Method, device and equipment for deleting streaming media cache and storage medium

Abstract

The embodiment of the invention discloses a streaming media cache deletion method and device, equipment and a storage medium, and belongs to the technical field of streaming media. The method comprises: determining a streaming media score of a target streaming media corresponding to a streaming media cache, the streaming media score being determined at least based on a playing record and an interaction record of the target streaming media, the interaction record being used for representing an interaction operation performed on the target streaming media; based on the streaming media score, the deletion priority of the streaming media cache is determined, and the deletion priority is in negative correlation with the streaming media score; and under the condition that the streaming media cache deletion demand exists, performing streaming media cache deletion based on the deletion priority. By adopting the technical scheme provided by the embodiment of the invention, the number of times of re-buffering the streaming media can be reduced, and traffic resources are saved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of streaming media technology, and in particular to a method, device, equipment and storage medium for deleting a streaming media cache. Background Art

[0002] When a streaming media player plays online audio files, a cache file needs to be created. If the user's storage space is insufficient, the cache file needs to be deleted to free up storage space.

[0003] In the related art, when the number of cached files reaches a threshold, the cached files are sorted according to their creation time, and then the files are deleted according to the sorting result. Frequently used cached files are easily deleted due to their creation time being too early, resulting in re-buffering and consuming traffic. Summary of the invention

[0004] The present application provides a method, apparatus, device and storage medium for deleting a streaming media cache. The technical solution includes the following contents:

[0005] In one aspect, an embodiment of the present application provides a method for deleting a streaming media cache. The method comprises:

[0006] Determine a streaming media score of a target streaming media corresponding to the streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media;

[0007] Based on the streaming media score, determining a deletion priority of the streaming media cache, wherein the deletion priority is negatively correlated with the streaming media score;

[0008] When there is a need to delete the streaming media cache, the streaming media cache is deleted based on the deletion priority.

[0009] On the other hand, an embodiment of the present application provides a device for deleting a streaming media cache, the device comprising:

[0010] A score determination module, used to determine a streaming media score of a target streaming media corresponding to the streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media;

[0011] A priority determination module, configured to determine a deletion priority of the streaming media cache based on the streaming media score, wherein the deletion priority is negatively correlated with the streaming media score;

[0012] The deletion module is used to delete the streaming media cache based on the deletion priority when there is a streaming media cache deletion demand.

[0013] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one computer instruction, and the at least one computer instruction is loaded and executed by the processor to implement the method for deleting the streaming media cache as described in the above aspects.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores at least one computer instruction, and the at least one computer instruction is loaded and executed by a processor to implement the method for deleting a streaming media cache as described in the above aspects.

[0015] On the other hand, an embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device implements the method for deleting the streaming media cache as described in the above aspects.

[0016] In the related art, a computer device sorts streaming media caches according to creation time. However, deleting streaming media caches with an earlier creation time may easily lead to deletion of frequently used streaming media caches.

[0017] In the embodiment of the present application, the computer device calculates the streaming media score according to the playback record and interaction record of the target streaming media, so that the streaming media score can reflect the user's use of the target streaming media. The computer device determines the deletion priority according to the streaming media score, and then deletes the streaming media cache according to the deletion priority, so that when deleting the streaming media cache, the computer device can determine whether to delete it according to whether the target streaming media is frequently used, which is conducive to alleviating the problem of re-buffering of frequently used streaming media due to deleting the streaming media cache of frequently used streaming media, reducing the number of re-buffering of streaming media and saving traffic resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of a method for deleting a streaming media cache provided by an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a streaming media score determination process provided by an exemplary embodiment of the present application;

[0020] Figure 3 is a flowchart of a process for updating the number of playbacks of a streaming media provided by an exemplary embodiment of the present application;

[0021] Figure 4is a flowchart of a process for determining an interaction scoring factor provided by an exemplary embodiment of the present application;

[0022] Figure 5 is a flowchart of a process for determining the weight of an interaction scoring factor provided by an exemplary embodiment of the present application;

[0023] Figure 6 is a flowchart of a streaming media score determination process provided by another exemplary embodiment of the present application;

[0024] Figure 7 is a flowchart of a streaming media cache deletion process based on deletion priority provided by an exemplary embodiment of the present application;

[0025] Figure 8 It is a structural block diagram of a streaming media cache deletion device provided by an exemplary embodiment of the present application;

[0026] Fig. 9 A structural block diagram of a computer device provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0028] When a streaming media player plays online audio files, a cache file needs to be created. If the user's storage space is insufficient, the cache file needs to be deleted to free up storage space.

[0029] In the related art, a computer device sorts streaming media caches according to creation time. However, deleting streaming media caches with an earlier creation time may easily lead to deletion of frequently used streaming media caches.

[0030] The embodiment of the present application provides a method for deleting a streaming media cache, which calculates a streaming media score based on the playback record and interaction record of the streaming media, determines the deletion priority, and implements the streaming media cache deletion. The method can solve the problem of commonly used streaming media being mistakenly deleted. The method is executed by a computer device, which may include a client device such as a smart phone, a tablet computer, a car computer, a smart watch, and an Internet TV.

[0031] See also Figure 1 , Figure 1 This is a flow chart of a method for deleting a streaming media cache provided by an exemplary embodiment of the present application. The method includes the following steps.

[0032] Step 101, determining a streaming media score of a target streaming media corresponding to a streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media.

[0033] In some embodiments, the streaming media cache refers to a cache file loaded from a server to a computer device when a streaming media player plays online streaming media, such as playing a video or playing an application. In the case of a target streaming media cache, the computer device no longer consumes traffic when playing the target streaming media again.

[0034] In the embodiment of the present application, the target streaming media refers to a unit quantity of streaming media corresponding to the streaming media score to be determined, such as a single song or a single short video.

[0035] In some embodiments, the streaming media score is used to characterize the user's demand for the streaming media cache of the target streaming media. Optionally, the streaming media score is positively correlated with the user's demand for the target streaming media.

[0036] In some embodiments, the play record refers to the user's historical play record of the target streaming media. For example, the user played song A at 18:31:48 and played song B at 17:03:39. Optionally, the play record may include at least one of the historical play time, historical play duration, complete play duration at the play interruption time point, remaining play duration, and play triggering mode of the target streaming media.

[0037] In some embodiments, the interaction record refers to a record of the user's interactive operations on the target streaming media in the historical time. Optionally, the interaction record may include at least one of the interactive operation time, interactive operation type, interactive operation object, and interactive operation content. For example, the user performed a comment operation on the target streaming media at 12:08:28, and the comment content was "very good".

[0038] It should be noted that, since the target streaming media is buffered during online playback, the streaming media cache of the target streaming media is obtained, so the target streaming media has at least one playback record. Since the user may not perform any interactive operation on the target streaming media, the target streaming media may not have an interactive record.

[0039] Step 102: Determine a deletion priority of the streaming media cache based on the streaming media score, where the deletion priority is negatively correlated with the streaming media score.

[0040] Optionally, the computer device determines the deletion priority corresponding to the target streaming media according to the corresponding relationship between the streaming media score and the deletion priority.

[0041] In a possible implementation, the computer device establishes a correspondence between the score interval of the streaming media score and the deletion priority. The computer device first determines the score interval in which the streaming media score is located, and then determines the deletion priority corresponding to the score interval as the deletion priority of the target streaming media.

