CN112241327A - Shared information processing method and device, storage medium and electronic equipment - Google Patents
Shared information processing method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure provides a shared information processing method and device, a storage medium and electronic equipment, and relates to the technical field of human-computer interaction. The method comprises the following steps: the method comprises the steps that information to be shared and a user identification for sending the information to be shared are obtained from a terminal, and the type of the information to be shared is identified and used as a target type; when the user preference data corresponding to the target type under the user identification is found, determining a target sharing position of the information to be shared according to the user preference data; when the user preference data corresponding to the target type under the user identification is not found, predicting a target sharing position of the information to be shared according to a sharing prediction model; and sending the target sharing position to the terminal so as to preferentially display the target sharing position on the terminal. The method and the device can simplify the process of sharing operation of the user, facilitate the user to select the target position to be shared, and have higher flexibility and robustness.
Description
Technical Field
The present disclosure relates to the field of human-computer interaction technologies, and in particular, to a shared information processing method, a shared information processing apparatus, a computer-readable storage medium, and an electronic device.
Background
When a user uses electronic devices such as a smart phone and a personal computer daily, the user often needs to share information with other users, such sharing behavior may be across APPs (applications), for example, sharing news in a news APP to a social APP, sharing music in a music APP to a network disk, or within one APP, for example, sharing an article in the social APP to a friend or a group in the social APP.
At present, when a user needs to share information, all possible sharing positions are usually displayed on a sharing interface, and then the user selects a target position to be shared. The processing method increases the complexity of user operation, and particularly when more information-sharable APPs are installed in the electronic device, the user needs to drag the interface to find the target position, or when some electronic devices set multi-level options on the sharing interface, or a part of the options are folded, the user is very inconvenient to operate when selecting the target position, and user experience is affected.
Therefore, how to enable users to conveniently share information is a problem to be urgently solved at present.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a shared information processing method, a shared information processing apparatus, a computer-readable storage medium, and an electronic device, so as to improve the problem of inconvenient user operation in the current information sharing process at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a shared information processing method including: the method comprises the steps that information to be shared and a user identification for sending the information to be shared are obtained from a terminal, and the type of the information to be shared is identified and used as a target type; when the user preference data corresponding to the target type under the user identification is found, determining a target sharing position of the information to be shared according to the user preference data; when the user preference data corresponding to the target type under the user identification is not found, determining a target sharing position of the information to be shared through a sharing prediction model; and sending the target sharing position to the terminal so as to preferentially display the target sharing position on the terminal.
Optionally, the user preference data corresponding to the target type under the user identifier is obtained by the following method: collecting historical sharing events corresponding to the user identification; counting the historical sharing events of each type according to the types of the information shared by the historical sharing events; and calculating user preference data corresponding to the target type under the user identification based on the time distribution characteristics of the historical sharing events of the target type.
Optionally, the user preference data corresponding to the target type includes: the information of the target type is shared to the preference degree of each sharing position; calculating user preference data corresponding to the target type under the user identification based on the time distribution characteristics of the historical sharing events of the target type, wherein the calculation comprises the following steps: arranging the history sharing events of the target type according to the time sequence, merging the history sharing events which are continuous and have the same sharing position into a history sharing event set, and respectively forming the history sharing event set of a single event by the history sharing events which are not merged; calculating the weight of each history sharing event set according to the sequence of each history sharing event set and the number of events in each history sharing event set; and combining the weights of the historical sharing event sets with the same sharing position, and calculating the preference degree of the information of the target type shared to each sharing position.
Optionally, when the user preference data is calculated, if a preset condition is met, determining that the user preference data is a null value; wherein the preset condition comprises any one or combination of more of the following: the number of the historical sharing events corresponding to the target type is less than a first threshold value; the number of the history sharing events corresponding to the target type in the latest preset period is less than a second threshold value; the sharing preference degrees of all the sharing positions are lower than a third threshold value.
