CN112733023A - Information pushing method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The invention relates to a data processing technology, and discloses an information pushing method, which comprises the following steps: acquiring an information browsing trace data set of a user, and judging whether the user is an active user; if the user is an active user, classifying the information data subsets in the information browsing trace data set, calculating the preference degree of the user to different types of information data in the information data subsets, generating a first information recommendation list according to the preference degree, and pushing information in the first information recommendation list to the user; if the user is an inactive user, acquiring a similar user set according to the information data subset in the information browsing trace data set, generating a second information recommendation list according to the information score of the information set to be recommended of the similar user set, and pushing information in the second information recommendation list to the user. In addition, the invention also relates to a block chain technology, and the data set of the information browsing trace can be stored in the block chain nodes. The invention can improve the efficiency of information push and carry out personalized information push.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information pushing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of networks, a large amount of information may be generated in the network at any time. When information is pushed to a user, if massive information is pushed to a client, a large network bandwidth is consumed, a large storage resource is occupied, and the operating efficiency of a server and the client is reduced. How to screen out the information meeting the user and push the information to the user becomes an increasingly important requirement.
The mainstream information push method in the market at present is to manually screen information, so as to selectively push information to users. However, this method is too dependent on manual operation, the efficiency is low, and the screened information cannot be accurately matched with the user, and efficient and personalized information push cannot be achieved.
Disclosure of Invention
The invention provides an information pushing method, an information pushing device, electronic equipment and a computer readable storage medium, and mainly aims to improve the information pushing efficiency and perform personalized information pushing.
To achieve the above object, the present invention provides an information pushing method, which includes:
acquiring an information browsing trace data set of a user, and judging whether the user is an active user or not according to the information browsing trace data set;
if the user is an active user, classifying the information data subsets in the information browsing trace data set, calculating the preference degrees of the user to different types of information data in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degrees, generating a first information recommendation list, and pushing information in the first information recommendation list to the user;
if the user is an inactive user, acquiring a similar user set according to the information data subset in the information browsing trace data set, scoring the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user.
Optionally, the pushing information in the first information recommendation list to the user includes:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
Optionally, the extracting the time-series feature of the information browsing trace data set includes:
extracting the time sequence feature b of the information browsing trace data set by using a time sequence feature extraction algorithmu(t)：
Wherein,
information data is collected for the information browsing trace data set,
is the historical browsing time of the information data, t-tuA time interval for browsing said information data for said user.
Optionally, the classifying the information data subset in the information browsing trace data set includes:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
Optionally, the classification function is:
wherein theta is a preset system parameter, X(i)Is the ith information data in the information data subset, e is the natural logarithm, g (theta X)(i)) Is the classification result.
In order to solve the above problem, the present invention further provides an information pushing apparatus, including:
the user type judging module is used for acquiring an information browsing trace data set of a user and judging whether the user is an active user or not according to the information browsing trace data set;
the active user pushing module is used for classifying the information data subsets in the information browsing trace data set if the user is an active user, calculating the preference degree of the user to the information data of different types in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degree, generating a first information recommendation list, and pushing the information in the first information recommendation list to the user;
and the inactive user pushing module is used for acquiring a similar user set according to the information data subset in the information browsing trace data set if the user is an inactive user, grading the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user.
Optionally, the pushing, by the active user pushing module, information in the first information recommendation list to the user includes:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
Optionally, the classifying, by the active user pushing module, the information data subset in the information browsing trace data set includes:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the information pushing method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium including a storage data area and a storage program area, the storage data area storing created data, the storage program area storing a computer program; wherein, the computer program is executed by a processor to implement the information pushing method.
According to the embodiment of the invention, the purpose of accurate personalized pushing is realized by judging whether the user is an active user or not, selecting different modes to generate the information recommendation list according to the type of the user and recommending the information, and meanwhile, manual screening is not needed during recommendation, so that the information pushing efficiency is improved. Therefore, the information pushing method, the information pushing device and the computer readable storage medium provided by the invention can improve the information pushing efficiency and perform personalized information pushing.
