CN115002200A - User portrait based message pushing method, device, equipment and storage medium - Google Patents
User portrait based message pushing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of big data, and discloses a user portrait based message pushing method, device, equipment and medium, wherein the method comprises the following steps: acquiring a message to be pushed, analyzing the class of the message to be pushed by using a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the class; acquiring basic information of a client and historical behavior data of the client, and performing statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client; modeling analysis is carried out according to the fact label to obtain a model label of the customer, and prediction analysis is carried out on the model label to obtain a prediction label of the customer; generating a user portrait of each client according to the fact label, the model label and the prediction label; determining one or more user tags for each customer based on the user representation of the customer; and matching the message tag with the user tag, and pushing the message to be pushed to a client according to a matching result. The invention can improve the accuracy of message pushing.
Description
Technical Field
The invention relates to the field of big data, in particular to a user portrait based message pushing method and device, electronic equipment and a readable storage medium.
Background
With the popularization and application of the enterprise WeChat, the enterprise can push messages for the client through the enterprise WeChat, the interaction between the enterprise and the client is improved, actual convenience is brought to the client through the messages, and the user experience can be improved. However, currently, when an enterprise pushes a message for a user, the enterprise does not perform differential treatment according to the situation of the user, and a large amount of messages are not pushed accurately, so that harassment is caused to the client, the client feels dislike, and the user experience is reduced.
Disclosure of Invention
The invention provides a user portrait-based message pushing method and device, electronic equipment and a computer-readable storage medium, and aims to improve the accuracy of message pushing.
In order to achieve the above object, the present invention provides a message pushing method based on a user portrait, including:
acquiring a message to be pushed, analyzing the category of the message to be pushed by using a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the category;
acquiring basic information of a client and historical behavior data of the client, and performing statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client;
modeling and analyzing according to the fact label to obtain a model label of the customer, and performing predictive analysis on the model label to obtain a predictive label of the customer;
generating a user representation of each said customer based on said fact tags, model tags and prediction tags;
determining one or more user tags for each of the customers based on the customer representation;
and matching the message label with the user label, and pushing the message to be pushed to a corresponding client according to a matching result.
Optionally, the generating a user representation of each customer according to the prediction tag, the model tag, and the fact tag includes:
calculating actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customers and all the customers to respectively obtain actual weights of the prediction labels, the model labels and the fact labels;
and generating a user portrait of each client according to the actual weight of the prediction label, the actual weight of the model label and the actual weight of the fact label.
Optionally, the calculating the actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customer and all the customers to obtain the actual weights of the prediction labels, the actual weights of the model labels and the actual weights of the fact labels respectively includes:
counting the label quantity of the prediction label, the model label and the fact label of the client to respectively obtain the number of the prediction label, the number of the model label and the number of the fact label;
calculating the proportions of the predicted label, the model label and the fact label in all labels of the client according to the number of the predicted labels, the number of the model labels and the number of the fact labels, and respectively obtaining a first weight of the predicted label, a first weight of the model label and a first weight of the fact label;
counting the number of the prediction labels, the model labels and the number of the fact labels of all the customers to respectively obtain the total number of the prediction labels, the total number of the model labels and the total number of the fact labels;
calculating the occupation ratios of the prediction labels, the model labels and the fact labels in all the labels of all the customers according to the total prediction label number, the total model label number and the total fact label number, and respectively obtaining a second weight of the prediction labels, a second weight of the model labels and a second weight of the fact labels;
and calculating the actual weight of the prediction label, the actual weight of the model label and the actual weight of the fact label by using a TF-IDF algorithm according to the first weight and the second weight of the prediction label, the first weight and the second weight of the model label and the first weight and the second weight of the fact label.
Optionally, said determining one or more user tags for said customer based on said user representation for each said customer comprises:
matching the user portrait with a pre-constructed candidate word template to obtain a candidate word set in the user portrait;
performing key information classification judgment on the candidate word set by using a pre-trained key information classification model to obtain key information in the user portrait;
and generating one or more user tags of the client according to the key information.
Optionally, the analyzing, by using a pre-constructed multi-classification model, the category to which the message to be pushed belongs includes:
acquiring a training data set consisting of a historical push message data set and a category to which the historical push message data set belongs, and constructing a first training set and a second training set based on the training data set;
obtaining a pre-constructed original multi-classification model, and performing model training on the original multi-classification model pair by using the first training set to obtain an initial multi-classification model;
performing model test on the initial multi-classification model by using the second training set, and adjusting the hyper-parameters in the initial multi-classification model according to a test result to obtain a trained multi-classification model;
and classifying the message to be pushed by using the trained multi-classification model to obtain the category of the message to be pushed.
