.g., a user of application 110) within application 110. The media item 120 can include various types, such as, but not limited to, videos, short videos, music, text, images, games, or rich media data including combinations of the above types. For ease of description, video will be used as an example of a media item in the context of this disclosure. The application can provide a comment control 130, and various objects within the application can actively press the comment control to post comments. As users increasingly demand feedback on media items while viewing them, a more efficient way to provide feedback within the application is desired.

[0031] Summary of feedback provided

[0032] To at least partially address the shortcomings of the prior art, a method for providing feedback is proposed according to one implementation of this disclosure. In summary, feedback controls can be automatically provided in the application when it is determined that a user is interested in a media item and expects feedback. This simplifies user interaction with the application and improves the efficiency of using the application. Referring to Figure 2, which describes a summary of one implementation of this disclosure, Figure 2 shows a block diagram 200 for providing feedback according to some implementations of this disclosure. A media item 120 can be provided in the application, and an interaction 220 between an object 210 in the application and the media item 120 can be determined. In response to determining that an interaction 220 exists, a degree of association 230 between the object 210 and the media item 120 can be determined based on object information of the object 210 and media information of the media item 120. Here, the degree of association 230 can represent the degree of interest of the object 210 in the media item 120 and can reflect to some extent whether the object 210 will provide feedback such as a comment on the media item 120.

[0033] Furthermore, in response to determining that the correlation degree 230 meets a predetermined correlation condition (e.g., exceeding a predetermined threshold), a feedback control 240 can be provided in the application. This feedback control is used to provide feedback for the media item within the application. At this point, if it is found that object 210 is interested in media item 120 and is likely to submit feedback, the feedback control can be automatically provided in the application. In this way, object 210 can directly invoke the feedback control to provide feedback. This approach allows the feedback control to be provided at appropriate times, reducing the complexity of the interaction and improving application performance.

[0034] Detailed process of providing feedback

[0035] Having outlined some implementations of this disclosure, further details regarding the methods used to provide feedback will be described below. According to some implementations of this disclosure, interactions between objects and media items can include various types, such as an object watching a media item, an object viewing comments on a media item by other objects, an object viewing information about the publisher of a media item, an object saving a media item, an object liking a media item, an object sharing a media item, an object downloading a media item, and so on. For ease of description, the following example of an object watching a media item will be used to provide further details of this disclosure.

[0036] For example, when it is determined that an object is watching a media item, interaction can be confirmed, and thus the degree of association between the object and the media item can be determined. According to some implementations of this disclosure, the degree of association between the object and the media item can be determined immediately upon detecting that the object has begun watching the media item. Alternatively and / or additionally, the degree of association between the object and the media item can be determined in response to determining that the duration of the interaction meets a predetermined time condition. In this way, it is possible to determine more accurately whether the object is truly interested in the media item, thereby presenting feedback controls at a more precise point in time. For another example, it is possible to determine that the object is interested in the media item and wants to learn more about it in response to determining that the object is dragging the media item's playback progress bar, rewinding the media item, or fast-forwarding the media item, and so on.

[0037] According to some implementations of this disclosure, feedback for media items is provided in the application in response to receiving interaction with the feedback control. Figure 3 shows a block diagram 300 of providing feedback controls in the application according to some implementations of this disclosure. As shown in Figure 3, quick feedback controls can be provided at the bottom (or other location) of the interface of application 110. Specifically, feedback control 310 can be used to input comments, and feedback controls 312, 314, and 316 can be used to provide icon comments for quick comment posting. Using some implementations of this disclosure, feedback controls can be automatically provided at appropriate times, thereby reducing the complexity of user operations.

[0038] According to some implementations of this disclosure, in the process of providing feedback for media items in the application, an input area for inputting feedback can be provided in the application; in response to receiving an interaction with the input area, feedback corresponding to the interaction is presented in the input area; and in response to receiving confirmation of the feedback, feedback is provided in the application. In response to the interaction between the determined object and the feedback control 310 in FIG3, an interface as shown in FIG4 can be provided, which shows a block diagram 400 of providing comments in the application according to some implementations of this disclosure.