[0042] For example, the deletion priority corresponding to the streaming media score interval [0, 10] is 10, the deletion priority corresponding to the streaming media score interval [11, 20] is 9, the deletion priority corresponding to the streaming media score interval [21, 30] is 8, and so on, the deletion priority corresponding to the streaming media score interval [91, 100] is 1. In the case of a streaming media score of 68, the streaming media score belongs to the score interval [61, 70], and the deletion priority corresponding to the score interval is 4.

[0043] In another possible implementation, the computer device establishes a mapping relationship between the streaming media score interval and the deletion priority interval, and determines the result obtained by converting the streaming media score through the interval as the deletion priority.

[0044] Exemplarily, the streaming media score range is 0 to 100, and the deletion priority range is 0 to 10. The computer device maps the interval 0 to 100 to the interval 10 to 0. When the streaming media score is 89.5, according to the mapping relationship between the streaming media score range and the deletion priority range, the computer device first calculates the difference of 10.5 between 100 and 89.5, and then reduces the difference from the interval range of 0 to 100 to the interval range of 0 to 10, and obtains a deletion priority of 1.05.

[0045] Step 103: When there is a requirement to delete the streaming media cache, the streaming media cache is deleted based on the deletion priority.

[0046] Optionally, the computer device determines that there is a need to delete the streaming media cache when the stored cache capacity of the streaming media cache reaches a capacity threshold. For example, when the stored cache capacity reaches 80% of the cache space, it is determined that there is a need to delete.

[0047] Optionally, the computer device determines that there is a need to delete the streaming media cache when the sum of the cache capacity to be stored and the stored cache capacity exceeds the available capacity of the cache space.

[0048] In some embodiments, the computer device sorts the streaming media cache based on deletion priority and then performs streaming media cache deletion.

[0049] In the embodiment of the present application, the computer device calculates the streaming media score according to the playback record and interaction record of the target streaming media, so that the streaming media score can reflect the user's use of the target streaming media. The computer device determines the deletion priority according to the streaming media score, and then deletes the streaming media cache according to the deletion priority, so that when deleting the streaming media cache, the computer device can determine whether to delete it according to whether the target streaming media is frequently used, which is conducive to alleviating the problem of re-buffering of frequently used streaming media due to deleting the streaming media cache of frequently used streaming media, reducing the number of re-buffering of streaming media and saving traffic resources.

[0050] The following embodiment describes the process of determining the streaming media score.

[0051] In some embodiments, the computer device weights the scoring factors determined based on the playback records and the interaction records and the scoring factor weights corresponding to the scoring factors to obtain a streaming media score.

[0052] See also Figure 2 , Figure 2 FIG. 1 is a flowchart of a process for determining a streaming media score provided by an exemplary embodiment of the present application. The process includes the following steps.

[0053] Step 201: Determine at least one playback scoring factor based on the playback record of the target streaming media.

[0054] In some embodiments, the play score factor is used to determine the streaming media score, and the play score factor is positively correlated with the streaming media score. The play records of the target streaming media under different accounts are different, and the play score factors are also different.

[0055] Optionally, the play score factor can be determined based on the number of times the target streaming media is played, based on the play ratio of the target streaming media, or based on the play interval of the target streaming media. The play interval refers to the play time interval. The play ratio refers to the ratio of the played content to the total content of the streaming media.

[0056] For example, the number of times the target streaming media is played is positively correlated with the playback scoring factor. For another example, the playback ratio of the target streaming media is positively correlated with the playback scoring factor. For another example, the playback interval of the target streaming media is negatively correlated with the playback scoring factor.

[0057] Step 202: Determine at least one interaction scoring factor based on the interaction record of the target streaming media.

[0058] In some embodiments, the interaction scoring factor is used to determine the streaming media score, and the interaction scoring factor is positively correlated with the streaming media score. The interaction records of the target streaming media under different accounts are different, and the interaction scoring factors are also different.

[0059] Optionally, the interaction scoring factor can be determined based on the interaction operation type, interaction operation frequency, and interaction operation time in the interaction record. For example, different interaction operation types of the target streaming media correspond to different interaction scoring factors. For another example, the interaction operation frequency of the target streaming media is positively correlated with the interaction scoring factor. For another example, the time interval between the interaction operation time of the target streaming media and the current time is negatively correlated with the interaction scoring factor.

[0060] It should be noted that step 201 and step 202 may be executed synchronously, and the embodiment of the present application does not limit the execution sequence of step 201 and step 202.

[0061] Step 203: Based on the playback scoring factor weights corresponding to each playback scoring factor and the interaction scoring factor weights corresponding to each interaction scoring factor, at least one playback scoring factor and at least one interaction scoring factor are weighted to obtain a streaming media score of the target streaming media.

[0062] Exemplarily, the streaming media score calculation formula 1 of the target streaming media is:

[0063] Formula 1: y1=w1*a+w2*b.

[0064] Among them, a is a single playback scoring factor, w1 is the playback factor weight, b is a single interaction scoring factor, w2 is the interaction scoring factor weight, and y1 is the streaming media score of the target streaming media.

[0065] Exemplarily, the streaming media score calculation formula 2 of the target streaming media is:

[0066] Formula 2: y2=w1*a1+w2*a2+w3*b1+w4*b2.

[0067] Among them, a1 is a single playback scoring factor, w1 is the playback factor weight corresponding to a1, a2 is another playback scoring factor, w2 is the playback factor weight corresponding to a2, b1 is a single interaction scoring factor, w3 is the interaction scoring factor weight corresponding to b1, b2 is another interaction scoring factor, w4 is the interaction scoring factor weight corresponding to b2, and y2 is the streaming media score of the target streaming media.

[0068] Optionally, the playback scoring factor weight and the interaction scoring factor weight can be adjusted according to actual needs.

[0069] In the embodiment of the present application, the computer device performs weighted calculation on various interaction scoring factors and playback scoring factors to determine the streaming media score, so that different scoring factors have different degrees of influence on the streaming media score, which can meet actual needs and is conducive to improving the accuracy of the streaming media score.

[0070] It should be noted that, since different users have different usage habits for streaming media, the computer device determines different streaming media scores for target streaming media under different accounts, that is, the computer device determines the streaming media score of the target streaming media based on the playback records and interaction records under the current account.

[0071] Optionally, the method for determining at least one playback score factor may include at least one of the following:

[0072] The first method is to determine the average number of playbacks of the streaming media under the current account. Based on the target number of playbacks of the target streaming media under the current account and the average number of playbacks, a first playback scoring factor is determined, and the first playback scoring factor is positively correlated with the target number of playbacks.

[0073] In some embodiments, the average number of plays is the average number of plays between different streaming media in the historical play records under the current account. For example, under the current account, streaming media A is played 3 times, streaming media B is played 2 times, and streaming media C is played 4 times, and the computer device determines that the average number of plays is 3 times.

[0074] In some embodiments, the target number of plays refers to the number of plays of the target streaming media in the historical play records of the current account. Since the situations of streaming media played by users under different accounts are different, a unified measurement standard cannot be used for all users. Therefore, in the embodiments of the present application, the first play score factor is determined by combining the average number of plays of the streaming media under the current account and the target number of plays. The average number of plays under different accounts is different.

[0075] Exemplarily, the first playback scoring factor is the ratio of the target playback times to the average playback times. If the average playback times are 40 times and the target playback times are 10 times, then the first playback scoring factor is 0.25. It should be noted that this example is for reference only and is not used to limit the method for determining the first playback scoring factor. The first playback scoring factor can also be determined in other ways. For example, a computer device determines the ratio of the square of the target playback times to the average playback times as the first playback scoring factor, thereby enhancing the effect of distinguishing the playback times between different streaming media.