Drawings
Fig. 1 is a flowchart illustrating an information pushing method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an information pushing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic internal structural diagram of an electronic device implementing an information 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 execution subject of the information pushing method provided by the embodiment of the present application includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server and a terminal. In other words, the information pushing method may be executed 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 invention provides an information pushing method. Fig. 1 is a flowchart illustrating an information pushing method according to an embodiment of the present invention. In this embodiment, the information pushing method includes:
s1, acquiring an information browsing trace data set of a user, and judging whether the user is an active user according to the information browsing trace data set.
In an embodiment of the present invention, the data set of information browsing traces of the user includes a browsing information set of the user browsing the information data in a browser or a similar client for a period of time, where the data set of information browsing traces includes but is not limited to: the start time of the browsing information, the end time of the browsing information, the title of the browsing information, the content of the browsing information, and the score of the browsed information.
Specifically, a data capture method can be used to obtain the data set of the information browsing trace. For example, using a python statement with a data fetching function to fetch information browsing trace data from a background of a website browsed by a user and/or a block chain for storing the information browsing trace data set, and aggregating all the fetched information browsing trace data of a certain user as an information browsing trace data set of the user, where the information browsing trace data of the user is expressed as: x(i)＝{x(1),…x(n)And x represents a browsing information subset of each browsing information data.
Optionally, the determining whether the user is an active user according to the information browsing trace data set includes:
and determining whether the user is an active user according to browsing information in the information browsing trace data set, wherein the browsing information comprises one or more of, but not limited to, browsing times, browsing time intervals and scores of browsed information.
For example, counting the browsing times of the information data by the user in the information browsing trace data set, determining whether the browsing times reaches a preset time threshold (e.g., 10 times) within a preset time period, and determining that the user is an inactive client when the browsing times is less than or equal to the time threshold; and when the browsing times are larger than the time threshold value, judging that the user is an active user.
For another example, the browsing times and the browsing time interval of the user in the information browsing trace data set are counted, and if the browsing times of the user is greater than a preset time threshold (for example, 10 times), and the browsing time interval is smaller than a preset interval time or the browsing time interval gradually decreases and tends to be stable, the user is determined to be an active user; and if the browsing times of the user are less than the preset times or the browsing time interval is greater than the preset interval time, judging that the user is an inactive user.
S2, if the user is an active user, classifying the information data subsets in the information browsing trace data set, calculating the preference degree of the user to the information data of different types in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degree, generating a first information recommendation list, and pushing the information in the first information recommendation list to the user.
In this embodiment, the information data subset includes a plurality of information data, wherein the information data includes content of information, such as text content of information, title of information, and the like.
In this embodiment, the information data subsets in the information browsing trace data set can be classified from different dimensions. For example, the information data in the information data subset is classified according to the type of the data; or the information data in the information data subset is classified according to the author of the information data.
Specifically, in this embodiment, feature extraction is performed on the information data subsets in the information browsing trace data set, and then the information feature data subsets after feature extraction are classified.
Preferably, in the embodiment of the present invention, a convolutional neural network is adopted to perform feature extraction on an information data subset in an information browsing trace data set to obtain an information feature data subset, where the information feature data subset includes information feature data, and the information feature data subset includes: information title, information length, information keyword, information industry distribution, information-related stock distribution, etc.
Preferably, in an optional embodiment of the present invention, the classifying the information data subset in the information browsing trace data set includes:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
Further, the classification function is:
wherein theta is a preset system parameter, X(i)Is the ith information data in the information data subset, e is the natural logarithm, g (theta X)(i)) Is the classification result.
Preferably, the classification result includes a category corresponding to each information data in the information data subset, such as news category, advertisement category, etc.
Further, in an optional embodiment of the present invention, based on the browsing times of the information data by the user, the preference degrees of the user for different categories of the information data are calculated.
In detail, the present invention calculates the preference degree using a preference algorithm as follows:
p(o)＝m/n
wherein n is the total browsing times of the user for all information, m is the browsing times of the user for a certain category of information data, and p (o) is the preference degree of the user for the category of information data.
In this embodiment, the information set to be recommended may be a newly generated set of information data that is not pushed to the user.
The selecting a plurality of information data from the information set to be recommended according to the preference degree comprises the following steps:
and calculating the similarity between the type of the information data in the information set to be recommended and the preference degree, selecting a plurality of information data with the similarity larger than the preset similarity, and generating a first information recommendation list.
