CN116739545B - Method and device for improving intelligent message touch rate - Google Patents
Method and device for improving intelligent message touch rate Download PDFInfo
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Abstract
The invention provides a method and a device for improving the touch rate of intelligent messages, which relate to the technical field of data processing, and the method comprises the following steps: the method comprises the steps of collecting user basic information of a target user, calling a user portrait of the target user through big data, calling a historical interaction data setting stage window of the target user, historical interaction data and the user portrait based on the user basic information to extract target user interest points, generating information matching analysis of the window interest points of the target user and intelligent information, determining content matching degree and time correlation nodes, performing information value evaluation on the information to generate basic value grade, inputting an intelligent touch model, and outputting touch time nodes to send the intelligent information to the target user.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for improving the touch rate of intelligent messages.
Background
With the development of internet technology, particularly the development of the field of intelligent messaging, the form of messaging has changed greatly. The former message forms are mostly based on offline paper messages and online television messages. Today, internet intelligent messages have largely replaced traditional messages, and the prosperity of internet intelligent messages makes the message touch to be a problem to be solved urgently, but the lack of control over the intelligent message touch process in the prior art has the technical problem of causing low intelligent message touch rate.
Disclosure of Invention
The application provides a method and a device for improving the intelligent message touch rate, which are used for solving the technical problem that the intelligent message touch rate is low due to the lack of control over the intelligent message touch process in the prior art.
In view of the above, the present application provides a method and apparatus for improving the touch rate of intelligent messages.
In a first aspect, the present application provides a method for improving the touch rate of an intelligent message, where the method includes: collecting user basic information of a target user, and calling a user portrait of the target user through big data; invoking historical interaction data of the target user based on the user basic information, and setting a stage window based on the historical interaction data; extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user; reading message information of an intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time associated nodes; evaluating the information value of the message information to generate a basic value grade; inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node; and delivering the intelligent message to the target user through the touch time node.
In a second aspect, the present application provides a device for improving the touch rate of an intelligent message, where the device includes: the calling module is used for collecting user basic information of the target user and calling user portraits of the target user through big data; the window setting module is used for calling the historical interaction data of the target user based on the user basic information and setting a stage window based on the historical interaction data; the extraction module is used for extracting the target user interest points based on the stage window, the historical interaction data and the user portrait and generating window interest points of the target user; the matching analysis module is used for reading message information of the intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time association nodes; the evaluation module is used for evaluating the information value of the message information and generating a basic value grade; the input module is used for inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model and outputting touch time nodes; and the delivering module is used for delivering the intelligent message to the target user through the touch time node.
In a third aspect, a computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program: collecting user basic information of a target user, and calling a user portrait of the target user through big data; invoking historical interaction data of the target user based on the user basic information, and setting a stage window based on the historical interaction data; extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user; reading message information of an intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time associated nodes; evaluating the information value of the message information to generate a basic value grade; inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node; and delivering the intelligent message to the target user through the touch time node.
In a fourth aspect, a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of: collecting user basic information of a target user, and calling a user portrait of the target user through big data; invoking historical interaction data of the target user based on the user basic information, and setting a stage window based on the historical interaction data; extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user; reading message information of an intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time associated nodes; evaluating the information value of the message information to generate a basic value grade; inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node; and delivering the intelligent message to the target user through the touch time node.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides a method and a device for improving the intelligent message touch rate, which relate to the technical field of data processing, solve the technical problem that the intelligent message touch rate is low due to the lack of control on the intelligent message touch process in the prior art, realize rationalizing and accurately controlling the intelligent message touch process, and further improve the intelligent message touch rate.
Drawings
Fig. 1 is a schematic flow chart of a method for improving the touch rate of an intelligent message;
fig. 2 is a schematic flow chart of node update of a touch time node in a method for improving the touch rate of an intelligent message;
fig. 3 is a schematic flow chart of delivering to a target user in a method for improving the touch rate of an intelligent message;
fig. 4 is a schematic flow chart of updating the touch feature of a target user in a method for improving the touch rate of an intelligent message;
fig. 5 is a schematic structural diagram of a device for improving the touch rate of an intelligent message;
FIG. 6 is an internal block diagram of a computer device in one embodiment;
reference numerals illustrate: the system comprises a calling module 1, a window setting module 2, an extracting module 3, a matching analysis module 4, an evaluation module 5, an input module 6 and a delivery module 7.