Optionally, the target sharing location includes: the sharing position with the highest preference degree or the sharing position with the preference degree higher than a preset preference threshold value.
Optionally, the shared prediction model is obtained by: acquiring historical sharing events of different users, and extracting a plurality of groups of sample data from the historical sharing events, wherein each group of sample data comprises user information and any N +1 continuous historical sharing events which correspond to the user information and have the same type of shared information, and N is a positive integer not less than 2; and training a machine learning model by taking the user information and the sharing position of the former N events in the N +1 historical sharing events as training data and taking the sharing position of the last event in the N +1 historical sharing events as marking data to obtain the sharing prediction model.
Optionally, the information to be shared is information shared in a target application program, and the target sharing position is a sharing position in the target application program.
According to a second aspect of the present disclosure, there is provided a shared information processing apparatus including: the device comprises an acquisition module, a sharing module and a processing module, wherein the acquisition module is used for acquiring information to be shared from a terminal, sending a user identifier of the information to be shared, and identifying the type of the information to be shared as a target type; the first processing module is used for determining a target sharing position of the information to be shared according to the user preference data when the user preference data corresponding to the target type under the user identification is found; the second processing module is used for predicting a target sharing position of the information to be shared according to the sharing prediction model when the user preference data corresponding to the target type under the user identification is not found; and the sending module is used for sending the target sharing position to the terminal so as to preferentially display the target sharing position on the terminal.
Optionally, the shared information processing apparatus further includes: and the user preference analysis module is used for collecting the historical sharing events corresponding to the user identification, counting the historical sharing events of each type according to the type of the information shared by each historical sharing event, and calculating user preference data corresponding to the target type under the user identification based on the time distribution characteristics of the historical sharing events of the target type.
Optionally, the user preference data corresponding to the target type includes: the information of the target type is shared to the preference degree of each sharing position; the user preference analysis module is further configured to arrange the history sharing events of the target type according to a time sequence, merge the history sharing events that are continuous and have the same sharing position into a history sharing event set, form a history sharing event set of a single event respectively for the history sharing events that are not merged, calculate a weight of each history sharing event set according to the sequence of each history sharing event set and the number of events therein, merge the weights of the history sharing event sets having the same sharing position, and calculate a preference degree of the information of the target type shared to each sharing position.
Optionally, the user preference analysis module is further configured to determine that the user preference data is a null value if a preset condition is met when the user preference data is calculated; wherein the preset condition comprises any one or combination of more of the following: the number of the historical sharing events corresponding to the target type is less than a first threshold value; the number of the history sharing events corresponding to the target type in the latest preset period is less than a second threshold value; the sharing preference degrees of all the sharing positions are lower than a third threshold value.
Optionally, the target sharing location includes: the sharing position with the highest preference degree or the sharing position with the preference degree higher than a preset preference threshold value.
Optionally, the shared information processing apparatus further includes: the model training module is used for acquiring historical sharing events of different users, extracting multiple groups of sample data from the historical sharing events, wherein each group of sample data comprises user information, any N +1 continuous historical sharing events corresponding to the user information and having the same shared information type, N is a positive integer not less than 2, the user information and the shared positions of the former N events in the N +1 historical sharing events are used as training data, the shared position of the last event in the N +1 historical sharing events is used as marking data, and a machine learning model is trained to obtain the sharing prediction model.
Optionally, the information to be shared is information shared in a target application program, and the target sharing position is a sharing position in the target application program.
According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the above-described shared information processing methods.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above-described shared information processing methods via execution of the executable instructions.