[0076] Optionally, the average number of playbacks is negatively correlated with the first playback scoring factor.

[0077] The second method is to determine the average playback interval of the streaming media under the current account. Based on the real-time playback interval and the average playback interval of the target streaming media under the current account, the second playback scoring factor is determined. The real-time playback interval is the time interval between the current moment and the last time the target streaming media was played. The second playback scoring factor is negatively correlated with the real-time playback interval.

[0078] In some embodiments, the average play interval refers to the average duration interval between adjacent play records in the historical play records, for example, the duration interval between the earliest play record and the latest play record in the historical play records divided by the number of historical play records.

[0079] Since the real-time playback time of the target streaming media is negatively correlated with the probability that the user plays the target streaming media again, when the real-time playback time of the target streaming media is shorter, the probability that the user plays the target streaming media again is higher. By increasing the second playback scoring factor, the streaming media score can be improved, thereby reducing the deletion priority. Therefore, the second playback scoring factor is negatively correlated with the real-time playback interval.

[0080] In an illustrative example, a computer device has played 10 songs in the last 24 hours, and each song has been played once, with an average playing interval of 2.4 hours. The current time is 12:00:00. Target song A is played at 8:00:00, and the real-time playing interval is 4 hours. Target song B is played at 9:00:00, and the real-time playing interval is 3 hours. The probability of target song B being played again is higher than the probability of target song A being played again. The second playing score factor corresponding to target song B needs to be higher than the streaming media score factor corresponding to target song A, so that the deletion priority of target song B is lower than the deletion priority of target song A.

[0081] Optionally, the average play interval and the second scoring factor may be positively correlated. The average play interval corresponds to the total play volume of different streaming media in a unit time. The smaller the average play interval, the greater the total play volume of different streaming media in a unit time, and the lower the probability that the user plays the target streaming media again. By reducing the second play scoring factor, the streaming media score can be reduced, thereby increasing the deletion priority.

[0082] Example 1: The computer device plays 10 songs in 24 hours, and each song is played once, with an average playing interval of 2.4 hours. The current time is 12:00:00, and the target song A is played at 8:00:00, with a real-time playing interval of 4 hours.

[0083] Example 2: The computer device plays 10 songs in 12 hours, and each song is played once, with an average playing interval of 1.2 hours. The current time is 12:00:00, and the target song B is played at 8:00:00, with a real-time playing interval of 4 hours. This is the same as the real-time playing interval of the target song A in Example 1.

[0084] Among them, the number of plays per hour in Example 1 is 0.42, and the number of plays per hour in Example 2 is 0.83. The number of plays in Example 2 is larger, and the number of plays in Example 1 is smaller. Under the same real-time playback interval, compared with the target song B in Example 2, the real-time probability of the target song A being played again in Example 1 is lower, and a higher second scoring factor is required. Under the same real-time playback interval, compared with the target song A in Example 1, the real-time probability of the target song B being played again in Example 2 is higher, and a lower second scoring factor is required.

[0085] The third method is to determine the average playback ratio of the streaming media under the current account. Based on the target playback ratio and the average playback ratio of the target streaming media under the current account, a third playback scoring factor is determined, and the third playback scoring factor is positively correlated with the target playback ratio.

[0086] In some embodiments, the target playback ratio is the playback ratio of the target streaming media. The playback ratio refers to the ratio of the played content to the total content of the streaming media.

[0087] In some embodiments, the average play ratio refers to the average value of the ratio of played content to the total content of the streaming media corresponding to the streaming media cache. The average play ratio under different accounts is different.

[0088] For example, the complete playback time of streaming media 1 is 4 minutes, of which the played time is 1 minute, and the playback ratio is 0.25; the complete playback time of streaming media 2 is 4 minutes, of which the played time is 3 minutes, and the playback ratio is 0.75; the complete playback time of streaming media 3 is 5 minutes, of which the played time is 1 minute, and the playback ratio is 0.2, then the average playback ratio is 0.4.

[0089] In some embodiments, the target playback ratio is positively correlated with the user's interest in the target streaming media. The larger the target playback ratio is, the higher the user's interest in the target streaming media is. By increasing the third playback scoring factor, the deletion priority of the target streaming media can be reduced, so that the deletion time of the target streaming media is delayed, so the third playback scoring factor is positively correlated with the target playback ratio.

[0090] Optionally, by comparing the target playback ratio and the average playback ratio of the target streaming media, the computer device can determine the user's attitude towards the target streaming media. For example, if the target playback ratio is greater than the average playback ratio, it indicates that the user is interested in the target streaming media, and if the target playback ratio is less than the average playback ratio, it indicates that the user is not interested in the target streaming media.

[0091] Optionally, the average playback ratio may be negatively correlated with the third playback scoring factor.

[0092] Optionally, the playback scoring factor weight of at least one playback scoring factor may be uniformly determined to be one.

[0093] In the embodiment of the present application, the computer device can determine the playback scoring factor from three aspects: the number of playbacks, the playback interval, and the playback ratio, thereby improving the richness of the playback scoring factor and enabling the playback scoring factor to fully reflect the user's demand for the target streaming media, which is conducive to improving the accuracy of the streaming media scoring.

[0094] In some embodiments, when the playback scoring factor includes a second playback scoring factor, the computer device updates the average playback interval of the streaming media and the real-time time interval of the target streaming media according to the current moment and the updated playback record, thereby determining the real-time second playback scoring factor.

[0095] In some embodiments, when the playback scoring factor includes a third playback scoring factor, the computer device determines a real-time third playback scoring factor based on the playback ratio of the streaming media in the updated playback record, the average playback ratio of the updated streaming media, and the target playback ratio of the target streaming media.

[0096] In some embodiments, when the playback scoring factor includes the first playback scoring factor, the computer device needs to update the playback times in order to update the average playback times and the target playback times, thereby determining the real-time first playback scoring factor.

[0097] Optionally, the computer device may update the number of times n corresponding to the playback of the streaming media to n+1, where n is a positive integer, each time the user triggers the playback of the streaming media.

[0098] Optionally, when the user triggers the playback of the streaming media in different triggering modes, the computer device can update the playback times n corresponding to the streaming media to n+a, where n is greater than 0 and a is greater than 0. Figure 3 As shown, the updating method may include the following steps:

[0099] Step 301 : in the case of streaming media playback, determining a streaming media playback triggering mode, where the playback triggering mode is used to characterize a mode of triggering the playback of streaming media.

[0100] In some embodiments, the playback triggering mode of the streaming media includes at least one of sequential playback triggering, selective playback triggering, search playback triggering, and loop playback triggering.

[0101] In a possible implementation, during the process of playing the streaming media, the computer device determines the playback triggering mode of the streaming media according to the current playback mode, and saves the playback triggering mode in the playback record. For example, if the current playback mode is the sequential playback mode, the playback triggering mode of the streaming media is the sequential playback triggering mode. For another example, if the current playback mode is the loop playback mode, the playback triggering mode of the streaming media is the loop playback triggering mode.

[0102] In another possible implementation, the computer device receives a playback operation on the streaming media, determines a playback trigger mode according to the playback operation, and saves the playback trigger mode in the playback record. For example, when a select playback operation is received for the streaming media in the playlist, the playback trigger mode of the streaming media is determined to be a select playback trigger. For another example, when a search operation is received and a trigger playback operation is received for the search results, the playback trigger mode of the streaming media is determined to be a search playback trigger.