Further, in an optional embodiment of the present invention, the pushing information in the first information recommendation list to the user includes:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
Further, in another optional embodiment of the present invention, the extracting the time-series characteristic of the data set of the information browsing trace includes:
extracting the time sequence feature b of the information browsing trace data set by using a time sequence feature extraction algorithmu(t)：
Wherein,
browsing trace data for said informationThe information data are collected and then transmitted to the mobile terminal,
is the historical browsing time, t-t, of the information datauA time interval for browsing said information data for said user.
In this embodiment, after the time sequence feature is obtained, the time sequence feature is subjected to mathematical statistics in the embodiment of the present invention, so as to obtain the browsing time preference of the user browsing information, and then the push time is determined according to the browsing time preference. If the user L prefers to browse the information data in the morning and the user J prefers to browse the information data in the afternoon, the information in the first information recommendation list is pushed to the user L in the morning and the information in the first information recommendation list is pushed to the user J in the afternoon.
Preferably, the embodiment of the invention can adopt the timer to push the information data to the user according to the pushing time, so as to achieve the purpose of personalized pushing in time.
In the embodiment, the first information recommendation list is determined by calculating the preference programs of the user on different information data, and personalized push can be rapidly and accurately performed.
S3, if the user is an inactive user, obtaining a similar user set according to the information data subset in the information browsing trace data set, scoring the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user. In this embodiment, the similar user set may include a plurality of similar users, where the similar users are users who have browsed the same or similar information data as the users, or the similar users are users who have browsed the same or similar information data as the users' basic information.
Optionally, the obtaining a similar user set according to the information data subset in the information browsing trace data set includes:
and performing feature extraction on the information data subset in the information browsing trace data set, and determining a similar user set according to a feature extraction result.
Preferably, in the embodiment of the present invention, a convolutional neural network is adopted to perform feature extraction on an information data subset in an information browsing trace data set to obtain an information feature data subset, where the information feature data subset includes information feature data, and the information feature data subset includes: information title, information length, information keyword, information industry distribution, information-related stock distribution, information score, etc.
Calculating the similarity sim (a, b) of the inactive user and the similar users in the similar user set by using the following similarity algorithm:
wherein a is the inactive user, b is a similar user in the similar user set,
is the average value of the scores of the information data in the information data subset by the user a,
mean value, r, of the score of the information data in the information data subset for subscriber ba，pRating, r, of information data p in the information data subset for user ab，pThe user b is given a rating for information data P in the information data subset, P being said information data subset.
Preferably, in the embodiment of the present invention, the similar users with the highest similarity to the inactive user in the similar user set are screened out, a preset scoring algorithm is used to calculate the prediction scores of the to-be-recommended information in the to-be-recommended information set by the similar users, the to-be-recommended information with the prediction scores larger than a preset scoring threshold in the to-be-recommended information set is selected, and the to-be-recommended information is collected into the second information recommendation list.
In this embodiment, the information set to be recommended may be a newly generated set of information data that is not pushed to the user.
In detail, the scoring algorithm is:
wherein pred (x, b) is the score of a similar user b on information x to be recommended in the information set to be recommended, b is the similar user, N is the similar user set, x is the information to be recommended in the information set to be recommended, rb，pFor user b's rating of information data p in the information data subset,
is the average value of the scores of the information data in the information data subset for the user b,
sim (a, b) is the similarity between the inactive user a and the similar user b in the similar user set, and is a preset score base value.
Due to the fact that the information browsing amount of the inactive users is small, the browsing preference of the inactive users is difficult to be accurately found through the browsing traces of the inactive users, and therefore the information is difficult to be accurately pushed. In a preferred embodiment of the invention, the purpose of accurate personalized push is realized by acquiring the similar user set, generating the second information recommendation list according to the information score of the information set to be recommended by the similar user set, and pushing based on the second information recommendation list.
According to the embodiment of the invention, the purpose of accurate personalized pushing is realized by judging whether the user is an active user or not, selecting different modes to generate the information recommendation list according to the type of the user and recommending the information, and meanwhile, manual screening is not needed during recommendation, so that the information pushing efficiency is improved. Therefore, the information pushing method provided by the invention can improve the information pushing efficiency and carry out personalized information pushing.