The technical scheme of the disclosure has the following beneficial effects:
the server obtains the information to be shared and the user identification from the terminal, searches user preference data according to the user identification and the type of the information to be shared, determines a target sharing position of the information to be shared according to the user preference data if the user preference data are found, determines the target sharing position through a sharing prediction model if the user preference data are not found, and finally returns the target sharing position to the terminal to be preferentially displayed on the terminal. On one hand, whether the target sharing position is based on user preference data or a sharing prediction model, the determined target sharing position predicts the position of the user sharing the information to be shared, the position is preferentially displayed on a sharing page on the terminal, the user can conveniently share the information, the target position can be selected without sliding the page or clicking for multiple times, the operation flow is simplified, and the user experience is improved; on the other hand, by adopting a double way of user preference data and sharing a prediction model, the flexibility of processing shared information can be improved, the sharing intention of the user can be reasonably predicted under the condition of lacking user historical shared data, and the robustness is higher.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is apparent that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings can be obtained from those drawings without inventive effort for a person skilled in the art.
Fig. 1 to 3 are schematic diagrams illustrating pages for sharing information in the related art;
FIG. 4 is a system architecture diagram illustrating the environment in which the exemplary embodiment operates;
fig. 5 shows a flowchart of a shared information processing method in the present exemplary embodiment;
fig. 6 shows a sub-flowchart of a shared information processing method in the present exemplary embodiment;
fig. 7 is a block diagram showing the configuration of a shared information processing apparatus in the present exemplary embodiment;
fig. 8 shows an electronic device for implementing the above method in the present exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In a scheme of the related art, a process of sharing information may be as shown in fig. 1, where a user shares a piece of news in a news APP of a mobile phone, and pops up a sharing page, where sharable location options are provided, including friends, work groups, trends, blogs, and the like, and the user selects a target location to be shared, and shares the news to the target location. Due to the size limitations of the page, only 4 options can be displayed in the page of fig. 1, and as shown in fig. 2, sliding the page to the right can display the remaining options. If there are more locations to share, or the screen size is smaller, then multiple pages need to be occupied. Therefore, when the user selects the sharing position, the user needs to slide the page to find the target position. In addition, the option displayed in the page may be a sub-menu, which also includes a plurality of specific sharing positions, as shown in fig. 2 and 3, the "system" option in the page is the sub-menu, and after the "system" option is clicked, a plurality of sharing positions under the "system" are displayed, such as short messages, mails, and the like, so that the user needs to perform one more selection operation, and sometimes the option not displayed is folded into the "more" option, so that the user needs to perform further operations. Therefore, in the process of sharing information, a user needs to perform multi-step operation to select the target position, which is very inconvenient.
In view of one or more of the above problems, the present exemplary embodiment first provides a shared information processing method. It should be noted that the sharing according to the exemplary embodiment includes various similar operations such as sharing, forwarding, sending to a friend, and the like. Fig. 4 shows a system architecture diagram of the method operating environment. As shown in fig. 4, system 400 may include: one or more of the terminal devices 401, 402, 403, a network 404 and a server 405. The terminal devices 401, 402, and 403 may be various electronic devices with display screens, including but not limited to desktop computers, portable computers, smart phones, tablet computers, and the like; the network 404 is a medium used to provide communication links between the terminal devices 401, 402, 403 and the server 405, and may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others; the server 405 may be a backend server that provides a sharing service. It should be understood that the number of terminal devices and servers in fig. 4 is merely illustrative. There may be any number of terminal devices and servers, depending on the actual needs. For example, the server 405 may be a server cluster composed of a plurality of servers, and the like.
The shared information processing method according to the present exemplary embodiment may be executed on the server 405. Fig. 5 shows a flow of the method, which may include the following steps S510 to S540:
step S510, obtaining information to be shared from the terminal and sending a user identifier of the information to be shared, and identifying a type of the information to be shared as a target type.
In the exemplary embodiment, a user performs an operation of sharing information on a terminal, generally, clicks a sharing option in a page of information to be shared, and may trigger the terminal to send the information to be shared to a server, where the information to be shared may be various types such as news, music, videos, documents, pictures, and the like, and the server identifies the types of the information to be shared, and uses the types as target types for subsequent processing. The server can classify the sharable information in advance to different degrees, the classification granularity is not limited in the method, for example, all news can be used as one type of information, the news can be further classified, and more subdivided types of current news, sports news, scientific news and the like are determined based on division of news sections in the APP.