Detailed Description
The application provides a method and a device for improving the touch rate of intelligent messages, which are used for solving the technical problem that the touch rate of the intelligent messages is low due to the lack of control over the touch process of the intelligent messages in the prior art.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for improving the touch rate of an intelligent message, where the method includes:
step S100: collecting user basic information of a target user, and calling a user portrait of the target user through big data;
specifically, the method for improving the touch rate of the intelligent message is applied to a device for improving the touch rate of the intelligent message, in order to improve the touch rate of the intelligent message, user basic information of a target user is firstly required to be collected, the user basic information can include behavior preference information of the target user, sex information of the target user, age information of the target user and the like, and meanwhile the collected user basic information is input into big data to carry out sketching of a user figure of the target user, namely, a labeled user model is abstracted according to information such as user attributes, user preferences, living habits and user behaviors, so that the user figure of the target user is obtained, and the intelligent message touch rate is improved for later implementation as an important reference basis.
Step S200: invoking historical interaction data of the target user based on the user basic information, and setting a stage window based on the historical interaction data;
specifically, the collected user basic information is used as call standard data, historical interaction data corresponding to the user basic information is called in historical interaction data of a target user, the historical interaction data of the target user refers to data when the target user interacts with the system before the current time under the condition that the target user authorizes the system, the interaction data refers to data transfer between a target user end and a system end, the historical interaction data can comprise interaction data such as interaction times of the target user, interaction time of the target user, interaction preference of the target user and the like, further, a stage window is set according to the historical interaction data, different interaction data of the target user are divided in stages based on the historical interaction data of the target user and the system, and the range in all divided stages is recorded as a stage window so as to realize improvement of the intelligent message accessibility.
Step S300: extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user;
specifically, extracting interest points of a target user on the basis of a user portrait called by big data, historical interaction data of the target user called based on user basic information and a stage window set based on the historical interaction data, namely firstly extracting attribute tags, user preference tags, life habit tags, user behavior tags and other portrait tags of the user contained in the target user portrait, then extracting interaction data such as interaction times of the target user, interaction time of the target user and interaction preference of the target user in the historical interaction data, finally extracting the stage window containing the most interaction data, matching the extracted portrait tags and the interaction data with the stage window, taking the interaction data contained in the stage window with the highest matching degree as interest points of the target user, recording the stage window corresponding to the interest points of the extracted target user as window interest points of the target user, outputting the window interest points of the target user on the basis, and further realizing the promotion of the intelligent message touch rate.
Step S400: reading message information of an intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time associated nodes;
specifically, reading information of an intelligent message generated in the system, wherein the intelligent message can comprise an intelligent message capturing rate, an intelligent message pushing rate, an intelligent message clicking rate, an intelligent message conversion rate and an intelligent message reaching rate, the read information in the intelligent message refers to that the language, the characters, the image or the data are collectively called as the message, the message is given to new knowledge of a target user, further, the read information is used as matching standard data to carry out matching analysis with the determined window interest point, whether the information contained in the current intelligent message is in the window interest point or not is judged, if the information is in the window interest point, the information is considered to be matched with the window interest point at the moment, meanwhile, the content matching degree of the information and a time correlation node of the information and the window interest point are determined, the content matching degree is in direct proportion, namely, the higher the content matching degree is the higher the matching degree of the information and the window interest point is, and the time correlation node refers to that the pushing time of the information and the target user is more similar to the window interest point is achieved, and the similarity of the information is improved if the information is similar to the window interest point.
Step S500: evaluating the information value of the message information to generate a basic value grade;
specifically, in order to ensure that the intelligent message is pushed to the target user, the degree of relevance of the information content in the intelligent message to the target user is determined, the information value of the message information is firstly evaluated, namely the information of which the degree of relevance is greater than 80%, the information value is highest, the degree of relevance is equal to or less than 50% and is equal to 80% based on the degree of relevance between the user portrait of the target user and the information of the message information, the higher the degree of relevance is, the current information is regarded as the value of the target user when the degree of relevance is high, the current information is evaluated according to the information content of the material, the energy and the attribute contained in the message information, the information value evaluation can be classified into a first-level value, a second-level value and a third-level value, wherein the first-level value is the information of which the degree of relevance of the message information to the target user is greater than 80%, the second-level value is the information of which the degree of relevance is equal to or less than 80%, the information of which the degree of relevance is equal to or less than 50% is the information of the target user, the first-level value is the information of which the degree of relevance is equal to 50% and the information of the target user, the information is the information of which is the information of the information which is the minimum, and the information of which is the information of the grade after the information is integrated with the information is the information of the target grade.