FIG. 2 is a block diagram of an information pushing apparatus according to the present invention.
The information pushing apparatus 100 of the present invention can be installed in an electronic device. According to the implemented functions, the information pushing apparatus may include a user category determining module 101, an active user pushing module 102 and an inactive user pushing module 103. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the user type judging module 101 is configured to obtain an information browsing trace data set of a user, and judge whether the user is an active user according to the information browsing trace data set;
the active user pushing module 102 is configured to, if the user is an active user, classify the information data subsets in the information browsing trace data set, calculate preference degrees of the user for different types of information data in the information data subsets, select a plurality of information data from the information set to be recommended according to the preference degrees, generate a first information recommendation list, and push information in the first information recommendation list to the user;
the inactive user pushing module 103 is configured to, if the user is an inactive user, obtain a similar user set according to the information data subset in the information browsing trace data set, generate a second information recommendation list according to the similar user set by scoring the information of the information set to be recommended, and push information in the second information recommendation list to the user.
In detail, the specific implementation of each module of the device for extracting and generating the text content in the image is as follows:
the user type judging module 101 is configured to obtain an information browsing trace data set of a user, and judge whether the user is an active user according to the information browsing trace data set.
In an embodiment of the present invention, the data set of information browsing traces of the user includes a browsing information set of the user browsing the information data in a browser or a similar client for a period of time, where the data set of information browsing traces includes but is not limited to: the start time of the browsing information, the end time of the browsing information, the title of the browsing information, the content of the browsing information, and the score of the browsed information.
Specifically, the user type determining module 101 may capture information browsing trace data from a background of a website browsed by a user using a python statement having a data capture function, and collect all captured information browsing trace data of a certain user as an information browsing trace data set of the user, where the information browsing trace data of the user is represented as: x(i)＝{x(1),…x(n)And x represents a browsing information subset of each browsing information data.
Optionally, the determining whether the user is an active user according to the information browsing trace data set includes:
and determining whether the user is an active user according to browsing information in the information browsing trace data set, wherein the browsing information comprises one or more of, but not limited to, browsing times, browsing time intervals and scores of browsed information.
For example, counting the browsing times of the information data by the user in the information browsing trace data set, determining whether the browsing times reaches a preset time threshold (e.g., 10 times) within a preset time period, and determining that the user is an inactive client when the browsing times is less than or equal to the time threshold; and when the browsing times are larger than the time threshold value, judging that the user is an active user.
For another example, the browsing times and the browsing time interval of the user in the information browsing trace data set are counted, and if the browsing times of the user is greater than a preset time threshold (for example, 10 times), and the browsing time interval is smaller than a preset interval time or the browsing time interval gradually decreases and tends to be stable, the user is determined to be an active user; and if the browsing times of the user are less than the preset times or the browsing time interval is greater than the preset interval time, judging that the user is an inactive user.
The active user pushing module 102 is configured to, if the user is an active user, classify the information data subsets in the information browsing trace data set, calculate preference degrees of the user for different types of information data in the information data subsets, select a plurality of information data from the information set to be recommended according to the preference degrees, generate a first information recommendation list, and push information in the first information recommendation list to the user.
In this embodiment, the information data subset includes a plurality of information data, wherein the information data includes content of information, such as text content of information, title of information, and the like.
In this embodiment, the information data subsets in the information browsing trace data set can be classified from different dimensions. For example, the information data in the information data subset is classified according to the type of the data; or the information data in the information data subset is classified according to the author of the information data.
Specifically, in this embodiment, feature extraction is performed on the information data subsets in the information browsing trace data set, and then the information feature data subsets after feature extraction are classified.
Preferably, in the embodiment of the present invention, a convolutional neural network is adopted to perform feature extraction on an information data subset in an information browsing trace data set to obtain an information feature data subset, where the information feature data subset includes information feature data, and the information feature data subset includes: information title, information length, information keyword, information industry distribution, information-related stock distribution, etc.
Preferably, in an optional embodiment of the present invention, the classifying, by the active user pushing module, the subset of the information data in the information browsing trace data set includes:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
Further, the classification function is:
wherein theta is a preset system parameter, X(i)Is the ith information data in the information data subset, e is the natural logarithm, g (theta X)(i)) Is the classification result.