When the terminal sends the information to be shared, the information is sent through a specific user identifier, for example, a user shares an article in a social Application (APP), the user identifier may be an APP account number, a mobile phone number and the like of the user, and a message received by the server includes related information of the user identifier.
In an alternative embodiment, the information to be shared may be information shared by the user in the target application, for example, an article, a file, a link, and the like shared by the user in a social APP.
Step S520, when the user preference data corresponding to the target type under the user identifier is found, determining a target sharing position of the information to be shared according to the user preference data.
The user preference data is conclusive data of which sharing position or sharing positions the server counts the historical sharing events of the information of the user sharing target type, and the conclusive data is obtained, and the conclusive data includes: and sharing positions which are most preferred when the user shares the target type of information, or the preference degree of sharing the target type of information to each sharing position. The server may store the user preference data via a data table, indexed by the user identification and type of information. After the user preference data corresponding to the target type under the user identification is found, one or more sharing positions most preferred by the user can be determined as target sharing positions, and if a plurality of target sharing positions are determined, sorting can be performed according to the preference degrees.
In an optional implementation manner, the target sharing location may be a sharing location in the target application program, for example, when the user shares information in a certain social APP, different location modules of the social APP may be shared, including a friend, a group, a dynamic state, a space, a favorite, and the like.
Step S530, when the user preference data corresponding to the target type under the user identifier is not found, determining a target sharing position of the information to be shared through the sharing prediction model.
The condition that the user preference data is not found may include: the user shares the information of the target type for the first time, or the number of times of sharing the information of the target type is small, the server is not enough to count the preference of the user, or the last time that the user shares the information of the target type is longer than the current time, the preference of the user is possibly changed, and the like. In the exemplary embodiment, the sharing prediction model is a machine learning model obtained by a server based on big data of different users through training in advance, and is used for predicting a target sharing position of information shared by the users next time.
In an optional implementation manner, user information (such as user age, gender, registration time, login frequency, and member level) and a target type corresponding to the user identifier may be input into the sharing prediction model, or information of previous sharing events of the user may also be input into the sharing prediction model, and the specific information content of the input model is not limited in the present disclosure, which is related to the model training process. And after the model is processed, outputting the target sharing position.
Step S540, sending the target sharing location to the terminal, so as to preferentially display the target sharing location on the terminal.
After the target sharing position of the information to be shared is determined in step S520 or step S530, the server sends the target sharing position back to the terminal, and the terminal preferentially displays the target sharing position on the sharing page, so that the user can select the target sharing position, or when only one target sharing position is provided, the user can directly jump to the target sharing position, and the step of selecting the information by the user is omitted.
For example: the user prefers to share the file to the network disk, and when the file is shared, the network disk is displayed at the front position of the shared page on the terminal; and the user prefers to share the webpage link to a colleague bar or a chat session of the enterprise communication APP, and when the webpage link is shared, the colleague bar and the chat session are displayed at the front position of the sharing page on the terminal, and the like.
The exemplary embodiment is further described in the following by two aspects of user preference data and sharing a prediction model.
First aspect, user preference data:
the present exemplary embodiment provides a method for obtaining user preference data, which may refer to fig. 6, and includes the following steps S610 to S630:
step S610, collecting a history sharing event corresponding to the user identifier;
step S620, counting the history sharing events of various types according to the types of the information shared by the history sharing events;
step S630, based on the time distribution characteristics of the historical sharing event of the target type, calculating user preference data corresponding to the target type under the user identifier.