Optionally, the analyzing, by using a pre-constructed multi-classification model, the category to which the message to be pushed belongs may further include:
performing word segmentation processing on the message to be pushed to obtain a word segmentation message set;
calculating the weight of each word in the word segmentation message set by using a TF-LDF algorithm to obtain the weight of the word;
extracting words in the word segmentation message set corresponding to the word weight larger than a preset threshold value to obtain keywords in the word segmentation message set;
performing part-of-speech tagging on the keyword by searching a preset dictionary to determine the meaning of the keyword;
and determining the category of the message to be pushed according to the word meaning of the keyword.
Optionally, after the message to be pushed is pushed to the corresponding client according to the matching result, the method further includes:
collecting feedback information of the client to the message to be pushed;
identifying and classifying the feedback information to obtain negative feedback information;
and adjusting the corresponding user portrait of the client according to the negative feedback information.
In order to solve the above problem, the present invention further provides a message pushing device based on user profile, the device comprising:
the message to be pushed and tag module is used for acquiring a message to be pushed, analyzing the category of the message to be pushed by utilizing a pre-constructed multi-classification model, and setting one or more message tags for the message to be pushed according to the category;
the user portrait generation module is used for acquiring basic information of a client, performing statistical analysis on the basic information and historical behavior data according to the basic information acquisition and the historical behavior data of the client to obtain a fact label of the client, performing modeling analysis according to the fact label to obtain a model label of the client, performing predictive analysis on the model label to obtain a predictive label of the client, and generating a user portrait of each client according to the fact label, the model label and the predictive label;
and the message pushing module is used for determining one or more user tags of the client according to the user portrait of each client, matching the message tags with the user tags, and pushing the message to be pushed to the corresponding client according to the matching result.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and the processor executes the computer program stored in the memory to realize the user portrait-based message pushing method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the user portrait based message pushing method described above.
The user portrait based message pushing method, the device, the electronic equipment and the readable storage medium provided by the embodiment of the invention generate the user portrait of the user by acquiring the basic information and the historical behavior data of the user, then carry out matching according to the user portrait and the message label of the message to be pushed, and carry out message pushing on the user according to the matching result, thereby well mastering the preference and the demand of the user, reducing unnecessary message pushing and improving the accuracy of message pushing.
Drawings
FIG. 1 is a flowchart illustrating a message pushing method based on a user profile according to an embodiment of the present invention;
FIG. 2 is a block diagram of a user portrait based message pushing apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an internal structure of an electronic device implementing a user portrait-based message pushing method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a message pushing method based on user portrait. The execution subject of the user portrait-based message pushing method includes, but is not limited to, at least one of the electronic devices, such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the user portrait based message pushing method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a user portrait based message pushing method according to an embodiment of the present invention is shown, in an embodiment of the present invention, the user portrait based message pushing method includes:
s1, obtaining the message to be pushed, analyzing the category of the message to be pushed by utilizing a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the category.
In the embodiment of the present invention, the message to be pushed may be any type of information, such as information of a certain product to be promoted of an enterprise, information of an event to be held, and the like.
In one embodiment of the present invention, the category to which the message to be pushed belongs may be an application scenario to which the message to be pushed belongs. For example, the categories may be beverage coupons, salon activities, cash benefits, etc., wherein the beverage coupons may be, for example, coffee coupons, the salon activities may include, for example, Yinian college invitations, and the cash benefits may be, for example, New Collar 60 Yuan benefits, etc.
In the embodiment of the present invention, the message to be pushed may be provided with different message tags according to different dimensions of the category to which the message to be pushed belongs. Wherein, the message label can comprise a consumption label, a content label, a date label, an age label, a high-latency label and the like. In one embodiment of the present invention, the multi-classification model may be a k-Nearest Neighbors (k-Nearest Neighbors) model, a Decision tree (Decision Trees) model, a Naive Bayes (Naive Bayes) model, a Random Forest (Random Forest) model, or a gradient boosting (gradient boosting) model. The multi-classification model may be used to classify messages to be pushed after being trained.
In the embodiment of the invention, the multi-classification model can be trained by a supervised learning method, namely, the multi-classification model is iteratively trained by using the historical push messages as a training set according to the categories of the historical push messages.