Preferably, the classification result includes a category corresponding to each information data in the information data subset, such as news category, advertisement category, etc.
Further, in an optional embodiment of the present invention, based on the browsing times of the information data by the user, the preference degrees of the user for different categories of the information data are calculated.
In detail, the present invention calculates the preference degree using a preference algorithm as follows:
p(o)＝m/n
wherein n is the total browsing times of the user for all information, m is the browsing times of the user for a certain category of information data, and p (o) is the preference degree of the user for the category of information data.
In this embodiment, the information set to be recommended may be a newly generated set of information data that is not pushed to the user.
The selecting a plurality of information data from the information set to be recommended according to the preference degree comprises the following steps:
and calculating the similarity between the type of the information data in the information set to be recommended and the preference degree, selecting a plurality of information data with the similarity larger than the preset similarity, and generating a first information recommendation list.
Further, in an optional embodiment of the present invention, the pushing, by the active user pushing module, information in the first information recommendation list to the user includes:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
Further, in another optional embodiment of the present invention, the extracting the time-series characteristic of the data set of the information browsing trace includes:
extracting the time sequence feature b of the information browsing trace data set by using a time sequence feature extraction algorithmu(t)：
Wherein,
information data is collected for the information browsing trace data set,
is the historical browsing time, t-t, of the information datauA time interval for browsing said information data for said user.
In this embodiment, after the time sequence feature is obtained, the time sequence feature is subjected to mathematical statistics in the embodiment of the present invention, so as to obtain the browsing time preference of the user browsing information, and then the push time is determined according to the browsing time preference. If the user L prefers to browse the information data in the morning and the user J prefers to browse the information data in the afternoon, the information in the first information recommendation list is pushed to the user L in the morning and the information in the first information recommendation list is pushed to the user J in the afternoon.
Preferably, the embodiment of the invention can adopt the timer to push the information data to the user according to the pushing time, so as to achieve the purpose of personalized pushing in time.
In the embodiment, the first information recommendation list is determined by calculating the preference programs of the user on different information data, and personalized push can be rapidly and accurately performed.
The inactive user pushing module 103 is configured to, if the user is an inactive user, obtain a similar user set according to the information data subset in the information browsing trace data set, generate a second information recommendation list according to the similar user set by scoring the information of the information set to be recommended, and push information in the second information recommendation list to the user.
In this embodiment, the similar user set may include a plurality of similar users, where the similar users are users who have browsed the same or similar information data as the users, or the similar users are users who have browsed the same or similar information data as the users' basic information.
Optionally, the obtaining a similar user set according to the information data subset in the information browsing trace data set includes:
and performing feature extraction on the information data subset in the information browsing trace data set, and determining a similar user set according to a feature extraction result.
Preferably, in the embodiment of the present invention, a convolutional neural network is adopted to perform feature extraction on an information data subset in an information browsing trace data set to obtain an information feature data subset, where the information feature data subset includes information feature data, and the information feature data subset includes: information title, information length, information keyword, information industry distribution, information-related stock distribution, information score, etc.
Calculating the similarity sim (a, b) of the inactive user and the similar users in the similar user set by using the following similarity algorithm:
wherein a is the inactive user, b is a similar user in the similar user set,
is the average value of the scores of the information data in the information data subset by the user a,
mean value, r, of the score of the information data in the information data subset for subscriber ba，pIs a pair of usersRating, r, of information data p in a subset of information datab，pThe user b is given a rating for information data P in the information data subset, P being said information data subset.
Preferably, in the embodiment of the present invention, the similar users with the highest similarity to the inactive user in the similar user set are screened out, a preset scoring algorithm is used to calculate the prediction scores of the to-be-recommended information in the to-be-recommended information set by the similar users, the to-be-recommended information with the prediction scores larger than a preset scoring threshold in the to-be-recommended information set is selected, and the to-be-recommended information is collected into the second information recommendation list.
In this embodiment, the information set to be recommended may be a newly generated set of information data that is not pushed to the user.
In detail, the scoring algorithm is:
wherein pred (x, b) is the score of a similar user b on information x to be recommended in the information set to be recommended, b is the similar user, N is the similar user set, x is the information to be recommended in the information set to be recommended, rb，pFor user b's rating of information data p in the information data subset,
is the average value of the scores of the information data in the information data subset for the user b,
sim (a, b) is the similarity between the inactive user a and the similar user b in the similar user set, and is a preset score base value.