The server can record sharing events of the users each time through the log library to form a historical sharing event library which is used as a basis for analyzing sharing preferences of the users, or collect sharing events of the users from all terminals. For example, an interface # content id _ userID _ type may be set on the terminal, where the content id represents shared information content, the userID is a user identifier, the type is a shared information type, such as a chat record msg, a picture pic, a text t, a file d, a video v, a web link h, a public article wx, and the like, and the data may further include a time when the user shares information. The server can acquire the data of the historical sharing events from the terminal through the interface and then perform statistical analysis.
During statistics, the history sharing events are classified and filtered according to the user identifier and the type of the shared information, for example, an interface # Server _ time _ type _ place may be set, and the collected history sharing events are converted, where the place indicates a position in the history sharing events where the information is shared. For example, table 1 shows a data table of historical sharing events of a user about file types (denoted by d) in 5 months in 2019, wherein the first bar indicates that the user shares files to friends at 14: 46/23 seconds in 5 months and 3 days in 2019.
TABLE 1
And assuming that the target type is a file, counting historical sharing events of the file type to form a data table, then obtaining the time distribution characteristics of each sharing position according to the data table, and then calculating user preference data. For example, the sharing location may include a friend, a network disk, and a work group, the number of times that a file is shared to the friend, the network disk, and the work group in the last month or all the time may be counted, and the location with the largest number of times is used as the user preference data. The temporal distribution characteristics may include a variety of dimensions or forms of statistical data, which the present disclosure is not limited to.
In an alternative embodiment, the user preference data corresponding to the target type may include: sharing information of the target type to the preference degrees of all sharing positions; based on this, step S630 can be specifically realized by the following steps:
arranging history sharing events of a target type according to a time sequence, merging the history sharing events which are continuous and have the same sharing position into a history sharing event set, and respectively forming the history sharing event set of a single event by the history sharing events which are not merged;
calculating the weight of each history sharing event set according to the sequence of each history sharing event set and the number of events in each history sharing event set;
and combining the weights of the historical sharing event sets with the same sharing position, and calculating the preference degree of the information of the target type shared to each sharing position.
Taking the data in table 1 as an example to illustrate, arranging the history sharing events in table 1 into an array according to the time sequence from near to far, recording the sharing position of each history sharing event, and taking a as a friend, B as a network disk, and C as a work group, the array is:
(C,C,B,A,A,C,C,C,C,B,B,A)；
then, combining the continuous history sharing events with the same sharing position into a history sharing event set, the above array can be expressed as:
(2C,B,2A,4C,2B,A)；
wherein 2C represents a history sharing event set formed by merging two consecutive history sharing events with sharing positions C, and B represents a single event set with sharing positions B. As can be seen, each entry in the array represents a set of historical sharing events.
Then, the weight of each item is calculated respectively, and the calculation principle is as follows: the weight of each term is inversely related to the order of the term and positively related to the number of events. There are many specific calculation methods, which the present disclosure does not limit, and an example is provided below:
the calculation of the power function can be used: w ═ seqa·quanb(ii) a Wherein W is weight, seq is order, quan is number of events, a and b are preset exponential parameters, a<0，b>1, can be assigned according to experience and actual requirements. For example, when a is-0.5 and b is 1.5, the above-mentioned array is calculated, and:
W(2C)＝1-0.5·21.5＝2.83；
W(B)＝2-0.5·11.5＝0.71；
W(2A)＝3-0.5·21.5＝1.63；
W(4C)＝4-0.5·41.5＝4；
W(2B)＝5-0.5·21.5＝1.26；
W(A)＝6-0.5·11.5＝0.41；
and then merging the items sharing the same position, merging the items 2C and 4C with the weight of 6.83, merging the items B and 2B with the weight of 1.97, merging the items 2A and A with the weight of 2.04. The weights were normalized to obtain A, B, C with preference degrees (highest 1) of 0.19, 0.18, and 0.63, respectively. Of course, the normalization algorithm used is different, and the final result will also be different, which is not limited in this disclosure.