Step S600: inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node;
further, as shown in fig. 2, step S600 of the present application further includes:
step S610: setting an active detection window of the target user;
step S620: performing activity detection on the electronic equipment on the target user through the activity detection window;
step S630: aggregating the detection results, and setting a dedicated active window and a coverage active window;
step S640: and updating the access time node based on the exclusive active window and the coverage active window.
Further, step S630 of the present application includes:
step S631: setting an active association factor;
step S632: determining a tolerance authentication frequency based on the duration of the activity detection window and the activity association factor;
step S633: actively authenticating the aggregation detection result through the tolerance authentication frequency;
step S634: and taking the time interval of passing the active authentication as the exclusive active window, and taking the time interval of failing the active authentication as the coverage active window.
Specifically, in order to determine a touch time node when the message information is pushed to the target user, the touch time node is input into the intelligent touch model according to a basic value level obtained according to the information value evaluation, and a content matching degree and a time correlation node determined by matching analysis.
The intelligent touch model is a neural network model in machine learning to continuously perform self iterative optimization, the neural network model refers to a multi-layer feedforward neural network model trained according to an error reverse propagation algorithm, the intelligent touch model is further constructed, input data of the intelligent touch model comprise basic value grades, content matching degree and time correlation nodes, the intelligent touch model is obtained through training of a training data set and a supervision data set, each group of training data in the training data set comprises input data, and the supervision data set is supervision data corresponding to the training data set one by one.
Further, the intelligent touch model construction process comprises the following steps: inputting each group of training data in the training data set into the intelligent touch model, performing output supervision adjustment of the intelligent touch model through supervision data corresponding to the group of training data, finishing the current group of training when the output result of the intelligent touch model is consistent with the supervision data, finishing all training data in the training data set, and finishing the intelligent touch model training.
In order to ensure the convergence and accuracy of the intelligent touch model, the convergence process may be that when the output data in the intelligent touch model is converged to one point, the convergence is performed when the output data approaches to a certain value, the accuracy may be tested by the intelligent touch model through a test data set, for example, the test accuracy may be set to 80%, and when the test accuracy of the test data set meets 80%, the intelligent touch model is constructed.
And inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node.
The current touch time node obtained through the intelligent touch model is an ideal touch time node, so that the output touch time node needs to be updated through actual data, namely, the active detection window of a target user needs to be set firstly, the active detection window is used for recording a time period of continuous data interaction operation of the target user, further, the active detection of the target user is carried out through the active detection window, the electronic device of the user can be a PC end, a mobile end and the like, statistics of the information quantity of the information is carried out in the electronic device, if the counted information quantity in the electronic device of the target user in the active detection window is continuously increased, the electronic device is regarded as an active state, and if the counted information quantity in the electronic device of the target user in the active detection window is continuously decreased, the electronic device is regarded as an inactive state, and the detection result of the active detection of the target user in the active detection window is aggregated, namely, a process of collecting the detection result and representing the target user in a summarizing form is carried out, wherein the detection result is a process of collecting the detection result, the detection result is carried out in a search report-based format, the collection and the specific service or the specific active window is set and the specific window or the specific window is set and the specific window is covered, and the specific process is set and the specific window is manually covered or is set and covered on the specific window and the specific window is set and the active window is set: firstly, setting an activity association factor, wherein the activity degree of the activity association factor is higher, the activity degree which has higher than 80% of the target user association is called an effective factor which has influence on the activity degree of the user, wherein the effective factor which has larger influence on the activity degree of the user is called the activity association factor, further, the activity detection duration of an activity detection window is extracted, the number of the activity association factors contained in the extracted duration is determined, the occurrence frequency of the maximum activity association factor number is recorded as a tolerance authentication frequency, the tolerance range of the activity detection of the electronic equipment is detected, the activity authentication of the aggregation detection result is carried out through the tolerance authentication frequency, if the activity detection is within the tolerance authentication frequency, the activity authentication of the aggregation detection result is regarded as passing, namely the aggregation detection result is active, if the activity authentication of the aggregation detection result is not passing, namely the aggregation detection result is regarded as not active.