[0039] As shown in Figure 4, a comment area 410 can be provided in application 110, which can display historical comments of media item 120, for example, 123 historical comments. Further, an input area 410 can be provided in application 110 for users to enter comments. Alternatively and / or additionally, a control 420 for inputting images, an "@" control 422 for "mentioning" other objects, a control 424 for inputting icons, etc., can be provided in application 110. Users can input data into input area 410 using a keyboard, and comments can be provided in the application in response to the detection of confirmation of the input data (e.g., interaction with control 426). Using some implementations of this disclosure, feedback controls can be provided automatically at more appropriate times, thereby reducing the operational complexity of the application.

[0040] It should be understood that although the details of providing feedback controls have been described above using comments as an example of feedback only, alternatively and / or additionally, feedback may include at least one of the following: commenting on a media item, forwarding, favoriting, and following the provider of the media item. See Figure 5 for further details, which shows a block diagram 500 of providing multiple feedback controls in an application according to some implementations of this disclosure. As shown in Figure 5, at least one feedback control may be presented in application 110. For example, feedback control 510 may be used to provide a comment function, feedback control 512 may be used to provide a like function, feedback control 514 may be used to provide a favorite function, feedback control 516 may provide a forwarding function, and so on. Using some implementations of this disclosure, multiple feedback functions can be provided at appropriate times in a more convenient and efficient manner, thereby simplifying the operational complexity of the application.

[0041] According to some implementations of this disclosure, machine learning models can be used to determine the degree of association between an object and a media item. Specifically, in determining the degree of association between an object and a media item, a machine learning model describing the degree of association between the object and the media item can be obtained; and using the machine learning model, the degree of association between the object and the media item can be determined based on the object information of the object and the media information of the media item. Using some implementations of this disclosure, machine learning models can be used to obtain knowledge from sample data about whether a user will provide a comment, thereby determining more accurately whether a feedback control should be provided.

[0042] According to some implementations of this disclosure, reference samples, also known as training samples, can be obtained during the determination of a machine learning model. Figure 6 shows a block diagram 600 for determining a machine learning model according to some implementations of this disclosure. Reference samples may include, for example, reference media information 610 related to reference media items provided in a reference application, reference object information 620 of a reference object interacting with the reference media item, and reference feedback information 630 representing reference feedback provided by the reference object to the reference media item. The machine learning model is determined using the reference media information, the reference object information, and the reference feedback information. Here, the reference media item may represent a historical media item provided in the reference application.

[0043] Specifically, the reference object information may include, but is not limited to: viewing duration, indicating the length of time the object watched the media item; media item completion rate, indicating the proportion of time the object watched the media item completely; number of likes, indicating the number of times the object liked the media item; number of shares, indicating the number of times the object shared the media item; number of comments, indicating the number of times the object commented on the media item; usage time, indicating the length of time the object used the application; activity level, indicating the length of time the object watched videos per day, the number of videos watched, etc.; device parameters, such as the type of electronic device used by the object, the operating system, etc. Alternatively and / or additionally, the media information of the reference media item may include, but is not limited to: content type, such as music, sports, news, etc.; publication time, such as a time of day, a time of week, etc.; number of views, indicating the number of times the media item was viewed; duration, indicating the duration of the media item, etc. Furthermore, the reference sample may include annotation information indicating whether the object provided feedback on the media item, i.e., reference feedback information. In the case of comments, 0 can be used to indicate no comment was provided, and 1 can be used to indicate a comment was provided.

[0044] Furthermore, a machine learning model 640 can be trained using a large amount of collected sample data to extract relevant knowledge about whether an object will comment on a media item. According to some implementations of this disclosure, a machine learning model can be built based on a regression model. The regression model can take the aforementioned features as input and output the probability that a user will click on a quick comment. The goal of model training is to find a suitable decision boundary so that the model can predict whether a user will click on a quick comment based on the input features. Methods such as cross-validation are used to evaluate the model's performance, ensuring its accuracy and generalization ability. The trained model can be deployed and the probability of a user clicking on a quick comment can be predicted in real time, triggering the quick comment function based on the prediction results. Here, the model's output can be a probability value between 0 and 1, representing the likelihood of a user clicking on a quick comment.

[0045] According to some implementations of this disclosure, for example, Formula 1 can be used as the loss function for a machine learning model.