Based on the preference degrees of the sharing positions calculated in the above manners, in step S520, the sharing position with the highest preference degree or the sharing position with the preference degree higher than the preset preference threshold may be determined as the target sharing position. The preset preference threshold is used as a judgment standard for judging whether the user prefers or not, and can be set according to experience or actual requirements.
In an optional implementation manner, the server may set a logic unit # set _ contentID _ userID _ type _ place, which is responsible for parsing the current sharing event according to the user preference data to obtain the target sharing location. The terminal may be provided with a logic unit # show _ contentID _ place, which is responsible for communicating with the logic unit of the server, and acquiring and displaying the target sharing location.
It should be added that, in an alternative embodiment, when the user preference data is calculated, if a preset condition is met, the user preference data may be determined to be null. The preset condition may include any one or more of the following combinations:
the number of the historical sharing events corresponding to the target type is less than a first threshold, which indicates that the number of the historical sharing events corresponding to the target type is too small to determine the user preference;
the number of the history sharing events corresponding to the target type in the recent preset period is less than a second threshold, which indicates that the number of the history sharing events corresponding to the recent target type is too small to determine the preference of the user in the recent period, and the recent preset period may be the recent week, the recent month, and the like;
the sharing preference degrees of all the sharing positions are lower than a third threshold, which indicates that the user has no obvious preference for all the sharing positions and is difficult to determine the preference of the user, and the third threshold can be 0.5, 0.7 and the like.
The first threshold, the second threshold and the third threshold are not related and can be set according to experience or actual requirements, which is not limited by the present disclosure. If the user preference data of the target type is null, step S530 is executed when the user shares the information of the target type next time, that is, the target sharing position is determined by the sharing prediction model.
Second aspect, sharing the predictive model:
the shared prediction model may be obtained by:
acquiring historical sharing events of different users, and extracting a plurality of groups of sample data from the historical sharing events, wherein each group of sample data comprises user information and any N +1 continuous historical sharing events which correspond to the user information and have the same type of shared information, and N is a positive integer not less than 2;
and training a machine learning model by taking the user information and the sharing position of the former N events in the N +1 historical sharing events as training data and taking the sharing position of the last event in the N +1 historical sharing events as marking data to obtain a sharing prediction model.
The server has the advantage of big data, can collect historical sharing events of different users, and arranges the historical sharing events into sample data taking a group as a unit according to the mode. The N +1 continuous history sharing events with the same information sharing type mean that the N +1 history sharing events are of the same type, and no other history sharing events of the same type exist between any two adjacent history sharing events. The user information may include information of various attributes such as user age, sex, registration time, login frequency, member level, etc., or the user may be portrayed, and the user may be classified into categories based on the user image, so that the user information may include the category to which the user belongs. For each shared position, the above-mentioned manner of encoding the shared position in table 1 may be referred to for numerical conversion. The user information and the shared positions of the first N events may be converted into feature vectors, which are used as training data, and the value converted from the shared position of the last event is used as labeling data, so as to train machine learning models, such as a neural network model, a support vector machine model, a logistic regression model, a random forest model, and the like, and after the training is completed, a shared prediction model may be obtained. The value of N can be set according to experience and actual requirements, and generally the larger N, the more accurate the prediction result, and of course, the higher the requirements on data.
Training based on the mode to obtain a sharing prediction model, taking the previous N times of sharing events of the user as a part of input data, and combining user information to learn and mine characteristics, so that higher prediction accuracy can be realized; moreover, multiple groups of sample data can be mined from the same type of history sharing events of the same user, so that abundant data support is provided for model training, and the implementation of a training process is facilitated.