Further, the time interval included in the aggregation detection result that the active authentication passes is taken as a dedicated active window, and the time interval included in the aggregation detection result that the active authentication fails is taken as a coverage active window, so that the intelligent message reaching time node is updated according to the determined dedicated active window or coverage active window according to the time interval included in the aggregation detection result, and the accuracy of improving the intelligent message reaching rate in the later stage is improved.
Step S700: and delivering the intelligent message to the target user through the touch time node.
Further, as shown in fig. 3, step S700 of the present application further includes:
step S710: reading the electronic equipment decoration information of the target user;
step S720: pushing a database template based on the electronic equipment decoration information matching notification to obtain a first template matching set;
step S730: performing template matching of the notification pushing database template based on the message information to obtain a second template matching set;
step S740: and carrying out template screening on the basis of the first template matching set and the second template matching set, and processing the intelligent message on the basis of a template screening result and then delivering the intelligent message to the target user.
Specifically, based on the touch time node output by the intelligent touch model, the read intelligent message is sent to the target user, namely, the electronic equipment decoration information of the target user is firstly read, the electronic equipment decoration information is decoration information such as a desktop, a theme and the like which are set by the target user in the electronic equipment, further, the electronic equipment decoration information is matched with a notification pushing database template, the intelligent message to be pushed is required to be consistent with the theme of the electronic equipment decoration information before being pushed, all the intelligent messages which are consistent with the electronic equipment decoration information after being matched are recorded as a first template matching set, the notification pushing database template is formed by carrying out templatization statistics on the style of notification pushing data in big data and the duty ratio of image characters, further, the template matching of the template of the notification pushing database is performed through the message information, namely, the proportion of the intelligent message to be pushed to the image-text proportion in the message information, which is required to be matched with the use preference of the target user, is selected in the notification pushing database template, so that all templates matched with the matching are marked as a second template matching set, finally, template screening is performed in the first template matching set and the second template matching set according to the similarity between the template matching set and the image of the target user, the target with the similarity higher than 80% is used as a template screening result, and the pattern of the intelligent message and the image-text proportion are processed according to the screened template screening result, and then the processed intelligent message is delivered to the target user, so that the technical effect of providing important basis for improving the touch rate of the intelligent message is achieved in the later period is achieved.
Further, step S740 of the present application includes:
step S741: setting a temporary touch window for activity monitoring;
step S742: when the electronic equipment active duration of the target user is monitored to meet the temporary touch window, and the active time node is not in the exclusive active window and the coverage active window, generating a temporary touch instruction;
step S743: and changing the touch time node through the temporary touch instruction, and sending the intelligent message to the target user.
Further, step S743 of the present application includes:
step S7431: matching a push frequency constraint interval based on the user basic information;
step S7432: invoking a main body APP of the intelligent message to construct a push history record;
step S7433: performing time correction of the touch time node based on the frequency constraint interval and the push history;
step S7434: and completing the touch of the intelligent message according to the time correction result.
Specifically, in order to enable the intelligent message to be finally sent to the target user, setting a temporary touch window for activity monitoring is needed, namely, when the target user touches the intelligent message, the intelligent message which is not in an activity detection window of the target user, and a message window for continuous data interaction operation of the target user in a current time period is recorded as a temporary touch window for activity monitoring, when the activity duration of the electronic equipment monitoring the target user meets the temporary touch window, and the activity time node is not in a dedicated activity window or a coverage activity window, a temporary touch instruction is generated according to the activity duration of the electronic equipment of the target user and the activity time node at the moment, further, the touch time node of the intelligent message is changed through the activity in the activity time node in the temporary touch instruction, namely, the touch time is prolonged according to the increase amplitude of the activity, and the intelligent message is sent to the target user according to the touch time node after the change.