[0046] In Formula 1, x can represent the input features, for example, represented by n dimensions (x1, x2, ..., x...). n-1 Features including media information and object information; parameters β0, β1, ..., β n This represents the parameters of the machine learning model. During training, features determined based on reference media information and reference object information can be used as input to the machine learning model, and the machine learning model can be used to determine the prediction y. Then, the parameters of the machine learning model can be updated in a direction that minimizes the difference between the prediction y and the reference feedback information (i.e., the true value). Using some implementations of this disclosure, the machine learning model can acquire knowledge about the object, the media item, and whether feedback is provided, thereby supporting a more accurate determination of whether to provide a quick feedback control to the object. The trained machine learning model can output the probability that the object provides feedback, which can be represented, for example, using a value between [0,1].

[0047] It should be understood that the training process was described above using the example of whether or not comments are provided as label data. Alternatively and / or additionally, label data can include the timing of when an object provides feedback, for example, how long after viewing a media item an object provides feedback. In this way, the machine learning model can provide more accurate predictions, and the model can output the timing of providing feedback controls, such as providing feedback controls 10 seconds, 15 seconds, or other times after the object views the media item. This avoids providing feedback controls too early and disrupting normal browsing of media items.

[0048] Referring to Figure 7 for further details regarding the machine learning model, Figure 7 illustrates a block diagram of a method 700 for determining and deploying a machine learning model according to some implementations of this disclosure. As shown in Figure 7, method 700 begins at box 710. At box 712, data can be collected, for example, a large number of reference samples from a past time period can be collected. At box 714, it can be determined which features to extract from the reference samples. At box 716, feature processing can be performed and the desired features can be extracted. At box 718, the machine learning model can be trained, for example, the machine learning model can be updated based on the loss function shown in Equation 1 above. At box 720, the updated machine learning model can be evaluated. At box 722, if it is determined that the updated machine learning model meets predetermined conditions, for example, reaching a predetermined accuracy, or reaching a predetermined number of updates, etc. If the result at box 722 is "yes", then method 700 proceeds to box 726 to deploy the machine learning model. Alternatively and / or additionally, if the result at box 722 is "No", the machine learning model 724 can be adjusted, and the adjusted machine learning model can be evaluated again. At box 728, the deployed machine learning model can be used to provide quick feedback functionality, and method 700 ends at box 730.

[0049] According to some implementations of this disclosure, the machine learning model can be deployed on the application's server, and alternatively and / or additionally, it can be deployed on the client device running the application. Whether to provide a feedback control in the application can be determined based on the relevance from the machine learning model. According to some implementations of this disclosure, the predetermined relevance condition may include at least one threshold. For example, an upper threshold may be provided. Specifically, in the process of providing a feedback control in the application, in response to determining that the relevance is higher than the upper threshold (e.g., denoted as TH_H), the feedback control is provided in the application. For example, the upper threshold may be set to 0.5, 0.6, or other values. If the relevance is determined to be greater than or equal to the upper threshold, the feedback control is provided directly in the application. In this way, feedback controls can be provided in a faster and more efficient manner, thereby supporting users to provide rapid feedback.

[0050] According to some implementations of this disclosure, a lower threshold (e.g., denoted as TH_L) can be provided during the process of providing feedback controls in the application. In response to determining that the relevance is lower than an upper threshold but higher than the lower threshold, interaction information between the object and the media item can be obtained; and in response to determining that the interaction information meets predetermined presentation conditions, the feedback control is presented in the application. Here, the lower threshold can be set, for example, to 0.3, 0.25, or other values. If the relevance is determined to be lower than the lower threshold, no feedback control is provided. If the relevance is determined to be between the lower and upper thresholds, there may be uncertainties, and therefore it is necessary to further determine whether to present the feedback control based on the interaction information.

[0051] According to some implementations of this disclosure, the interactive information can include various types, such as the length of time a user watches a media item, the length of time a user replays a media item, the length of time a user likes a media item, the length of time a user shares a media item, the length of time a user plays a media item in full screen, etc. If the length of time exceeds a predetermined threshold (e.g., 30 seconds, or other values), a feedback control can be presented. Alternatively and / or additionally, if a positive interaction between the user and the media item is determined, such as replaying a media item, liking a media item, sharing a media item, playing a media item in full screen, etc., a feedback control can be presented. Alternatively and / or additionally, if a negative interaction between the user and the media item is determined, such as stopping playback of a media item, exiting a media item, etc., the presentation of the feedback control is prohibited. In this way, it is possible to determine more accurately whether a user is about to submit a comment, thereby reducing the risk of feedback controls disturbing the user.