[0103] Step 302: updating the number of times the streaming media is played based on the weight of the number of times the playback triggering method corresponds to, wherein different playback triggering methods correspond to different weights of the number of times the playback is played.

[0104] In some embodiments, different play triggering modes characterize different user interests in streaming media. In the case of using a sequential play trigger, compared with a play triggering mode, the computer device can determine that the user has a lower interest in the streaming media currently played in sequence. In the case of using a selection play trigger, compared with a sequential triggering mode, the computer device can determine that the user has a higher interest in the streaming media currently selected for playback. In the case of using a search play trigger, compared with a selection triggering mode, the computer device can determine that the user has a higher interest in the streaming media currently searched for playback. In the case of using a loop play trigger, compared with a search triggering mode, the computer device can determine that the user has a higher interest in the streaming media currently played in a loop.

[0105] From the above reasoning, it can be known that since different playback triggering modes correspond to different playback count weights, the playback count weight is used to update the playback count. Therefore, when the playback triggering mode is sequential playback trigger, the computer device updates the playback count of the streaming media based on the first playback count weight; when the playback triggering mode is selective playback trigger, the playback count of the streaming media is updated based on the second playback count weight, and the first playback count weight is less than the second playback count weight; when the playback triggering mode is search playback trigger, the playback count of the streaming media is updated based on the third playback count weight, and the second playback count weight is less than the third playback count weight; when the playback triggering mode is loop playback trigger, the playback count of the streaming media is updated based on the fourth playback count weight, and the third playback count weight is less than the fourth playback count weight.

[0106] Among them, the play count weight is positively correlated with the change range of the streaming media play count, so that the play trigger method corresponding to a higher user interest has a higher increase in the play count.

[0107] In the embodiment of the present application, the computer device updates the number of times the streaming media is played according to the weight of the number of times the streaming media is played, and different playback triggering methods correspond to different weights of the number of times the streaming media played by the user through different playback triggering methods has different ranges of change in the number of times the streaming media is played, thereby increasing the proportion of the number of times the streaming media that the user is interested in in the total number of times the streaming media is played, and strengthening the distinction between the streaming media that the user is interested in and the streaming media that the user is not interested in, so that the first playback scoring factor is consistent with the user's interest in the target streaming media, which is conducive to improving the accuracy of the streaming media scoring.

[0108] Among them, the weights of the number of plays corresponding to the sequential play trigger, selective play trigger, search play trigger and loop play trigger show an increasing trend, further clarifying the weights of the number of plays corresponding to different play trigger methods and the relationship between the weights of the number of plays, so that the computer device can update the number of plays of the streaming media, improve the correlation between the first play rating factor and the user's interest, and help improve the accuracy of streaming media ratings.

[0109] In some embodiments, the interaction record includes interaction sub-records corresponding to different interaction operation types, wherein the interaction operation types include both positive interaction operations of the user and negative interaction operations of the user.

[0110] Figure 4 1 is a flow chart of a process for determining an interaction scoring factor provided by an exemplary embodiment of the present application. The process includes the following steps.

[0111] Step 401, determining an interaction operation type corresponding to at least one interaction sub-record in an interaction record of a target streaming media, where the interaction operation type includes at least one of favorite, unfavorite, like, positive comment, negative comment, share, and add to playlist.

[0112] Among them, favorites, likes, positive comments, shares, and adding value playlists are positive user interactions, indicating that the user is interested in the target streaming media. Unfavorites and negative comments are negative user interactions, indicating that the user is not interested in the target streaming media.

[0113] Optionally, the computer device determines the type of interactive operation when receiving a favorite operation, a cancel favorite operation, a like operation, a share operation, and an add to playlist operation.

[0114] Regarding the determination of positive and negative comments, optionally, the computer device may use a comment recognition model to recognize user comments and determine whether the user comments are positive or negative. Optionally, the computer device may also establish a positive word library and a negative word library, and determine that the user comments are positive comments when the comment text contains positive words, and determine that the user comments are negative comments when the comment text contains negative words.

[0115] Step 402: Determine at least one interaction scoring factor based on the interaction operation type corresponding to at least one interaction sub-record, wherein different interaction operation types correspond to different interaction scoring factors.

[0116] Optionally, the computer device may establish a correspondence between the interaction operation type and the interaction scoring factor, and then determine the interaction scoring factor according to the correspondence.

[0117] Optionally, the interaction scoring factor corresponding to the positive interaction operation is greater than zero, and the interaction scoring factor corresponding to the negative interaction operation is less than zero.

[0118] Exemplarily, the interaction score factor corresponding to favorites, likes, positive comments, shares, and adding to a playlist is 1, and the interaction score factor corresponding to unfavorites and negative comments is -1.

[0119] In the embodiment of the present application, the computer device can determine different interaction scoring factors according to different interaction operation types, so that the interaction scoring factors are consistent with the user's attitude towards streaming media, which is conducive to improving the accuracy of streaming media scoring.

[0120] Optionally, when the interaction scoring factor is determined based on the interaction operation type, the interaction scoring factor weight may be determined based on the occurrence frequency of the interaction operation type. Figure 5 As shown, the following steps are included.

[0121] Step 501, based on the streaming media playback records of the current account and the interaction records of each streaming media under the current account, determine the occurrence frequency of various interaction operation types.

[0122] In some embodiments, the interaction record of the streaming media can be used to determine the user's interaction habits. Optionally, the user's interaction habits may include a collection habit, a like habit, a comment habit, a sharing habit, and an adding habit. Optionally, when the frequency of occurrence of the interaction operation type in the historical time is higher than the frequency threshold, the computer device determines that the user has the interaction habit corresponding to the interaction operation type. Optionally, the frequency threshold of the interaction operation type is negatively correlated with the degree of habit of having the interaction habit corresponding to the interaction operation type.

[0123] In some embodiments, user interaction habits have an impact on the accuracy of the interaction scoring factor. For example, if a user has the habit of giving likes, the user's like operation on a streaming media does not represent the user's interest in the streaming media, which may easily lead to an excessively large interaction scoring factor corresponding to the like, thus interfering with the streaming media score.

[0124] Optionally, the streaming media playback record is used to determine the target streaming media so as to query the interaction record of the target streaming media.

[0125] In a possible implementation, the computer device determines the streaming media played by the user in the historical time according to the streaming media playback record of the current account. The computer device classifies the interaction sub-records in the interaction records of different streaming media according to the interaction operation type, and counts the occurrence frequency of the same interaction operation type in the different streaming media interaction types.

[0126] Step 502: Determine the interaction scoring factor weight corresponding to each interaction scoring factor based on the occurrence frequency of each interaction operation type.

[0127] Optionally, the computer device may determine the interaction scoring factor weight corresponding to each interaction scoring factor according to the correspondence between the occurrence frequency of the interaction operation type and the interaction scoring factor weight.

[0128] Exemplarily, when the frequency of occurrence is within the first interval, the weight of the interaction scoring factor is the weight of the first scoring factor, when the frequency of occurrence is within the second interval, the weight of the interaction scoring factor is the weight of the second scoring factor, and when the frequency of occurrence is within the third interval, the weight of the interaction scoring factor is the weight of the third scoring factor. The first interval is adjacent to the second interval, the second interval is adjacent to the third interval, and the upper limit of the first interval is less than the lower limit of the second interval, and the upper limit of the second interval is less than the lower limit of the third interval. The weight of the first scoring factor is less than the weight of the second scoring factor, and the weight of the second scoring factor is less than the weight of the third scoring factor.