In detail, the analyzing the category to which the message to be pushed belongs by using the pre-constructed multi-classification model includes:
acquiring a training data set consisting of a historical push message data set and a category to which the historical push message data set belongs, and constructing a first training set and a second training set based on the training data set;
obtaining a pre-constructed original multi-classification model, and performing model training on the original multi-classification model pair by using the first training set to obtain an initial multi-classification model;
performing model test on the initial multi-classification model by using the second training set, and adjusting the hyper-parameters in the initial multi-classification model according to a test result to obtain a trained multi-classification model;
and classifying the message to be pushed by using the trained multi-classification model to obtain the category of the message to be pushed.
In other embodiments of the present invention, the multi-classification model may also be a semantic analysis model based on a natural language processing technology, and the category of the message to be pushed is obtained by performing semantic analysis on the message to be pushed.
In detail, the analyzing the category to which the message to be pushed belongs by using the pre-constructed multi-classification model may further include:
performing word segmentation processing on the message to be pushed to obtain a word segmentation message set;
calculating the weight of each word in the word segmentation message set by using a TF-LDF algorithm to obtain the weight of the word;
extracting words with the word weight larger than a preset threshold value from the word segmentation message set to obtain keywords;
performing word sense labeling on the keywords by searching a preset dictionary to determine the word senses of the keywords;
and determining the category of the message to be pushed according to the word meaning of the keyword.
Further, in the embodiment of the present invention, one or more message tags may be set for the message to be pushed according to a preset category-tag table.
Wherein, the category-label table records one or more message labels corresponding to each message category. For example, if the message to be pushed is a beverage coupon, the corresponding message tag includes that the consumption tag is a beverage, the content tag is a preferential activity, the date tag is fully applicable, the age tag is young and male, and the high-latency tag is fully applicable.
S2, obtaining basic information of the client and historical behavior data of the client, and carrying out statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client.
In the embodiment of the present invention, the basic information may be static attributes of the client, such as the age, sex, working condition, and consumption level of the client. The historical behavior data may be dynamic data of the user, including browsing records, searching records, purchasing records, and the like. The fact label comprises population attributes, income situations, purchasing frequency, product purchasing frequency and the like.
In an optional embodiment of the present invention, historical behavior data of the client may be crawled by using a crawler technology according to the basic information.
According to the embodiment of the invention, the fact label of the client is obtained by carrying out statistical analysis on the basic information and the historical behavior data, the interest and hobbies of the client are preliminarily determined, and a foundation is laid for subsequently generating the user portrait.
And S3, carrying out modeling analysis according to the fact label to obtain a model label of the customer, and carrying out prediction analysis on the model label to obtain a prediction label of the customer.
In the embodiment of the invention, the model label comprises user interest, user influence, product preference, channel preference and the like. The prediction label comprises recent demand, consumption capacity, loss probability and the like.
In the optional embodiment of the present invention, since the fact label does not represent all features of the client enough, modeling analysis needs to be performed according to the fact label, and further prediction analysis needs to be performed on a result of the modeling analysis, so as to predict interests and hobbies of the client more accurately, and further improve the accuracy of message pushing.
S4, generating user portrait of each client according to the fact label, the model label and the forecast label.
In an optional embodiment of the invention, the user portrait of the customer is generated by calculating the weight ratios of the fact label, the model label and the prediction label of the customer and according to the weight ratios, so that the dimensions of the customer, such as the interest and hobbies, are more vivid and concrete.
In detail, the generating a user representation of each of the customers based on the fact tags, model tags, and prediction tags includes:
calculating actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customers and all the customers to respectively obtain actual weights of the prediction labels, the model labels and the fact labels;
and generating a user portrait of each client according to the actual weight of the prediction label, the actual weight of the model label and the actual weight of the fact label.
In this embodiment of the present invention, the all clients may be all clients including the current client.
The invention determines the proportion of the predicted label, the model label and the fact label in the user portrait through the actual weight of the predicted label, the actual weight of the model label and the actual weight of the fact label, and generates the user portrait according to the proportion, so as to ensure that the user portrait can truly display the interest and hobbies, basic information and the like of the client, and improve the accuracy of the user portrait.