Due to the fact that the information browsing amount of the inactive users is small, the browsing preference of the inactive users is difficult to be accurately found through the browsing traces of the inactive users, and therefore the information is difficult to be accurately pushed. In a preferred embodiment of the invention, the purpose of accurate personalized push is realized by acquiring the similar user set, generating the second information recommendation list according to the information score of the information set to be recommended by the similar user set, and pushing based on the second information recommendation list.
According to the embodiment of the invention, the purpose of accurate personalized pushing is realized by judging whether the user is an active user or not, selecting different modes to generate the information recommendation list according to the type of the user and recommending the information, and meanwhile, manual screening is not needed during recommendation, so that the information pushing efficiency is improved. Therefore, the information pushing device provided by the invention can improve the efficiency of information pushing and carry out personalized information pushing.
Fig. 3 is a schematic structural diagram of an electronic device implementing the information push method according to the present invention.
The electronic device 1 may include a processor 10, a memory 11 and a bus, and may further include a computer program, such as an information pushing program 12, stored in the memory 11 and operable 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 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 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 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used to store not only the application software installed in the electronic device 1 and various data, such as the codes of the information pushing program 12, but also temporarily store data that has been output or will 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, and connects various components of the whole electronic device by using various interfaces and lines, so as to execute various functions of the electronic device 1 and process data by running or executing programs or modules (for example, executing an information push program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise 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 1 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 as to implement functions of charge management, discharge management, power consumption management, and the like 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 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, 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 for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
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 information pushing program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can implement:
acquiring an information browsing trace data set of a user, and judging whether the user is an active user or not according to the information browsing trace data set;
if the user is an active user, classifying the information data subsets in the information browsing trace data set, calculating the preference degrees of the user to different types of information data in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degrees, generating a first information recommendation list, and pushing information in the first information recommendation list to the user;
if the user is an inactive user, acquiring a similar user set according to the information data subset in the information browsing trace data set, scoring the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user.
According to the embodiment of the invention, the purpose of accurate personalized pushing is realized by judging whether the user is an active user or not, selecting different modes to generate the information recommendation list according to the type of the user and recommending the information, and meanwhile, manual screening is not needed during recommendation, so that the information pushing efficiency is improved. Therefore, the efficiency of information push can be improved, and personalized information push can be performed.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. 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).
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 embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. 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 accompanying claims should 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.
Furthermore, it is obvious that the word "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 any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, 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 may be made on 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. An information pushing method, the method comprising:
acquiring an information browsing trace data set of a user, and judging whether the user is an active user or not according to the information browsing trace data set;
if the user is an active user, classifying the information data subsets in the information browsing trace data set, calculating the preference degrees of the user to different types of information data in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degrees, generating a first information recommendation list, and pushing information in the first information recommendation list to the user;
if the user is an inactive user, acquiring a similar user set according to the information data subset in the information browsing trace data set, scoring the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user.
2. The information pushing method of claim 1, wherein the pushing of the information in the first information recommendation list to the user comprises:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
3. The information pushing method of claim 2, wherein the extracting the time-series feature of the information browsing trace data set comprises:
extracting the time sequence feature b of the information browsing trace data set by using a time sequence feature extraction algorithmu(t)：
Wherein,
information data is collected for the information browsing trace data set,
is the historical browsing time, t-t, of the information datauA time interval for browsing said information data for said user.
4. The information pushing method of any one of claims 1 to 3, wherein the classifying the subset of information data in the information browsing trace data set comprises:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
5. The information push method according to claim 4, wherein the classification function is:
wherein theta is a preset system parameter, X(i)Is the ith information data in the information data subset, e is the natural logarithm, g (theta X)(i)) Is the classification result.