When pushing the intelligent message according to the arrival time node, there may be delay in pushing the intelligent message due to inaccuracy of the arrival time, so in order to avoid such a situation, first, it is necessary to use the user basic information as constraint data, and define a pushing frequency constraint interval of the intelligent message, where the pushing frequency constraint interval refers to limiting the frequency of pushing the intelligent message to the target user according to the behavior preference of the user in the user basic information, further, call a main body APP corresponding to the pushed intelligent message, collect and extract the message pushed by the target user through the main body APP, record the collected and extracted pushing record as a pushing history record, and finally, time modify the arrival time node of the intelligent message in the above defined frequency constraint interval and the extracted pushing history record, that is, determine whether the pushed intelligent message in the arrival time node is in the frequency constraint interval and accords with the pushing history record, and if not in the frequency constraint interval or/and does not accord with the pushing history record, make a time modification on the intelligent message contained in the arrival time node, and make a final time modification on the arrival time of the intelligent message included in the arrival time node, and ensure that the arrival time of the intelligent message reaches the target user better.
Further, as shown in fig. 4, step S800 of the present application further includes:
step S810: receiving user touch feedback of the target user;
step S820: matching analysis is carried out on the user touch feedback and the window interest points;
step S830: if the matching value meets a preset threshold, the touch feature of the target user is updated directly through the touch feedback;
step S840: and if the matching value cannot meet the preset threshold value, recording the user touch feedback, and updating the touch characteristic of the target user according to the accumulated recording result.
Specifically, in order to accurately perform the touch feeling when the intelligent message is touched to the target user, so as to ensure the improvement of the touch rate of the intelligent message in the later period, user touch feedback information of the target user is required to be received, the user touch feedback information of the target user refers to the satisfaction degree of the intelligent message touched by the user, the feedback information can comprise satisfaction degree of more than 80 percent and satisfaction degree of less than or equal to 40 percent, the satisfaction degree is not satisfied generally, the satisfaction degree is less than 40 percent, further, user touch feedback and window interest points are subjected to matching analysis, the user touch feedback satisfaction degree is more than or equal to 40 percent, the corresponding interest points are searched for in the window interest points for matching, further, whether the matching value of the user touch feedback and the window interest points meets a preset threshold value is judged, if the matching value meets the preset threshold value, the intelligent message is directly updated by the touch feedback to the target user through the touch feedback, the touch feedback characteristics of the intelligent message is performed, the user touch feedback is performed by pushing the contact information of the window, if the user touch feedback has the preset value and the preset value is not satisfied, the current interest point is not satisfied, the user is pushed by the contact information is recorded, and the current interest point is not satisfied, the user is pushed by the user, and the user is about the contact information is about the current, and the user is about the contact information is about the user to be satisfied by the characteristics, and improving the intelligent message accessibility rate according to the result of comparing the preset threshold value based on the matching value.
In summary, the method for improving the intelligent message touch rate provided by the embodiment of the application at least includes the following technical effects, so that reasonable and accurate control over the intelligent message touch process is realized, and the intelligent message touch rate is further improved.
Example two
Based on the same inventive concept as the method for improving the touch rate of the intelligent message in the foregoing embodiment, as shown in fig. 5, the present application provides an apparatus for improving the touch rate of the intelligent message, where the apparatus includes:
the calling module 1 is used for collecting user basic information of the target user and calling user portraits of the target user through big data;
a window setting module 2, wherein the window setting module 2 is used for calling the historical interaction data of the target user based on the user basic information and setting a stage window based on the historical interaction data;
the extraction module 3 is used for extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user;
the matching analysis module 4 is used for reading message information of the intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time association nodes;
the evaluation module 5 is used for evaluating the information value of the message information and generating a basic value grade;
the input module 6 is used for inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model and outputting a touch time node;
and the delivering module 7 is used for delivering the intelligent message to the target user through the touch time node by the delivering module 7.
Further, the apparatus further comprises:
the detection window module is used for setting an active detection window of the target user;
the activity detection module is used for detecting the activity of the electronic equipment for the target user through the activity detection window;
the first window module is used for aggregating detection results and setting a dedicated active window and a coverage active window;
and the node updating module is used for updating the node of the reaching time based on the exclusive active window and the coverage active window.
Further, the apparatus further comprises:
the factor module is used for setting an active association factor;
a frequency determination module for determining a tolerance authentication frequency based on a duration of the activity detection window and the activity association factor;
the active authentication module is used for carrying out active authentication on the aggregation detection result through the tolerant authentication frequency;
and the second window module is used for taking the time interval of passing the active authentication as the exclusive active window and taking the time interval of not passing the active authentication as the coverage active window.