[0052] Figure 8 shows a block diagram of a method 800 for providing feedback using a machine learning model according to some implementations of this disclosure. As shown in Figure 8, method 800 begins at block 810. At block 812, the correlation score (e.g., denoted as SCORE) can be read. At block 814, the upper limit preset TH_H and the lower limit threshold TH_L can be read. Then, SCORE, TH_H, and TH_L can be compared. At block 818, if it is determined that SCORE ≥ TH_H, method 800 can proceed to block 826 to provide feedback controls. At block 820, if it is determined that SCORE ≤ TH_L, the feedback controls are disabled and method 800 ends at block 828. At block 816, if it is determined that TH_L ≤ SCORE ≤ TH_H, method 800 can proceed to block 822 and obtain interaction information. At block 824, if the interaction information meets predetermined conditions, method 800 proceeds to block 826 and provides feedback controls. If the interaction information does not meet the predetermined conditions, method 800 proceeds to box 828 and ends. Utilizing some implementation methods of this disclosure, upper and lower thresholds can provide a two-level judgment strategy, thereby determining more accurately whether feedback controls should be provided in the application.

[0053] According to some implementations of this disclosure, the same threshold can be set for multiple objects in an application. This facilitates the management of a large number of objects in the application, thereby reducing application management complexity. Alternatively and / or additionally, individual thresholds can be set for multiple objects. This allows for more precise management of different objects and provides customized services for each object.

[0054] According to some implementations of this disclosure, interaction information between an object and a feedback control can be obtained; and predetermined association conditions can be updated based on the interaction information. For example, if it is found that an object likes to provide feedback, the conditions for providing feedback controls can be appropriately relaxed, and more feedback controls can be provided to that object. Conversely, if it is found that an object does not like to provide feedback, the conditions for providing feedback controls can be appropriately tightened, and fewer feedback controls can be provided to that object. Specifically, in the process of updating predetermined association conditions based on interaction information, in response to determining that the interaction information indicates that the object submits feedback, the upper threshold or the lower threshold is lowered. In this way, more feedback controls can be presented to that object. Alternatively and / or additionally, in response to determining that the interaction information indicates that the object does not submit feedback, the upper threshold is increased, or the lower threshold is decreased. In this way, fewer feedback controls can be presented to that object. Using some implementations of this disclosure, conditions for triggering quick feedback can be customized for different objects, thereby providing more refined customized services for different objects.

[0055] According to some implementations of this disclosure, the feedback information input by the object can be determined, and the machine learning model (e.g., a recommendation model) used to provide media items to the object can be updated based on the feedback information. In this way, the recommendation model can be updated based on whether the object provides feedback and the specific content of the feedback. This further improves the accuracy of the recommendation model, thereby increasing the efficiency with which the object obtains information of interest.

[0056] Using some of the implementation methods disclosed herein, feedback controls can be automatically provided in the application, and objects can directly call these feedback controls to provide feedback. In this way, feedback controls can be provided at appropriate times, reducing the complexity of the interaction and improving application performance.

[0057] Example process

[0058] Figure 9 shows a flowchart of a method 900 for providing feedback according to some implementations of this disclosure. At box 910, a media item is provided in the application. At box 920, in response to determining the interaction between an object and a media item in the application, the association degree between the object and the media item is determined based on the object's object information and the media item's media information. At box 930, in response to determining that the association degree satisfies a predetermined association condition, a feedback control is provided in the application, the feedback control being used to provide feedback for the media item in the application.

[0059] According to some implementations of this disclosure, the method 900 further includes: in response to receiving an interaction with the feedback control, presenting feedback for the media item in the application.

[0060] According to some implementations of this disclosure, providing feedback for media items in an application includes: providing an input area in the application for inputting feedback; presenting feedback corresponding to the interaction in the input area in response to receiving an interaction with the input area; and providing feedback in the application in response to receiving an acknowledgment of the feedback.