6. An information pushing apparatus, the apparatus comprising:
the user type judging module is used for acquiring an information browsing trace data set of a user and judging whether the user is an active user or not according to the information browsing trace data set;
the active user pushing module is used for classifying the information data subsets in the information browsing trace data set if the user is an active user, calculating the preference degree of the user to the information data of different types in the information data subsets, selecting a plurality of information data from the information set to be recommended according to the preference degree, generating a first information recommendation list, and pushing the information in the first information recommendation list to the user;
and the inactive user pushing module is used for acquiring a similar user set according to the information data subset in the information browsing trace data set if the user is an inactive user, grading the information of the information set to be recommended according to the similar user set to generate a second information recommendation list, and pushing the information in the second information recommendation list to the user.
7. The information pushing device of claim 6, wherein the active user pushing module pushes the information in the first information recommendation list to the user includes:
extracting the time sequence characteristics of the information browsing trace data set;
counting the browsing time preference of the user according to the time sequence characteristics;
determining pushing time according to the browsing time preference;
and pushing the information in the first information recommendation list to the user at the pushing time.
8. The information pushing device of claim 6, wherein the active user pushing module classifying the subset of information data in the information browsing trace data set comprises:
classifying the information data subsets in the information browsing trace data set by using a plurality of classification functions to obtain a plurality of classification results;
and determining the category of the information data in the information data subset according to the classification result with the maximum probability value in the classification results.
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 memory stores instructions executable by the at least one processor to enable the at least one processor to perform the information pushing method according to any one of claims 1 to 5.
10. A computer-readable storage medium comprising a storage data area storing created data and a storage program area storing a computer program; wherein the computer program is executed by a processor to implement the information pushing method according to any one of claims 1 to 5.
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| CN202011644071.7A CN112733023A (en) | 2020-12-30 | 2020-12-30 | Information pushing method and device, electronic equipment and computer readable storage medium |
| PCT/CN2021/090700 WO2022142020A1 (en) | 2020-12-30 | 2021-04-28 | Information pushing method and apparatus, electronic device, and computer-readable storage medium |
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| CN113139129A (en) * | 2021-05-12 | 2021-07-20 | 平安国际智慧城市科技股份有限公司 | Virtual reading track map generation method and device, electronic equipment and storage medium |
| CN114579916A (en) * | 2022-05-06 | 2022-06-03 | 深圳格隆汇信息科技有限公司 | Big data based information recommendation method and system |
| CN115348353A (en) * | 2022-08-10 | 2022-11-15 | 青岛海信智慧生活科技股份有限公司 | Content pushing method and server |
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| CN118013129B (en) * | 2024-03-27 | 2024-10-11 | 广东科技学院 | Financial information pushing method, system, equipment and storage medium based on big data |
| CN118075347B (en) * | 2024-04-24 | 2024-08-06 | 杭州高能云科技有限公司 | Information data pushing method, system and storage medium |
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| CN107066582B (en) * | 2017-04-14 | 2020-06-26 | 聚好看科技股份有限公司 | Method and device for realizing virtual resource recommendation |
| CN109977320B (en) * | 2019-04-08 | 2021-07-27 | 北京网聘咨询有限公司 | Information pushing method and system |
| CN110516147B (en) * | 2019-07-22 | 2024-08-09 | 平安科技（深圳）有限公司 | Page data generation method, device, computer equipment and storage medium |
| CN111625713B (en) * | 2020-04-30 | 2024-06-04 | 平安国际智慧城市科技股份有限公司 | Big data-based resource recommendation method and device, electronic equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113139129A (en) * | 2021-05-12 | 2021-07-20 | 平安国际智慧城市科技股份有限公司 | Virtual reading track map generation method and device, electronic equipment and storage medium |
| CN113139129B (en) * | 2021-05-12 | 2024-02-09 | 深圳平安智慧医健科技有限公司 | Virtual reading trajectory graph generation method and device, electronic equipment and storage medium |
| CN114579916A (en) * | 2022-05-06 | 2022-06-03 | 深圳格隆汇信息科技有限公司 | Big data based information recommendation method and system |
| CN114579916B (en) * | 2022-05-06 | 2022-07-29 | 深圳格隆汇信息科技有限公司 | Big data based information recommendation method and system |
| CN115348353A (en) * | 2022-08-10 | 2022-11-15 | 青岛海信智慧生活科技股份有限公司 | Content pushing method and server |
| CN115348353B (en) * | 2022-08-10 | 2024-06-18 | 青岛海信智慧生活科技股份有限公司 | Content pushing method and server |
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