Further, the apparatus further comprises:
the information delivery module is used for reading the electronic equipment decoration information of the target user;
the first set acquisition module is used for pushing database templates based on the electronic equipment decoration information matching notification to obtain a first template matching set;
the template matching module is used for carrying out template matching of the notification pushing database template based on the message information to obtain a second template matching set;
and the template screening module is used for screening templates based on the first template matching set and the second template matching set, processing the intelligent message based on a template screening result and then sending the intelligent message to the target user.
Further, the apparatus further comprises:
the third window module is used for setting a temporary touch window for activity monitoring;
the instruction generation module is used for generating a temporary touch instruction when the electronic equipment active duration of the target user is monitored to meet the temporary touch window and the active time node is not in the exclusive active window and the coverage active window;
and the message sending module is used for sending the intelligent message to the target user by changing the touch time node through the temporary touch instruction.
Further, the apparatus further comprises:
the interval module is used for matching and pushing a frequency constraint interval based on the user basic information;
the recording module is used for calling the main body APP of the intelligent message and constructing a push history record;
the correction module is used for carrying out time correction on the touch time node based on the frequency constraint interval and the push history;
and the message touch module is used for finishing touch of the intelligent message according to the time correction result.
Further, the apparatus further comprises:
the touch feedback module is used for receiving user touch feedback of the target user;
the matching analysis module is used for carrying out matching analysis on the user touch feedback and the window interest points;
the first touch feature updating module is used for updating the touch feature of the target user directly through the touch feedback if the matching value meets a preset threshold value;
and the second touch feature updating module is used for recording touch feedback of the user if the matching value cannot meet the preset threshold value, and updating the touch feature of the target user according to the accumulated recording result.
The foregoing detailed description of a method for improving the touch rate of an intelligent message will be clear to those skilled in the art, and the device disclosed in this embodiment is relatively simple in description, and relevant places refer to the method part for description.
For a specific embodiment of an apparatus for enhancing the touch rate of an intelligent message, reference may be made to the above embodiment of a method for enhancing the touch rate of an intelligent message, which is not described herein. Each module in the above-mentioned device for improving the access rate of the intelligent message may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer readable storage medium is provided, the computer device may be a server, and the internal structure of the computer device may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing news data, time attenuation factors and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of improving the reach of an intelligent message.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A method for improving the touch rate of intelligent messages, the method comprising:
collecting user basic information of a target user, and calling a user portrait of the target user through big data;
invoking historical interaction data of the target user based on the user basic information, and setting a stage window based on the historical interaction data;
extracting the target user interest points based on the stage window, the historical interaction data and the user portrait, and generating window interest points of the target user;
reading message information of an intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time associated nodes;
evaluating the information value of the message information to generate a basic value grade;
inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model, and outputting a touch time node;
delivering the intelligent message to the target user through the touch time node;
wherein the method further comprises:
reading the electronic equipment decoration information of the target user;
pushing a database template based on the electronic equipment decoration information matching notification to obtain a first template matching set;
performing template matching of the notification pushing database template based on the message information to obtain a second template matching set;
template screening is carried out on the basis of the first template matching set and the second template matching set, and the intelligent message is sent to the target user after being processed on the basis of a template screening result;
wherein the method further comprises:
setting an active detection window of the target user;
performing activity detection on the electronic equipment on the target user through the activity detection window;
aggregating the detection results, and setting a dedicated active window and a coverage active window;
node updating is carried out on the touch time node based on the exclusive active window and the coverage active window;
setting an active association factor;
determining a tolerance authentication frequency based on the duration of the activity detection window and the activity association factor;
actively authenticating the aggregation detection result through the tolerance authentication frequency;
taking a time interval of passing the active authentication as the exclusive active window, and taking a time interval of failing the active authentication as the covering active window;
setting a temporary touch window for activity monitoring;
when the electronic equipment active duration of the target user is monitored to meet the temporary touch window, and the active time node is not in the exclusive active window and the coverage active window, generating a temporary touch instruction;
and changing the touch time node through the temporary touch instruction, and sending the intelligent message to the target user.
2. The method of claim 1, wherein the method further comprises:
matching a push frequency constraint interval based on the user basic information;
invoking a main body APP of the intelligent message to construct a push history record;
performing time correction of the touch time node based on the frequency constraint interval and the push history;
and completing the touch of the intelligent message according to the time correction result.