In summary, in the exemplary embodiment, after obtaining the information to be shared and the user identifier from the terminal, the server searches the user preference data according to the user identifier and the type of the information to be shared, if the user preference data is found, determines the target sharing position of the information to be shared according to the user preference data, if the user preference data is not found, determines the target sharing position through the sharing prediction model, and finally returns the target sharing position to the terminal to be preferentially displayed on the terminal. On one hand, whether the target sharing position is based on user preference data or a sharing prediction model, the determined target sharing position predicts the position of the user sharing the information to be shared, the position is preferentially displayed on a sharing page on the terminal, the user can conveniently share the information, the target position can be selected without sliding the page or clicking for multiple times, the operation flow is simplified, and the user experience is improved; on the other hand, by adopting a double way of user preference data and sharing a prediction model, the flexibility of processing shared information can be improved, the sharing intention of the user can be reasonably predicted under the condition of lacking user historical shared data, and the robustness is higher.
Exemplary embodiments of the present disclosure also provide a shared information processing apparatus, which may be configured in the server 405 described above. As shown in fig. 7, the shared information processing apparatus 700 may include: an obtaining module 710, configured to obtain information to be shared from a terminal, send a user identifier of the information to be shared, and identify a type of the information to be shared as a target type; the first processing module 720 is configured to, when user preference data corresponding to a target type under the user identifier is found, determine a target sharing position of the information to be shared according to the user preference data; the second processing module 730 is configured to predict a target sharing position of the information to be shared according to the sharing prediction model when the user preference data corresponding to the target type under the user identifier is not found; the sending module 740 is configured to send the target sharing location to the terminal, so that the target sharing location is preferentially displayed on the terminal.
In an optional implementation manner, the shared information processing apparatus 700 may further include: and a user preference analysis module (not shown in the figure) for collecting the history sharing events corresponding to the user identifier, counting the history sharing events of each type according to the types of the information shared by the history sharing events, and calculating user preference data corresponding to the target type under the user identifier based on the time distribution characteristics of the history sharing events of the target type.
In an alternative embodiment, the user preference data corresponding to the target type may include: sharing information of the target type to the preference degrees of all sharing positions; the user preference analysis module (not shown in the figure) may also be configured to arrange the history sharing events of the target type according to a time sequence, merge the history sharing events that are consecutive and have the same sharing position into a history sharing event set, form a history sharing event set of a single event respectively for the history sharing events that are not merged, calculate a weight of each history sharing event set according to an order of each history sharing event set and the number of events therein, merge weights of the history sharing event sets having the same sharing position, and calculate a preference degree of information of the target type shared to each sharing position.
In an optional embodiment, the user preference analysis module (not shown in the figure) may be further configured to determine that the user preference data is a null value if a preset condition is met when the user preference data is calculated; wherein the preset condition comprises any one or more of the following combinations: the number of history sharing events corresponding to the target type is less than a first threshold value; the number of the history sharing events corresponding to the target type in the latest preset period is less than a second threshold value; the sharing preference of each sharing position is lower than a third threshold value.
In an alternative embodiment, the target sharing location may include: the sharing position with the highest preference degree or the sharing position with the preference degree higher than a preset preference threshold value.
In an optional implementation manner, the shared information processing apparatus 700 may further include: the model training module (not shown in the figure) is used for acquiring historical sharing events of different users and extracting a plurality of groups of sample data from the historical sharing events, wherein each group of sample data comprises user information and any N +1 continuous historical sharing events corresponding to the user information and having the same sharing information type, N is a positive integer not less than 2, the sharing positions of the former N events in the N +1 historical sharing events are used as training data, the sharing position of the last event in the N +1 historical sharing events is used as marking data, and a machine learning model is trained to obtain a sharing prediction model.
In an optional implementation manner, the information to be shared may be information shared in the target application, and the target sharing location may be a sharing location in the target application.
The specific details of each module in the above apparatus have been described in detail in the method section, and details of an undisclosed scheme may refer to the method section, and thus are not described again.