[0061] According to some implementations of this disclosure, determining the degree of association between an object and a media item includes: obtaining a machine learning model that describes the degree of association between the object and the media item; and using the machine learning model, determining the degree of association between the object and the media item based on the object information of the object and the media information of the media item.

[0062] According to some implementations of this disclosure, providing feedback controls in the application includes: providing feedback controls in the application in response to determining that the correlation degree is higher than an upper limit threshold.

[0063] According to some implementations of this disclosure, providing feedback controls in an application includes: in response to determining that the relevance is lower than an upper threshold and higher than a lower threshold, acquiring interaction information between the object and the media item; and in response to determining that the interaction information meets predetermined presentation conditions, presenting feedback controls in the application.

[0064] According to some implementations of this disclosure, determining the degree of association between an object and a media item further includes: determining the degree of association between the object and the media item in response to the determination that the duration of the interaction meets a predetermined time condition.

[0065] According to some implementations of this disclosure, the machine learning model is obtained based on: acquiring reference media information of a reference media item provided in a reference application, reference object information of a reference object interacting with the reference media item, and reference feedback information representing the reference feedback provided by the reference object to the reference media item; and using the reference media information, reference object information, and reference feedback information to determine the machine learning model.

[0066] According to some implementations of this disclosure, the method 900 further includes: acquiring interaction information between the object and the feedback control; and updating predetermined association conditions based on the interaction information.

[0067] According to some implementations of this disclosure, feedback includes at least one of the following: comments, reposts, favorites, and following media item providers.

[0068] Example devices and equipment

[0069] Figure 10 shows a block diagram of an apparatus 1000 for providing feedback according to some implementations of the present disclosure. The apparatus includes: a providing module 1010 configured to provide a media item in an application; a determining module 1020 configured to determine, in response to determining an interaction between an object and a media item in the application, a correlation degree between the object and the media item based on object information of the object and media information of the media item; and a feedback module 1030 configured to provide a feedback control in the application in response to determining that the correlation degree meets a predetermined correlation condition, the feedback control being used to provide feedback for the media item in the application.

[0070] According to some implementations of this disclosure, the device 1000 further includes a presentation module configured to: in response to receiving an interaction with a feedback control, present feedback for a media item in the application.

[0071] According to some implementations of this disclosure, the presentation module is further configured to: provide an input area in the application for input feedback; present feedback in the input area corresponding to the interaction in response to receiving an interaction with the input area; and provide feedback in the application in response to receiving confirmation of the feedback.

[0072] According to some implementations of this disclosure, the determining module is further configured to: obtain a machine learning model describing the degree of association between an object and a media item; and use the machine learning model to determine the degree of association between the object and the media item based on the object information of the object and the media information of the media item.

[0073] According to some implementations of this disclosure, the providing module is further configured to: provide feedback controls in the application in response to determining that the correlation degree is higher than an upper limit threshold.

[0074] According to some implementations of this disclosure, the providing module is further configured to include: in response to determining that the relevance is below an upper threshold and above a lower threshold, acquiring interaction information between the object and the media item; and in response to determining that the interaction information meets predetermined presentation conditions, presenting a feedback control in the application.

[0075] According to some implementations of this disclosure, the determining module is further configured to: determine the degree of association between the object and the media item in response to the determination that the duration of the interaction meets a predetermined time condition.

[0076] According to some implementations of this disclosure, the machine learning model is obtained based on: acquiring reference media information of a reference media item provided in a reference application, reference object information of a reference object interacting with the reference media item, and reference feedback information representing the reference feedback provided by the reference object to the reference media item; and using the reference media information, reference object information, and reference feedback information to determine the machine learning model.

[0077] According to some implementations of this disclosure, the device further includes a processing module configured to: acquire interaction information between the object and the feedback control; and update predetermined association conditions based on the interaction information.

[0078] According to some implementations of this disclosure, feedback includes at least one of the following: comments, reposts, favorites, and following media item providers.

[0079] Figure 11 shows a block diagram of a device 1100 capable of implementing various implementations of the present disclosure. It should be understood that the computing device 1100 shown in Figure 11 is merely exemplary and should not constitute any limitation on the functionality and scope of the implementations described herein. The computing device 1100 shown in Figure 11 can be used to implement the methods described above.