3. The method of claim 1, wherein the method further comprises:
receiving user touch feedback of the target user;
matching analysis is carried out on the user touch feedback and the window interest points;
if the matching value meets a preset threshold, the touch feature of the target user is updated directly through the touch feedback;
and if the matching value cannot meet the preset threshold value, recording the user touch feedback, and updating the touch characteristic of the target user according to the accumulated recording result.
4. An apparatus for enhancing the touch-up rate of intelligent messages, for performing the method of any of claims 1-3, the apparatus comprising:
the calling module is used for collecting user basic information of the target user and calling user portraits of the target user through big data;
the window setting module is used for calling the historical interaction data of the target user based on the user basic information and setting a stage window based on the historical interaction data;
the extraction module is used for extracting the target user interest points based on the stage window, the historical interaction data and the user portrait and generating window interest points of the target user;
the matching analysis module is used for reading message information of the intelligent message, carrying out matching analysis based on the message information and the window interest points, and determining content matching degree and time association nodes;
the evaluation module is used for evaluating the information value of the message information and generating a basic value grade;
the input module is used for inputting the basic value grade, the content matching degree and the time correlation node into an intelligent touch model and outputting touch time nodes;
the delivering module is used for delivering the intelligent message to the target user through the touch time node;
the information delivery module is used for reading the electronic equipment decoration information of the target user;
the first set acquisition module is used for pushing database templates based on the electronic equipment decoration information matching notification to obtain a first template matching set;
the template matching module is used for carrying out template matching of the notification pushing database template based on the message information to obtain a second template matching set;
and the template screening module is used for screening templates based on the first template matching set and the second template matching set, processing the intelligent message based on a template screening result and then sending the intelligent message to the target user.
5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 3 when the computer program is executed.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 3.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106412254A (en) * | 2016-09-13 | 2017-02-15 | 传线网络科技（上海）有限公司 | Method and apparatus for pushing information by using alarm clock in intelligent terminal |
| CN111859102A (en) * | 2020-02-17 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Prompt information determination method, system, medium and storage medium |
| CN112099830A (en) * | 2020-09-25 | 2020-12-18 | 努比亚技术有限公司 | System updating method, equipment and computer readable storage medium |
| CN113468411A (en) * | 2021-05-30 | 2021-10-01 | 咸宁方片互娱网络有限公司 | Method and device for improving reach rate of Android push message |
| CN114218482A (en) * | 2021-12-15 | 2022-03-22 | 上海幻电信息科技有限公司 | Information pushing method and device |
| CN114491582A (en) * | 2021-12-30 | 2022-05-13 | 深信服科技股份有限公司 | Authentication method and device and terminal equipment |
| CN115456684A (en) * | 2022-09-22 | 2022-12-09 | 平安科技（深圳）有限公司 | Information reach processing method and device, computer equipment and storage medium |
| CN116055553A (en) * | 2023-01-09 | 2023-05-02 | 中国第一汽车股份有限公司 | Method, system and device for processing vehicle push information |
-
2023
- 2023-08-16 CN CN202311030171.4A patent/CN116739545B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106412254A (en) * | 2016-09-13 | 2017-02-15 | 传线网络科技（上海）有限公司 | Method and apparatus for pushing information by using alarm clock in intelligent terminal |
| CN111859102A (en) * | 2020-02-17 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Prompt information determination method, system, medium and storage medium |
| CN112099830A (en) * | 2020-09-25 | 2020-12-18 | 努比亚技术有限公司 | System updating method, equipment and computer readable storage medium |
| CN113468411A (en) * | 2021-05-30 | 2021-10-01 | 咸宁方片互娱网络有限公司 | Method and device for improving reach rate of Android push message |
| CN114218482A (en) * | 2021-12-15 | 2022-03-22 | 上海幻电信息科技有限公司 | Information pushing method and device |
| CN114491582A (en) * | 2021-12-30 | 2022-05-13 | 深信服科技股份有限公司 | Authentication method and device and terminal equipment |
| CN115456684A (en) * | 2022-09-22 | 2022-12-09 | 平安科技（深圳）有限公司 | Information reach processing method and device, computer equipment and storage medium |
| CN116055553A (en) * | 2023-01-09 | 2023-05-02 | 中国第一汽车股份有限公司 | Method, system and device for processing vehicle push information |
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