[0129] Optionally, the computer device determines the interaction scoring factor weight corresponding to each interaction scoring factor according to the mapping relationship between the occurrence frequency interval and the weight interval of the interaction operation type, wherein the occurrence frequency is negatively correlated with the interaction scoring factor weight.

[0130] Exemplarily, the frequency interval is [0, 1000], the weight interval is [0, 10], and when the frequency of occurrence is 100, the weight of the interaction scoring factor is 9.

[0131] Optionally, the computer device determines the account type of the current account based on the frequency of occurrence of various types of interactive operations; and determines the interaction scoring factor weights corresponding to each interaction scoring factor based on the weight configuration corresponding to the account type, wherein different account types correspond to different weight configurations.

[0132] Among them, the account type of the current account may include an account type with frequent interactions and an account type with infrequent interactions. Optionally, the account type may also determine the sub-account type with frequent interactions based on the interactive operation type of the frequent interactions. For example, frequent collections, frequent comments, and frequent sharing. Optionally, the account type may also be divided into account types based on the frequency of interactions represented by the frequency of occurrence.

[0133] Exemplarily, the account types are divided into advanced account types, intermediate account types and low-level account types. Among them, the frequency of occurrence of interactive operation types under the advanced account type is low, and the frequency of occurrence of interactive operation types under the low-level account type is high. Among them, the weight of the interactive scoring factor corresponding to the advanced account type is 3, the weight of the interactive scoring factor corresponding to the intermediate account type is 2, and the weight of the interactive scoring factor corresponding to the low-level account type is 1.

[0134] Optionally, the computer device determines the importance of interaction operations of various interaction operation types based on the frequency of occurrence of various interaction operation types, where the importance of interaction operations is negatively correlated with the frequency of occurrence; and determines the interaction scoring factor weight corresponding to the interaction scoring factor based on the importance of interaction operations, where the interaction scoring factor weight is positively correlated with the importance of interaction operations.

[0135] Among them, the interactive operation types frequently performed by users may be due to the user's interactive habits, not the user's preferences, and the interactive operation importance of this interactive operation type is relatively low. On the other hand, the interactive operation types that are not frequently performed by users can highlight the user's preferences, and the interactive operation importance of this interactive operation type is relatively high.

[0136] In one possible implementation, the computer device determines the ratio of the occurrence frequency of each interaction operation type to the total occurrence frequency of different interaction operation types as the interaction operation importance, and then determines the interaction scoring factor weight based on the positive correlation between the interaction operation importance and the interaction scoring factor weight.

[0137] In another possible implementation, the computer device determines the ratio of the occurrence frequency of each interaction operation type to the average occurrence frequency of different interaction operation types as the interaction operation importance, and then determines the interaction scoring factor weight based on the positive correlation between the interaction operation importance and the interaction scoring factor weight.

[0138] In the embodiment of the present application, the interaction scoring factor corresponds to the interaction operation type. The computer device determines the interaction scoring factor weight corresponding to the interaction scoring factor according to the occurrence frequency of the interaction operation type, so that the interaction scoring factor weight is consistent with the user's interaction habits, which is conducive to improving the accuracy of streaming media scoring.

[0139] Among them, the computer device determines the account type and the weight configuration corresponding to the account type according to the frequency of occurrence of the interactive operation type, or determines the importance of the interactive operation and the interaction scoring factor weight corresponding to the importance of the interactive operation, so that the computer device can set dedicated weight configurations for different account types and determine the interaction scoring factor weights for different interactive operation types respectively, thereby improving the ability of the computer device to determine the interaction scoring factor weights.

[0140] In other embodiments, in addition to determining the playback scoring factor and the interaction scoring factor, the computer device can also determine other scoring factors and scoring factor weights corresponding to the other scoring factors, and then weight them to obtain a streaming media score.

[0141] See also Figure 6 , Figure 6 FIG. 1 is a flowchart of a process for determining a streaming media score provided by another exemplary embodiment of the present application. The process includes the following steps.

[0142] Step 601, based on the preference matching degree between the target streaming media and the current account, a preference scoring factor is determined, and the preference scoring factor is positively correlated with the preference matching degree.

[0143] Optionally, the preference matching degree may be determined based on the favorites list of the current account.

[0144] In a possible implementation, the computer device determines the proportion of the streaming media type corresponding to the target streaming media in the favorites list of the current account as the preference matching degree.

[0145] Optionally, the preference matching degree may be determined based on the preference tags of the current account.

[0146] In a possible implementation, the computer device determines the streaming media tags with a frequency greater than a frequency threshold as the favorite tags of the current account based on the streaming media tags of the streaming media played by the user in history. The computer device determines the preference matching degree based on the matching degree between the streaming media tags of the target streaming media and the favorite tags of the current account.

[0147] Optionally, the computer device internally stores a positively correlated relationship between the preference matching degree interval and the preference scoring factor, and the computer device determines the preference scoring factor according to the corresponding relationship.

[0148] Optionally, the computer device converts the preference matching degree to obtain the preference scoring factor. For example, the computer device maps the preference matching degree from the matching degree interval to the scoring factor interval.

[0149] Step 602, based on the playback records of the target streaming media, the historical playback scenarios of the target streaming media, and the streaming media attributes of the target streaming media, the playback time is estimated through a large language model; based on the time interval between the estimated playback time and the current time, a time scoring factor is determined, and the time scoring factor is negatively correlated with the time interval.

[0150] Optionally, the historical playback scenes of the target streaming media can be determined based on time, location, and real-time events. For example, the historical playback scenes are morning, afternoon, and late night. Another example is the historical playback scenes are coffee shops, tea houses, and gyms. Another example is the historical playback scenes are weddings, commuting, and sports.

[0151] Optionally, the streaming media attributes of the target streaming media are used to characterize the characteristics of the target streaming media. For example, the style, theme, category, name, introduction, etc. of the target streaming media. For example, when the target streaming media is a song, the streaming media attributes of the target streaming media may include the song name, song style, song type, song theme, song source, etc. For example, when the target streaming media is a video, the streaming media attributes of the target streaming media may include the video type, video theme, picture style, etc.

[0152] In some embodiments, the estimated play time refers to the play time of the target streaming media estimated by the large language model. The longer the time interval between the estimated play time and the current moment, the lower the possibility that the user plays the target streaming media. In the case where the time interval between the estimated play time and the current moment is long, the deletion priority of the target streaming media should be higher, the streaming media score should be lower, and the time score factor should be lower. From the above reasoning, it can be seen that the time score factor is negatively correlated with the time interval.

[0153] Exemplarily, the computer device inputs the playback records of the target streaming media, the historical playback scenes of the target streaming media, the streaming media attributes of the target streaming media, and the prompt text for prompting the playback time estimation into the large language model to obtain the estimated playback time output by the large language model.

[0154] Step 603, based on the playback scoring factor weights corresponding to each playback scoring factor, the interaction scoring factor weights corresponding to each interaction scoring factor, the preference scoring factor weights corresponding to the preference scoring factor, and the time scoring factor weights corresponding to the time scoring factor, weight at least one playback scoring factor, at least one interaction scoring factor, the preference scoring factor, and the time scoring factor are weighted to obtain a streaming media score for the target streaming media.

[0155] Exemplarily, the streaming media score calculation formula 3 of the target streaming media is:

[0156] Formula 3: y3=w1*a+w2*b+w3*c+w4*d.