It should be noted that although in the above detailed description several modules of the device for action execution are mentioned, this division is not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
Exemplary embodiments of the present disclosure also provide an electronic device. FIG. 8 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present disclosure. It should be noted that the computer system 800 of the electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for system operation are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by a Central Processing Unit (CPU)801, performs various functions defined in the methods and apparatuses of the present application. In some embodiments, computer system 800 may also include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist separately without being assembled into the electronic device. The computer-readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the embodiments below. For example, the electronic device may implement the various steps shown in fig. 5 or fig. 6, and so on.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (10)
1. A method for processing shared information, comprising:
the method comprises the steps that information to be shared and a user identification for sending the information to be shared are obtained from a terminal, and the type of the information to be shared is identified and used as a target type;
when the user preference data corresponding to the target type under the user identification is found, determining a target sharing position of the information to be shared according to the user preference data;
when the user preference data corresponding to the target type under the user identification is not found, determining a target sharing position of the information to be shared through a sharing prediction model;
and sending the target sharing position to the terminal so as to preferentially display the target sharing position on the terminal.
2. The method of claim 1, wherein the user preference data corresponding to the target type under the user identifier is obtained by:
collecting historical sharing events corresponding to the user identification;
counting the historical sharing events of each type according to the types of the information shared by the historical sharing events;
and calculating user preference data corresponding to the target type under the user identification based on the time distribution characteristics of the historical sharing events of the target type.
3. The method of claim 2, wherein the user preference data corresponding to the target type comprises: the information of the target type is shared to the preference degree of each sharing position;
calculating user preference data corresponding to the target type under the user identification based on the time distribution characteristics of the historical sharing events of the target type, wherein the calculation comprises the following steps:
arranging the history sharing events of the target type according to the time sequence, merging the history sharing events which are continuous and have the same sharing position into a history sharing event set, and respectively forming the history sharing event set of a single event by the history sharing events which are not merged;
calculating the weight of each history sharing event set according to the sequence of each history sharing event set and the number of events in each history sharing event set;
and combining the weights of the historical sharing event sets with the same sharing position, and calculating the preference degree of the information of the target type shared to each sharing position.
4. The method according to claim 3, wherein when calculating the user preference data, if a preset condition is satisfied, determining that the user preference data is a null value;
wherein the preset condition comprises any one or combination of more of the following:
the number of the historical sharing events corresponding to the target type is less than a first threshold value;
the number of the history sharing events corresponding to the target type in the latest preset period is less than a second threshold value;
the sharing preference degrees of all the sharing positions are lower than a third threshold value.
5. The method of claim 3, wherein the target sharing location comprises: the sharing position with the highest preference degree or the sharing position with the preference degree higher than a preset preference threshold value.
6. The method of claim 1, wherein the shared predictive model is obtained by:
acquiring historical sharing events of different users, and extracting a plurality of groups of sample data from the historical sharing events, wherein each group of sample data comprises user information and any N +1 continuous historical sharing events which correspond to the user information and have the same type of shared information, and N is a positive integer not less than 2;
and training a machine learning model by taking the user information and the sharing position of the former N events in the N +1 historical sharing events as training data and taking the sharing position of the last event in the N +1 historical sharing events as marking data to obtain the sharing prediction model.
7. The method according to any one of claims 1 to 6, wherein the information to be shared is information shared in a target application program, and the target sharing location is a sharing location in the target application program.
8. A shared information processing apparatus, comprising:
the device comprises an acquisition module, a sharing module and a processing module, wherein the acquisition module is used for acquiring information to be shared from a terminal, sending a user identifier of the information to be shared, and identifying the type of the information to be shared as a target type;
the first processing module is used for determining a target sharing position of the information to be shared according to the user preference data when the user preference data corresponding to the target type under the user identification is found;
the second processing module is used for predicting a target sharing position of the information to be shared according to the sharing prediction model when the user preference data corresponding to the target type under the user identification is not found;
and the sending module is used for sending the target sharing position to the terminal so as to preferentially display the target sharing position on the terminal.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any of claims 1-7 via execution of the executable instructions.
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