Further, the calculating actual weights of the prediction tags, the model tags and the fact tags according to the number of the prediction tags, the number of the model tags and the number of the fact tags in the current customer and all the customers to obtain an actual weight of the prediction tags, an actual weight of the model tags and an actual weight of the fact tags respectively includes:
counting the label quantity of the prediction label, the model label and the fact label of the client to respectively obtain the number of the prediction label, the number of the model label and the number of the fact label;
calculating the proportion of the predicted label, the model label and the fact label in all labels of the client according to the predicted label number, the model label number and the fact label number, and respectively obtaining a first weight of the predicted label, a first weight of the model label and a first weight of the fact label;
counting the number of the prediction labels, the model labels and the number of the fact labels of all the customers to respectively obtain the total number of the prediction labels, the total number of the model labels and the total number of the fact labels;
calculating the occupation ratios of the prediction labels, the model labels and the fact labels in all the labels of all the customers according to the total prediction label number, the total model label number and the total fact label number, and respectively obtaining a second weight of the prediction labels, a second weight of the model labels and a second weight of the fact labels;
and calculating the actual weights of the prediction label, the model label and the fact label by using a TF-IDF algorithm according to the first weight and the second weight of the prediction label, the first weight and the second weight of the model label and the first weight and the second weight of the fact label. S5, determining one or more user tags for the customer based on the user profile of each of the customers.
In the embodiment of the present invention, the user tag may be tag data corresponding to an enterprise message tag, and includes a consumption tag, a content tag, a date tag, an age tag, a high-latency tag, and the like.
In an optional embodiment of the present invention, the user representation includes attribute data of the client, a tag keyword may be determined by matching the attribute data with a preset candidate word template, and a user tag of the client may be generated according to the tag keyword.
In detail, said determining one or more user tags of said customer based on a user representation of each said customer comprises:
matching the user portrait with a pre-constructed candidate word template to obtain a candidate word set in the user portrait;
performing key information classification judgment on the candidate word set by using a pre-trained key information classification model to obtain key information in the user portrait;
and generating one or more user tags of the client according to the key information.
And S6, matching the message label with the user label, and pushing the message to be pushed to a corresponding client according to a matching result.
In the embodiment of the invention, the message label is matched with the user label, so that the messages pushed to the client are all messages required by the user, the situation that the user experience is poor due to message pushing is reduced, and the accuracy of enterprise and micro message pushing is improved.
In an alternative embodiment of the invention, the interest points of some customers change with time, for example, a customer needs to drink a lot of coffee due to working pressure in a period of time, for which a lot of information about coffee is searched, and when the customer has no working pressure, it is obviously not very accurate to push the coffee coupon to the customer. Therefore, when the information is pushed to the client according to the original user portrait, the feedback information of the client can be obtained in real time, and the user portrait is adjusted according to the feedback information, so that the enterprise micro information is pushed more accurately.
In detail, after the message to be pushed is pushed to the corresponding client according to the matching result, the method further includes:
collecting feedback information of the client to the message to be pushed;
identifying and classifying the feedback information to obtain negative feedback information;
and adjusting the corresponding user portrait of the client according to the negative feedback information.
According to the embodiment of the invention, the user portrait of the user is generated by acquiring the basic information and the historical behavior data of the user, the matching is carried out according to the user portrait and the message label of the message to be pushed, and the message pushing is carried out on the user according to the matching result, so that the preference and the demand of the user are well mastered, unnecessary message pushing is reduced, and the accuracy of the message pushing is improved.
FIG. 2 is a functional block diagram of a message pushing device based on a user profile according to the present invention.
The message pushing device 100 based on user portrait can be installed in an electronic device. According to the implemented functions, the user portrait based message pushing apparatus may include a message tag to be pushed module 101, a user portrait generating module 102 and a message pushing module 103, which may also be referred to as a unit, and refer to a series of computer program segments capable of being executed by a processor of an electronic device and performing a fixed function, and stored in a memory of the electronic device.
In the present embodiment, the functions of the respective modules/units are as follows:
the message to be pushed and tag module 101 is configured to obtain a message to be pushed, analyze a category to which the message to be pushed belongs by using a pre-constructed multi-classification model, and set one or more message tags for the message to be pushed according to the category.
In the embodiment of the present invention, the message to be pushed may be any type of information, such as information of a certain product to be promoted in an enterprise, information of an event to be held, and the like.
In one embodiment of the present invention, the category to which the message to be pushed belongs may be an application scenario to which the message to be pushed belongs. For example, the categories may be beverage coupons, salon activities, cash benefits, etc., wherein the beverage coupons may be, for example, coffee coupons, the salon activities may include, for example, Yinian college invitations, and the cash benefits may be, for example, New Collar 60 Yuan benefits, etc.
In the embodiment of the present invention, the message to be pushed may be provided with different message tags according to different dimensions of the category to which the message to be pushed belongs. Wherein, the message label can comprise a consumption label, a content label, a date label, an age label, a high-latency label and the like. In one embodiment of the present invention, the multi-classification model may be a k-Nearest Neighbors (k-Nearest Neighbors) model, a Decision tree (Decision Trees) model, a Naive Bayes (Naive Bayes) model, a Random Forest (Random Forest) model, or a gradient boosting (gradient boosting) model. The multi-classification model may be used to classify messages to be pushed after being trained.