[0157] Among them, a is a single playback scoring factor, w1 is the playback factor weight corresponding to a, b is a single interaction scoring factor, w2 is the interaction factor weight corresponding to b, c is a single preference scoring factor, w3 is the preference scoring factor weight corresponding to c, d is a single time scoring factor, w4 is the time scoring factor weight corresponding to d, and y3 is the streaming media score of the target streaming media.

[0158] Exemplarily, the streaming media score calculation formula 4 of the target streaming media is:

[0159] Formula 4: y4=w1*a1+w2*b1+w3*c1+w4*d1+w5*a2+w6*b2+w7*c2+w8*d2.

[0160] Among them, a1 is a single playback scoring factor, w1 is the playback factor weight corresponding to a1, b1 is a single interaction scoring factor, w2 is the interaction factor weight corresponding to b1, c1 is a single preference scoring factor, w3 is the preference scoring factor weight corresponding to c1, d1 is a single time scoring factor, w4 is the time scoring factor weight corresponding to d1, a2 is a single playback scoring factor, w5 is the playback factor weight corresponding to a2, b2 is a single interaction scoring factor, w6 is the interaction factor weight corresponding to b2, c2 is a single preference scoring factor, w7 is the preference scoring factor weight corresponding to c2, d2 is a single time scoring factor, w8 is the time scoring factor weight corresponding to d2, and y4 is the streaming media score of the target streaming media.

[0161] In an embodiment of the present application, when determining the streaming media score, the computer device further determines the preference score factor and the time score factor, and then performs weighted averaging on different score factors according to different weights to obtain the streaming media score, thereby enriching the determination dimensions of the score factors and facilitating improving the accuracy of the streaming media score.

[0162] In some embodiments, during the deletion process of the streaming media cache, the computer device first determines the deletion amount and then performs the deletion step.

[0163] See also Figure 7 , Figure 7 This is a flow chart of a streaming media cache deletion process based on deletion priority provided by an exemplary embodiment of the present application. The process includes the following steps.

[0164] Step 701: When there is a requirement to delete the streaming media cache, determine the cache capacity to be deleted.

[0165] In some embodiments, the cache capacity to be deleted refers to the cache capacity that the computer device is expected to delete.

[0166] In a possible implementation, the computer device determines the cache capacity to be deleted based on the stored cache capacity. When the stored cache capacity of the streaming media cache reaches the capacity threshold, it is determined that there is a streaming media cache deletion requirement. Subsequently, the computer device determines the capacity greater than the difference between the stored cache capacity and the capacity threshold as the cache capacity to be deleted, so that the stored cache capacity after the streaming media cache is deleted is less than the capacity threshold.

[0167] In another possible implementation, the computer device determines that there is a need to delete the streaming media cache when the sum of the capacity of the cache to be stored and the capacity of the cache that has been stored exceeds the available capacity of the cache space. Subsequently, the computer device determines the capacity greater than the difference between the capacity of the cache that has been stored and the portion that exceeds the available capacity as the cache capacity to be deleted, so that after the streaming media cache is deleted, the sum of the capacity of the cache to be stored and the capacity of the cache that has been stored is less than the available capacity of the cache space.

[0168] Step 702: Delete the streaming media cache based on the cache capacity to be deleted and the deletion priority.

[0169] Optionally, the computer device sorts the streaming media caches according to the deletion priority, and selects the streaming media caches for deletion according to the capacity of the caches to be deleted. The specific steps include the following.

[0170] Sub-step 1: based on the capacity of the buffer to be deleted, determine the first n streaming media buffers in descending order of deletion priority, and the total capacity of the first n streaming media buffers is greater than or equal to the capacity of the buffer to be deleted.

[0171] Exemplarily, the capacity of the cache to be deleted is 10MB. The sequence of the streaming media cache capacity in descending order of deletion priority is 5MB, 4MB, 3.5MB, and 2.5MB. Among them, the capacity of the first two streaming media caches is 9MB, which is less than the capacity of the cache to be deleted, and the capacity of the first three streaming media caches is 12.5MB, which is greater than the capacity of the cache to be deleted. The computer device determines the first three streaming media caches.

[0172] Sub-step 2: when the streaming media scores of the target streaming media corresponding to the first n streaming media caches are all less than the score threshold, the first n streaming media caches are deleted.

[0173] In which, when the streaming media scores of the target streaming media are all less than the score threshold, the user's demand for the target streaming media is low, and the computer device can automatically delete the first n streaming media caches.

[0174] Sub-step 3: If there is at least one streaming media cache in the first n streaming media caches whose corresponding target streaming media has a streaming media score greater than the score threshold, display a cache deletion prompt; if a confirmation deletion operation for the cache deletion prompt is received, delete the first n streaming media caches.

[0175] The target streaming media with a streaming media score greater than the score threshold is a streaming media with a high degree of importance and may be a commonly used streaming media. In order to avoid accidentally deleting commonly used streaming media and causing rebuffering, the computer device can display a cache deletion prompt before deleting the target streaming media.

[0176] Alternatively, the cache deletion prompt may be used to prompt the streaming media name of the target streaming media whose streaming media score is greater than the score threshold, or may simply prompt whether to perform streaming media cache deletion.

[0177] Exemplarily, the cache deletion prompt is displayed as “It is detected that the deleted content contains important cache content, do you confirm deletion?” When the computer device receives the confirmation deletion operation for the cache deletion prompt, it deletes the first n streaming media caches.

[0178] Exemplarily, the cache deletion prompt is displayed as "The cache to be deleted contains a song cache with a song name of xxxx, please confirm whether to delete it."

[0179] Optionally, when receiving a cancel deletion operation on a cache deletion prompt, the computer device retains the target streaming media in the first n streaming media caches whose streaming media scores are greater than the scoring threshold, and deletes the target streaming media in the first n streaming media caches whose streaming media scores are less than the scoring threshold.

[0180] In the embodiment of the present application, the computer device deletes the streaming media cache according to the cache capacity to be deleted and the deletion priority, so that important streaming media cache can be retained after the streaming media cache is deleted, and the streaming media cache deletion requirements are met to ensure sufficient cache capacity.

[0181] Among them, when determining the streaming media cache to be deleted according to the descending order of deletion priority, the computer device determines whether to display the cache deletion prompt based on the streaming media scores of the first n streaming media caches, which can alleviate the problem of deleting important streaming media caches, help reduce the number of re-buffering times, and save traffic.

[0182] See also Figure 8 , Figure 8 This is a structural block diagram of a streaming media cache deletion device provided by an exemplary embodiment of the present application. The device includes the following modules.

[0183] A score determination module 801 is used to determine a streaming media score of a target streaming media corresponding to a streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media;

[0184] A priority determination module 802, configured to determine a deletion priority of the streaming media cache based on the streaming media score, wherein the deletion priority is negatively correlated with the streaming media score;

[0185] The deleting module 803 is used to delete the streaming media cache based on the deletion priority when there is a streaming media cache deletion demand.

[0186] Optionally, the score determination module 801 is further used to:

[0187] Determining at least one playback scoring factor based on the playback record of the target streaming media;

[0188] Determining at least one interaction scoring factor based on the interaction record of the target streaming media;

[0189] Based on the playback scoring factor weights corresponding to each of the playback scoring factors and the interaction scoring factor weights corresponding to each of the interaction scoring factors, at least one of the playback scoring factors and at least one of the interaction scoring factors are weighted to obtain the streaming media score of the target streaming media.