[0080] As shown in Figure 11, computing device 1100 is in the form of a general-purpose computing device. Components of computing device 1100 may include, but are not limited to, one or more processors or processing units 1110, memory 1120, storage devices 1130, one or more communication units 1140, one or more input devices 1150, and one or more output devices 1160. Processing unit 1110 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 1120. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of computing device 1100.

[0081] Computing device 1100 typically includes multiple computer storage media. Such media can be any available media accessible to computing device 1100, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 1120 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 1130 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within computing device 1100.

[0082] The computing device 1100 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG11, disk drives for reading or writing from removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading or writing from removable, non-volatile optical disks may be provided. In these cases, each drive may be connected to a bus (not shown) via one or more data media interfaces. The memory 1120 may include a computer program product 1125 having one or more program modules configured to perform various methods or actions of various implementations of this disclosure.

[0083] The communication unit 1140 enables communication with other computing devices via a communication medium. Additionally, the components of the computing device 1100 can function as a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the computing device 1100 can operate in a networked environment using logical connections to one or more other servers, networked personal computers (PCs), or another network node.

[0084] Input device 1150 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 1160 can be one or more output devices, such as a monitor, speaker, printer, etc. Computing device 1100 can also communicate as needed with one or more external devices (not shown) via communication unit 1140. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with computing device 1100, or with any device that enables computing device 1100 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interfaces (not shown).

[0085] According to an implementation of this disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the method described above. According to an implementation of this disclosure, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the method described above.

[0086] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0087] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0088] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0089] 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 disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. 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 consecutive 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, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0090] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for providing feedback, comprising: Provide media items in the application; In response to determining the interaction between an object in the application and the media item, the degree of association between the object and the media item is determined based on the object information of the object and the media information of the media item; as well as In response to determining that the correlation degree meets a predetermined correlation condition, a feedback control is provided in the application, the feedback control being used to provide feedback for the media item in the application.

2. The method according to claim 1, further comprising: In response to receiving an interaction with the feedback control, the feedback is presented in the application for the media item.

3. The method of claim 2, wherein providing the feedback for the media item in the application comprises: The application provides an input area for inputting the feedback; In response to receiving an interaction with the input area, feedback corresponding to the interaction is presented in the input area; as well as In response to receiving confirmation of the feedback, the feedback is provided in the application.

4. The method of claim 1, wherein determining the degree of association between the object and the media item comprises: A machine learning model to obtain the correlation between descriptive objects and media items; as well as Using the machine learning model, the correlation between the object and the media item is determined based on the object information of the object and the media information of the media item.

5. The method of claim 1, wherein providing the feedback control in the application comprises: In response to determining that the correlation degree is higher than the upper limit threshold, the feedback control is provided in the application.

6. The method of claim 1, wherein providing the feedback control in the application comprises: In response to determining that the correlation degree is below an upper threshold and above a lower threshold, the interaction information between the object and the media item is obtained; as well as In response to determining that the interactive information meets predetermined presentation conditions, the feedback control is presented in the application.

7. The method of claim 1, wherein determining the degree of association between the object and the media item further comprises: In response to determining that the duration of the interaction meets a predetermined time condition, the degree of association between the object and the media item is determined.

8. The method of claim 4, wherein the machine learning model is obtained based on: Acquire reference media information of reference media items provided in the reference application, reference object information of reference objects interacting with the reference media items, and reference feedback information representing the reference feedback provided by the reference objects to the reference media items; and The machine learning model is determined using the reference media information, the reference object information, and the reference feedback information.

9. The method of claim 1, further comprising: Obtain the interaction information between the object and the feedback control; as well as The predetermined association conditions are updated based on the interaction information.

10. The method of claim 1, wherein the feedback includes at least one of the following: commenting on the media item, forwarding, saving, and following the provider of the media item.

11. An apparatus for providing feedback, comprising: Provides modules that are configured to provide media items within the application; A determination module is configured to determine the degree of association between an object and a media item in response to determining the interaction between the object and the media item based on the object information of the object and the media information of the media item. as well as A feedback module is configured to provide a feedback control in the application in response to determining that the correlation degree meets a predetermined correlation condition, the feedback control being used to provide feedback for the media item in the application.

12. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 10 when executed by the at least one processing unit.

13. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 10.

14. A computer instruction product comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the method according to any one of claims 1 to 10.

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