In the embodiment of the invention, the multi-classification model can be trained by a supervised learning method, namely, the multi-classification model is iteratively trained by using the historical push messages as a training set according to the categories of the historical push messages.
In detail, the analyzing the category to which the message to be pushed belongs by using the pre-constructed multi-classification model includes:
acquiring a training data set consisting of a historical push message data set and a category to which the historical push message data set belongs, and constructing a first training set and a second training set based on the training data set;
obtaining a pre-constructed original multi-classification model, and performing model training on the original multi-classification model pair by using the first training set to obtain an initial multi-classification model;
performing model test on the initial multi-classification model by using the second training set, and adjusting the hyper-parameters in the initial multi-classification model according to a test result to obtain a trained multi-classification model;
and classifying the message to be pushed by using the trained multi-classification model to obtain the category of the message to be pushed.
In other embodiments of the present invention, the multi-classification model may also be a semantic analysis model based on a natural language processing technology, and the category of the message to be pushed is obtained by performing semantic analysis on the message to be pushed.
In detail, the analyzing the category to which the message to be pushed belongs by using the pre-constructed multi-classification model may further include:
performing word segmentation processing on the message to be pushed to obtain a word segmentation message set;
calculating the weight of each word in the word segmentation message set by using a TF-LDF algorithm to obtain the weight of the word;
extracting words with the word weight larger than a preset threshold value from the word segmentation message set to obtain keywords;
performing word sense labeling on the keywords by searching a preset dictionary to determine the word senses of the keywords;
and determining the category of the message to be pushed according to the word sense of the keyword.
Further, in the embodiment of the present invention, one or more message tags may be set for the message to be pushed according to a preset category-tag table.
Wherein, the category-label table records one or more message labels corresponding to each message category. For example, if the message to be pushed is a beverage coupon, the corresponding message tag includes that the consumption tag is a beverage, the content tag is a preferential activity, the date tag is fully applicable, the age tag is young and male, and the high-latency tag is fully applicable.
The user portrait generation module 102 is configured to obtain basic information of a client, perform statistical analysis on the basic information and historical behavior data according to the basic information and the historical behavior data of the client to obtain a fact tag of the client, perform modeling analysis according to the fact tag to obtain a model tag of the client, perform predictive analysis on the model tag to obtain a predictive tag of the client, and generate a user portrait of each client according to the fact tag, the model tag, and the predictive tag.
In the embodiment of the present invention, the basic information may be static attributes of the client, such as the age, sex, working condition, and consumption level of the client. The historical behavior data may be dynamic data of the user, including browsing records, searching records, purchasing records, and the like. The fact label comprises population attributes, income situations, purchasing frequency, product purchasing frequency and the like.
In an optional embodiment of the present invention, historical behavior data of the client may be crawled by using a crawler technology according to the basic information.
According to the embodiment of the invention, the fact label of the client is obtained by carrying out statistical analysis on the basic information and the historical behavior data, the interest and hobbies of the client are preliminarily determined, and a foundation is laid for subsequently generating the user portrait.
In the embodiment of the invention, the model label comprises user interest, user influence, product preference, channel preference and the like. The prediction label comprises recent demand, consumption capacity, loss probability and the like.
In the optional embodiment of the present invention, since the fact label does not represent all features of the client enough, modeling analysis needs to be performed according to the fact label, and further prediction analysis needs to be performed on a result of the modeling analysis, so as to predict interests and hobbies of the client more accurately, and further improve the accuracy of message pushing.
In an optional embodiment of the invention, the user portrait of the customer is generated by calculating the weight proportions of the fact label, the model label and the prediction label of the customer and according to the weight proportions, so that the dimensions of the customer, such as the interest and hobbies, are more concrete.
In detail, the generating a user representation of each of the customers from the fact tags, the model tags, and the prediction tags includes:
calculating actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customers and all the customers to respectively obtain actual weights of the prediction labels, the model labels and the fact labels;
and generating a user portrait of each client according to the actual weight of the prediction label, the actual weight of the model label and the actual weight of the fact label.
In this embodiment of the present invention, the all clients may be all clients including the current client.
The invention determines the proportion of the predicted label, the model label and the fact label in the user portrait through the actual weight of the predicted label, the actual weight of the model label and the actual weight of the fact label, and generates the user portrait according to the proportion, so as to ensure that the user portrait can truly display the interest and hobbies, basic information and the like of the client, and improve the accuracy of the user portrait.