[0190] Optionally, the score determination module 801 is further used to:

[0191] Determine an average number of plays of a streaming media under a current account; determine a first play scoring factor based on a target number of plays of the target streaming media under the current account and the average number of plays, wherein the first play scoring factor is positively correlated with the target number of plays;

[0192] Determine an average playback interval of the streaming media under the current account; determine a second playback scoring factor based on the real-time playback interval of the target streaming media under the current account and the average playback interval, wherein the real-time playback interval is the time interval between the current moment and the last playback of the target streaming media, and the second playback scoring factor is negatively correlated with the real-time playback interval;

[0193] Determine an average playback ratio of the streaming media under the current account; and determine a third playback scoring factor based on the target playback ratio of the target streaming media under the current account and the average playback ratio, wherein the third playback scoring factor is positively correlated with the target playback ratio.

[0194] Optionally, when the playback scoring factor includes the first playback scoring factor, the apparatus further includes an updating module, configured to:

[0195] In the case of streaming media playback, determining a playback triggering mode of the streaming media, wherein the playback triggering mode is used to characterize a mode of triggering the playback of the streaming media;

[0196] The number of times the streaming media is played is updated based on the weight of the number of times the playback corresponds to the playback triggering mode, wherein different playback triggering modes correspond to different weights of the number of times the playback occurs.

[0197] Optionally, the update module is further used to:

[0198] When the play triggering mode is a sequential play triggering, updating the play times of the streaming media based on a first play times weight;

[0199] When the play triggering mode is a selective play triggering, updating the play times of the streaming media based on a second play times weight;

[0200] When the play triggering mode is a search play triggering, updating the play count of the streaming media based on a third play count weight;

[0201] When the play triggering mode is a loop play triggering, updating the play times of the streaming media based on a fourth play times weight;

[0202] Among them, the first playback number weight is smaller than the second playback number weight, the second playback number weight is smaller than the third playback number weight, and the third playback number weight is smaller than the fourth playback number weight.

[0203] Optionally, the score determination module 801 is further used to:

[0204] Determine an interaction operation type corresponding to at least one interaction sub-record in the interaction record of the target streaming media, where the interaction operation type includes at least one of favorite, unfavorite, like, positive comment, negative comment, share, and add to playlist;

[0205] At least one interaction scoring factor is determined based on the interaction operation type corresponding to at least one interaction sub-record, wherein different interaction operation types correspond to different interaction scoring factors.

[0206] Optionally, the device further includes a weight determination module, configured to:

[0207] Based on the streaming media playback records of the current account and the interaction records of each streaming media under the current account, determining the occurrence frequency of each type of interaction operation;

[0208] The interaction scoring factor weight corresponding to each interaction scoring factor is determined based on the occurrence frequencies of various interaction operation types.

[0209] Optionally, the weight determination module is further used to:

[0210] Determine the account type of the current account based on the occurrence frequencies of various types of interactive operations; determine the interaction scoring factor weights corresponding to each interaction scoring factor based on the weight configuration corresponding to the account type, wherein different account types correspond to different weight configurations;

[0211] or,

[0212] Based on the occurrence frequency of various types of interaction operations, the interaction operation importance of various types of interaction operations is determined, and the interaction operation importance is negatively correlated with the occurrence frequency; based on the interaction operation importance, the interaction scoring factor weight corresponding to the interaction scoring factor is determined, and the interaction scoring factor weight is positively correlated with the interaction operation importance.

[0213] Optionally, the score determination module 801 is further used to:

[0214] Determining a preference scoring factor based on the preference matching degree between the target streaming media and the current account, wherein the preference scoring factor is positively correlated with the preference matching degree;

[0215] Based on the playback record of the target streaming media, the historical playback scene of the target streaming media, and the streaming media attributes of the target streaming media, the playback time is estimated by using a large language model; based on the time interval between the estimated playback time and the current time, a time scoring factor is determined, and the time scoring factor is negatively correlated with the time interval;

[0216] The step of weighting at least one of the playback scoring factors and at least one of the interaction scoring factors based on the playback scoring factor weights corresponding to each of the playback scoring factors and the interaction scoring factor weights corresponding to each of the interaction scoring factors to obtain the streaming media score of the target streaming media includes:

[0217] Based on the playback scoring factor weights corresponding to each of the playback scoring factors, the interaction scoring factor weights corresponding to each of the interaction scoring factors, the preference scoring factor weights corresponding to the preference scoring factor, and the time scoring factor weights corresponding to the time scoring factor, at least one of the playback scoring factors, at least one of the interaction scoring factors, the preference scoring factor, and the time scoring factor are weighted to obtain the streaming media score of the target streaming media.

[0218] Optionally, the deleting module 803 is further used to:

[0219] In case there is a need to delete the streaming media cache, determine the cache capacity to be deleted;

[0220] The streaming media cache is deleted based on the to-be-deleted cache capacity and the deletion priority.

[0221] Optionally, the deleting module 803 is further used to:

[0222] Based on the capacity of the buffer to be deleted, determining the first n streaming media buffers in descending order of the deletion priority, the total capacity of the first n streaming media buffers being greater than or equal to the capacity of the buffer to be deleted;

[0223] When the streaming media scores of the target streaming media corresponding to the first n streaming media caches are all less than the score threshold, deleting the first n streaming media caches;

[0224] If there is at least one streaming media cache among the first n streaming media caches whose corresponding streaming media score of the target streaming media is greater than the score threshold, a cache deletion prompt is displayed; if a confirmation deletion operation for the cache deletion prompt is received, the first n streaming media caches are deleted.

[0225] In the related art, a computer device sorts streaming media caches according to creation time. However, deleting streaming media caches with an earlier creation time may easily lead to deletion of frequently used streaming media caches.

[0226] In the embodiment of the present application, the computer device calculates the streaming media score according to the playback record and interaction record of the target streaming media, so that the streaming media score can reflect the user's use of the target streaming media. The computer device determines the deletion priority according to the streaming media score, and then deletes the streaming media cache according to the deletion priority, so that when deleting the streaming media cache, the computer device can determine whether to delete it according to whether the target streaming media is frequently used, which is conducive to alleviating the problem of re-buffering of frequently used streaming media due to deleting the streaming media cache of frequently used streaming media, reducing the number of re-buffering of streaming media and saving traffic resources.

[0227] Please refer to Fig. 9 , which shows a structural block diagram of a computer device provided by an exemplary embodiment of the present application. The computer device includes: a processor 901 and a memory 902.

[0228] The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 may be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 901 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 901 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 901 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0229] The memory 902 may include one or more computer-readable storage media, which may be tangible and non-transitory. The memory 902 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one instruction, which is used to be executed by the processor 901 to implement the method for deleting the streaming media cache provided in the embodiment of the present application.

[0230] Those skilled in the art will understand that Fig. 9 The structure shown in the figure does not constitute a limitation on the computer device, and may include more or less components than shown in the figure, or combine some components, or adopt a different component arrangement.

[0231] An embodiment of the present application further provides a computer-readable storage medium, which stores at least one computer instruction. The at least one computer instruction is loaded and executed by a processor to implement the method for deleting a streaming media cache provided in the above embodiment.

[0232] Optionally, the computer readable storage medium may include: ROM, RAM, solid state drives (SSD) or optical disks, etc. Among them, RAM may include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

[0233] An embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for deleting the streaming media cache described in the above embodiment.

[0234] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by a program to guide the relevant hardware. The program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0235] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for deleting a streaming media cache, characterized in that: The method comprises: Determine a streaming media score of a target streaming media corresponding to the streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media; Determining a deletion priority of the streaming media cache based on the streaming media score, wherein the deletion priority is negatively correlated with the streaming media score; When there is a need to delete the streaming media cache, the streaming media cache is deleted based on the deletion priority.