Further, the calculating actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customer and all the customers to respectively obtain the actual weights of the prediction labels, the actual weights of the model labels and the actual weights of the fact labels includes:
counting the label quantity of the prediction label, the model label and the fact label of the client to respectively obtain the number of the prediction label, the number of the model label and the number of the fact label;
calculating the proportions of the predicted label, the model label and the fact label in all labels of the client according to the number of the predicted labels, the number of the model labels and the number of the fact labels, and respectively obtaining a first weight of the predicted label, a first weight of the model label and a first weight of the fact label;
counting the number of the prediction labels, the model labels and the number of the fact labels of all the customers to respectively obtain the total number of the prediction labels, the total number of the model labels and the total number of the fact labels;
calculating the occupation ratios of the prediction labels, the model labels and the fact labels in all the labels of all the customers according to the total prediction label number, the total model label number and the total fact label number, and respectively obtaining a second weight of the prediction labels, a second weight of the model labels and a second weight of the fact labels;
and calculating the actual weights of the prediction label, the model label and the fact label by using a TF-IDF algorithm according to the first weight and the second weight of the prediction label, the first weight and the second weight of the model label and the first weight and the second weight of the fact label. The message pushing module 103 is configured to determine one or more user tags of each client according to the user representation of each client, match the message tags with the user tags, and push the message to be pushed to the corresponding client according to a matching result.
In the embodiment of the present invention, the user tag may be tag data corresponding to an enterprise message tag, and includes a consumption tag, a content tag, a date tag, an age tag, a high latency tag, and the like.
In an optional embodiment of the present invention, the user representation includes attribute data of the client, a tag keyword may be determined by matching the attribute data with a preset candidate word template, and a user tag of the client may be generated according to the tag keyword.
In detail, said determining one or more user tags of said customer based on said user representation of each said customer comprises:
matching the user portrait with a pre-constructed candidate word template to obtain a candidate word set in the user portrait;
performing key information classification judgment on the candidate word set by using a pre-trained key information classification model to obtain key information in the user portrait;
and generating one or more user tags of the client according to the key information.
In the embodiment of the invention, the message label is matched with the user label, so that the messages pushed to the client are all messages required by the user, the situation that the user experience is poor due to message pushing is reduced, and the accuracy of enterprise and micro message pushing is improved.
In an alternative embodiment of the invention, the interest points of some customers change with time, for example, a customer needs to drink a lot of coffee due to working pressure in a period of time, for which a lot of information about coffee is searched, and when the customer has no working pressure, it is obviously not very accurate to push the coffee coupon to the customer. Therefore, when the information is pushed to the client according to the original user portrait, the feedback information of the client can be obtained in real time, and the user portrait is adjusted according to the feedback information, so that the pushing of the enterprise micro information is more accurate.
In detail, after the message to be pushed is pushed to the corresponding client according to the matching result, the method further includes:
collecting feedback information of the client to the message to be pushed;
identifying and classifying the feedback information to obtain negative feedback information;
and adjusting the corresponding user portrait of the client according to the negative feedback information.
Fig. 3 is a schematic structural diagram of an electronic device implementing a user portrait-based message push method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a user-image based message push program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as a code of a message push program based on a user profile, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., a message push program based on a user profile, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The user image based message pushing program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
acquiring a message to be pushed, analyzing the class of the message to be pushed by using a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the class;
acquiring basic information of a client and historical behavior data of the client, and performing statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client;
modeling analysis is carried out according to the fact label to obtain a model label of the customer, and prediction analysis is carried out on the model label to obtain a prediction label of the customer;
generating a user portrait of each customer according to the fact label, the model label and the prediction label;
determining one or more user tags for each of the customers based on the customer representation;
and matching the message label with the user label, and pushing the message to be pushed to a corresponding client according to a matching result.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
acquiring a message to be pushed, analyzing the class of the message to be pushed by using a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the class;
acquiring basic information of a client and historical behavior data of the client, and performing statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client;
modeling and analyzing according to the fact label to obtain a model label of the customer, and performing predictive analysis on the model label to obtain a predictive label of the customer;
generating a user representation of each said customer based on said fact tags, model tags and prediction tags;
determining one or more user tags for each of the customers based on the user representation of the customer;
and matching the message label with the user label, and pushing the message to be pushed to a corresponding client according to a matching result.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not to denote any particular order.
Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A user profile-based message pushing method, the method comprising:
acquiring a message to be pushed, analyzing the class of the message to be pushed by using a pre-constructed multi-classification model, and setting one or more message labels for the message to be pushed according to the class;
acquiring basic information of a client and historical behavior data of the client, and performing statistical analysis on the basic information and the historical behavior data to obtain a fact label of the client;
modeling and analyzing according to the fact label to obtain a model label of the customer, and performing predictive analysis on the model label to obtain a predictive label of the customer;
generating a user representation of each said customer based on said fact tags, model tags and prediction tags;
determining one or more user tags for each of the customers based on the user representation of the customer;
and matching the message label with the user label, and pushing the message to be pushed to a corresponding client according to a matching result.
2. A user representation-based message pushing method as recited in claim 1, wherein said generating a user representation for each of said clients based on said predictive tags, model tags, and fact tags comprises:
calculating actual weights of the prediction labels, the model labels and the fact labels according to the number of the prediction labels, the model labels and the fact labels in the current customers and all the customers to respectively obtain actual weights of the prediction labels, the model labels and the fact labels;
and generating a user portrait of each client according to the actual weight of the prediction label, the actual weight of the model label and the actual weight of the fact label.
3. The user representation-based message pushing method of claim 2, wherein the calculating actual weights of the predicted tag, the model tag and the fact tag according to the number of the predicted tag, the model tag and the fact tag in the current customer and all the customers to obtain the predicted tag actual weight, the model tag actual weight and the fact tag actual weight respectively comprises:
counting the label quantity of the prediction label, the model label and the fact label of the client to respectively obtain the number of the prediction label, the number of the model label and the number of the fact label;
calculating the proportions of the predicted label, the model label and the fact label in all labels of the client according to the number of the predicted labels, the number of the model labels and the number of the fact labels, and respectively obtaining a first weight of the predicted label, a first weight of the model label and a first weight of the fact label;
counting the number of the prediction labels, the model labels and the number of the fact labels of all the customers to respectively obtain the total number of the prediction labels, the total number of the model labels and the total number of the fact labels;
calculating the occupation ratios of the prediction labels, the model labels and the fact labels in all the labels of all the customers according to the total prediction label number, the total model label number and the total fact label number, and respectively obtaining a second weight of the prediction labels, a second weight of the model labels and a second weight of the fact labels;
and calculating the actual weights of the prediction label, the model label and the fact label by using a TF-IDF algorithm according to the first weight and the second weight of the prediction label, the first weight and the second weight of the model label and the first weight and the second weight of the fact label.
4. The user representation-based message pushing method of claim 1, wherein said determining one or more user tags for each of said clients based on said user representation of said client comprises:
matching the user portrait with a pre-constructed candidate word template to obtain a candidate word set in the user portrait;
performing key information classification judgment on the candidate word set by using a pre-trained key information classification model to obtain key information in the user portrait;
and generating one or more user tags of the client according to the key information.
5. The user representation-based message pushing method of claim 1, wherein the analyzing the category to which the message to be pushed belongs by using a pre-constructed multi-classification model comprises:
acquiring a training data set consisting of a historical push message data set and a category to which the historical push message data set belongs, and constructing a first training set and a second training set based on the training data set;
obtaining a pre-constructed original multi-classification model, and performing model training on the original multi-classification model pair by using the first training set to obtain an initial multi-classification model;
performing model test on the initial multi-classification model by using the second training set, and adjusting the hyper-parameters in the initial multi-classification model according to a test result to obtain a trained multi-classification model;
and classifying the message to be pushed by using the trained multi-classification model to obtain the category of the message to be pushed.
6. The user representation-based message pushing method of claim 5, wherein the analyzing the category of the message to be pushed by using the pre-constructed multi-classification model comprises:
performing word segmentation processing on the message to be pushed to obtain a word segmentation message set;
calculating the weight of each word in the word segmentation message set by using a TF-LDF algorithm to obtain the weight of the word;
extracting words in the word segmentation message set corresponding to the word weight larger than a preset threshold value to obtain keywords in the word segmentation message set;
performing part-of-speech tagging on the keyword by searching a preset dictionary to determine the meaning of the keyword;
and determining the category of the message to be pushed according to the word meaning of the keyword.
7. The user portrait based message pushing method of any one of claims 1 to 6, wherein after the message to be pushed is pushed to the corresponding client according to the matching result, the method further comprises:
collecting feedback information of the client to the message to be pushed;
identifying and classifying the feedback information to obtain negative feedback information;
and adjusting the corresponding user portrait of the client according to the negative feedback information.
8. A user profile based message push apparatus, comprising:
the message to be pushed and tag module is used for acquiring a message to be pushed, analyzing the class of the message to be pushed by utilizing a pre-constructed multi-classification model, and setting one or more message tags for the message to be pushed according to the class;
the user portrait generation module is used for acquiring basic information of a client, performing statistical analysis on the basic information and historical behavior data according to the basic information acquisition and the historical behavior data of the client to obtain a fact label of the client, performing modeling analysis according to the fact label to obtain a model label of the client, performing predictive analysis on the model label to obtain a predictive label of the client, and generating a user portrait of each client according to the fact label, the model label and the predictive label;
and the message pushing module is used for determining one or more user tags of the client according to the user portrait of each client, matching the message tags with the user tags, and pushing the message to be pushed to the corresponding client according to the matching result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to cause the at least one processor to perform a user representation-based message push method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the user portrait based message push method according to any one of claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115983902A (en) * | 2023-01-10 | 2023-04-18 | 苏州盈天地资讯科技有限公司 | Information pushing method and system based on user real-time event |
| CN116319395A (en) * | 2023-03-21 | 2023-06-23 | 石家庄卡尚科技有限公司 | Big data-based computer information processing system and method |
| CN116823382A (en) * | 2023-05-17 | 2023-09-29 | 南京邮电大学 | Product popularization method based on big data |
| CN117216803A (en) * | 2023-11-09 | 2023-12-12 | 成都乐超人科技有限公司 | Intelligent finance-oriented user information protection method and system |
| CN117235586A (en) * | 2023-11-16 | 2023-12-15 | 青岛小帅智能科技股份有限公司 | Hotel customer portrait construction method, system, electronic equipment and storage medium |
| CN117492738A (en) * | 2023-11-08 | 2024-02-02 | 交通银行股份有限公司北京市分行 | Full flow method and device for data mining |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6421751B1 (en) * | 1998-11-23 | 2002-07-16 | Advanced Micro Devices, Inc. | Detecting a no-tags-free condition in a computer system having multiple outstanding transactions |
| CN108427661A (en) * | 2018-03-19 | 2018-08-21 | 广州汇智通信技术有限公司 | A kind of new big data label manufacturing process and device |
| CN114387061A (en) * | 2022-01-12 | 2022-04-22 | 平安普惠企业管理有限公司 | Product pushing method and device, electronic equipment and readable storage medium |
-
2022
- 2022-05-31 CN CN202210610313.3A patent/CN115002200B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6421751B1 (en) * | 1998-11-23 | 2002-07-16 | Advanced Micro Devices, Inc. | Detecting a no-tags-free condition in a computer system having multiple outstanding transactions |
| CN108427661A (en) * | 2018-03-19 | 2018-08-21 | 广州汇智通信技术有限公司 | A kind of new big data label manufacturing process and device |
| CN114387061A (en) * | 2022-01-12 | 2022-04-22 | 平安普惠企业管理有限公司 | Product pushing method and device, electronic equipment and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 郭梁 等: "基于模型融合的搜索引擎用户画像技术", 科技与创新, no. 07 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115983902A (en) * | 2023-01-10 | 2023-04-18 | 苏州盈天地资讯科技有限公司 | Information pushing method and system based on user real-time event |
| CN115983902B (en) * | 2023-01-10 | 2023-10-20 | 苏州盈天地资讯科技有限公司 | Information pushing method and system based on user real-time event |
| CN116319395A (en) * | 2023-03-21 | 2023-06-23 | 石家庄卡尚科技有限公司 | Big data-based computer information processing system and method |
| CN116319395B (en) * | 2023-03-21 | 2023-10-31 | 苏州利博特信息科技有限公司 | Big data-based computer information processing system and method |
| CN116823382A (en) * | 2023-05-17 | 2023-09-29 | 南京邮电大学 | Product popularization method based on big data |
| CN116823382B (en) * | 2023-05-17 | 2024-01-05 | 南京邮电大学 | Product popularization method based on big data |
| CN117492738A (en) * | 2023-11-08 | 2024-02-02 | 交通银行股份有限公司北京市分行 | Full flow method and device for data mining |
| CN117216803A (en) * | 2023-11-09 | 2023-12-12 | 成都乐超人科技有限公司 | Intelligent finance-oriented user information protection method and system |
| CN117216803B (en) * | 2023-11-09 | 2024-02-09 | 成都乐超人科技有限公司 | Intelligent finance-oriented user information protection method and system |
| CN117235586A (en) * | 2023-11-16 | 2023-12-15 | 青岛小帅智能科技股份有限公司 | Hotel customer portrait construction method, system, electronic equipment and storage medium |
| CN117235586B (en) * | 2023-11-16 | 2024-02-09 | 青岛小帅智能科技股份有限公司 | Hotel customer portrait construction method, system, electronic equipment and storage medium |
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