2. The method according to claim 1, characterized in that Determining the streaming media score of the streaming media cache corresponding to the target streaming media includes: Determining at least one playback scoring factor based on the playback record of the target streaming media; Determining at least one interaction scoring factor based on the interaction record of the target streaming media; Based on the playback scoring factor weights corresponding to each of the playback scoring factors and the interaction scoring factor weights corresponding to each of the interaction scoring factors, at least one of the playback scoring factors and at least one of the interaction scoring factors are weighted to obtain the streaming media score of the target streaming media.

3. The method according to claim 2, characterized in that The determining of at least one playback scoring factor based on the playback record of the target streaming media includes at least one of the following: Determine an average number of plays of a streaming media under a current account; determine a first play scoring factor based on a target number of plays of the target streaming media under the current account and the average number of plays, wherein the first play scoring factor is positively correlated with the target number of plays; Determine the average playback interval of the media streamed by the current account; Determine a second playback scoring factor based on the real-time playback interval of the target streaming media under the current account and the average playback interval, wherein the real-time playback interval is the time interval between the current moment and the last playback of the target streaming media, and the second playback scoring factor is negatively correlated with the real-time playback interval; Determine the average playback ratio of the streaming media under the current account; Based on the target playback ratio of the target streaming media under the current account and the average playback ratio, a third playback scoring factor is determined, where the third playback scoring factor is positively correlated with the target playback ratio.

4. The method according to claim 3, characterized in that: In the case where the playback scoring factor includes the first playback scoring factor, the method includes: In the case of streaming media playback, determining a playback triggering mode of the streaming media, wherein the playback triggering mode is used to characterize a mode of triggering the playback of the streaming media; The number of times the streaming media is played is updated based on the weight of the number of times the playback corresponds to the playback triggering mode, wherein different playback triggering modes correspond to different weights of the number of times the playback occurs.

5. The method according to claim 4, characterized in that The updating the number of times the streaming media is played based on the weight of the number of times the streaming media is played corresponding to the playback triggering mode includes: When the play triggering mode is a sequential play triggering, updating the play times of the streaming media based on a first play times weight; When the play triggering mode is a selective play triggering, updating the play times of the streaming media based on a second play times weight; When the play triggering mode is a search play triggering, updating the play count of the streaming media based on a third play count weight; When the play triggering mode is a loop play triggering, updating the play times of the streaming media based on a fourth play times weight; Among them, the first playback number weight is smaller than the second playback number weight, the second playback number weight is smaller than the third playback number weight, and the third playback number weight is smaller than the fourth playback number weight.

6. The method according to claim 2, characterized in that The determining at least one interaction scoring factor based on the interaction record of the target streaming media includes: Determine an interaction operation type corresponding to at least one interaction sub-record in the interaction record of the target streaming media, where the interaction operation type includes at least one of favorite, unfavorite, like, positive comment, negative comment, share, and add to playlist; At least one interaction scoring factor is determined based on the interaction operation type corresponding to at least one interaction sub-record, wherein different interaction operation types correspond to different interaction scoring factors.

7. The method according to claim 6, characterized in that The method further comprises: Based on the streaming media playback records of the current account and the interaction records of each streaming media under the current account, determining the occurrence frequency of each type of interaction operation; The interaction scoring factor weight corresponding to each interaction scoring factor is determined based on the occurrence frequencies of various interaction operation types.

8. The method according to claim 7, characterized in that The determining, based on the occurrence frequencies of various types of interactive operations, the interaction scoring factor weights corresponding to each of the interaction scoring factors includes: Determine the account type of the current account based on the occurrence frequencies of various types of interactive operations; determine the interaction scoring factor weights corresponding to each interaction scoring factor based on the weight configuration corresponding to the account type, wherein different account types correspond to different weight configurations; or, Based on the occurrence frequency of various types of interaction operations, the interaction operation importance of various types of interaction operations is determined, and the interaction operation importance is negatively correlated with the occurrence frequency; based on the interaction operation importance, the interaction scoring factor weight corresponding to the interaction scoring factor is determined, and the interaction scoring factor weight is positively correlated with the interaction operation importance.

9. The method according to claim 2, characterized in that: The step of determining the streaming media score of the streaming media cache corresponding to the target streaming media also includes: Determining a preference scoring factor based on the preference matching degree between the target streaming media and the current account, wherein the preference scoring factor is positively correlated with the preference matching degree; Based on the playback record of the target streaming media, the historical playback scene of the target streaming media, and the streaming media attributes of the target streaming media, the playback time is estimated by using a large language model; based on the time interval between the estimated playback time and the current time, a time scoring factor is determined, and the time scoring factor is negatively correlated with the time interval; The step of weighting at least one of the playback scoring factors and at least one of the interaction scoring factors based on the playback scoring factor weights corresponding to each of the playback scoring factors and the interaction scoring factor weights corresponding to each of the interaction scoring factors to obtain the streaming media score of the target streaming media includes: Based on the playback scoring factor weights corresponding to each of the playback scoring factors, the interaction scoring factor weights corresponding to each of the interaction scoring factors, the preference scoring factor weights corresponding to the preference scoring factor, and the time scoring factor weights corresponding to the time scoring factor, at least one of the playback scoring factors, at least one of the interaction scoring factors, the preference scoring factor, and the time scoring factor are weighted to obtain the streaming media score of the target streaming media.

10. The method according to any one of claims 1 to 9, characterized in that: When there is a requirement to delete the streaming media cache, deleting the streaming media cache based on the deletion priority includes: In case there is a need to delete the streaming media cache, determine the cache capacity to be deleted; The streaming media cache is deleted based on the to-be-deleted cache capacity and the deletion priority.

11. The method according to claim 10, characterized in that The deleting the streaming media cache based on the cache capacity to be deleted and the deletion priority includes: Based on the capacity of the buffer to be deleted, determining the first n streaming media buffers in descending order of the deletion priority, the total capacity of the first n streaming media buffers being greater than or equal to the capacity of the buffer to be deleted; When the streaming media scores of the target streaming media corresponding to the first n streaming media caches are all less than the score threshold, deleting the first n streaming media caches; If there is at least one streaming media cache among the first n streaming media caches whose corresponding streaming media score of the target streaming media is greater than the score threshold, a cache deletion prompt is displayed; if a confirmation deletion operation for the cache deletion prompt is received, the first n streaming media caches are deleted.

12. A device for deleting a streaming media cache, characterized in that: The device comprises: A score determination module, used to determine a streaming media score of a target streaming media corresponding to the streaming media cache, wherein the streaming media score is determined based on at least a playback record and an interaction record of the target streaming media, wherein the interaction record is used to characterize an interaction operation performed on the target streaming media; A priority determination module, configured to determine a deletion priority of the streaming media cache based on the streaming media score, wherein the deletion priority is negatively correlated with the streaming media score; The deletion module is used to delete the streaming media cache based on the deletion priority when there is a streaming media cache deletion demand.

13. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one computer instruction, and the at least one computer instruction is loaded and executed by the processor to implement the method for deleting a streaming media cache as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer instruction, and the at least one computer instruction is loaded and executed by a processor to implement the method for deleting a streaming media cache as described in any one of claims 1 to 11.

15. A computer program product, characterized in that The computer program product includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device implements the method for deleting a streaming media cache as described in any one of claims 1 to 11.

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