CN115208946B - Message pushing method, message pushing server and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a message pushing method, a message pushing server and a storage medium, which can be applied to the field of mobile interconnection or the field of finance, wherein the method comprises the following steps: receiving a message pushing request sent by a target mobile terminal, and for each initial keyword in the initial keyword group: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, calculating the matching degree of the initial keyword based on the preset word data table, screening each initial keyword based on the matching degree to obtain a keyword group and a suspicious keyword group, and carrying out screening on each suspicious keyword group: calculating a correlation parameter according to the word type of the suspicious keyword by using a preset misjudgment checking algorithm, determining each suspicious keyword with the correlation parameter larger than a second preset threshold as an alternative keyword, determining a message to be pushed according to the keyword group and each alternative keyword, and pushing the message to be pushed to a target mobile terminal. The invention improves the accuracy of message pushing.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a message pushing method, a message pushing server, and a storage medium.
Background
With popularization of terminal applications, message pushing services based on application information and service popularization are more frequent in order to improve user experience and service expansion requirements. The message pushing mode is to push the whole message through the message server. However, these full-push messages have a low degree of match with the user's needs, resulting in reduced accuracy of message pushing. Therefore, in the prior art, a machine learning model is adopted, and the accuracy of message pushing is improved by obtaining user browsing data and filtering the message to be pushed. However, since the machine learning model has a strong generalization, when filtering is performed, the machine learning model may filter out part of the message matching with the user requirement, so that the accuracy of message pushing is reduced.
Disclosure of Invention
The embodiment of the invention aims to provide a message pushing method, a message pushing server and a storage medium, so as to achieve the aim of improving the accuracy of message pushing. The specific technical scheme is as follows:
a message pushing method, the message pushing method comprising:
and receiving a message pushing request sent by the target mobile terminal, wherein the message pushing request comprises an initial keyword group.
For each initial keyword in the initial keyword group: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table.
And screening all the initial keywords based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold.
For each suspicious keyword: and calculating the association parameters of the suspicious keywords according to the word types of the suspicious keywords by using a preset misjudgment checking algorithm.
And determining each suspicious keyword with the association parameter larger than a second preset threshold as an alternative keyword, wherein the first preset threshold and the second preset threshold are different.
And determining a message to be pushed according to the keyword group and each alternative keyword, and pushing the message to be pushed to the target mobile terminal.
Optionally, the calculating, by using a preset false positive verification algorithm, the association parameter of the suspicious keyword according to the word type of the suspicious keyword includes:
and determining word frequency and inverse document frequency of the suspicious keywords according to the word types of the suspicious keywords.
And carrying out product operation on the word frequency and the inverse document frequency by using the preset misjudgment checking algorithm, and determining an operation result as the associated parameter of the suspicious keyword.
Optionally, the determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword includes:
and determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group.
And determining the number of times the suspicious keyword appears in the first numerical value as a second numerical value.
And dividing the second numerical value by the quotient of the first numerical value by using the preset misjudgment checking algorithm, and determining the word frequency of the suspicious keyword.
Optionally, the determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword further includes:
and determining the total number of suspicious keywords in the suspicious keyword group as a third numerical value.
By the formula:
IDF t ＝lg(D n /N t )，
calculating the inverse document frequency IDF of the suspicious keyword t t Wherein the D n Is the third value, the N t And the second value is the second value, and the n is the number of suspicious keywords in the suspicious keyword group.
Optionally, the calculating the matching degree of the initial keyword based on the preset word data table includes:
and taking the initial keyword and each word data in the preset word data table as inputs, inputting the inputs into a preset character string matching algorithm, and obtaining the matching degree output by the preset character string matching algorithm, wherein the matching degree characterizes the association degree of the initial keyword and each word data in the preset word data table.
Optionally, the message pushing request further includes terminal usage data of the mobile terminal, and the pushing the message to be pushed to the target mobile terminal includes:
and reading the use period in the terminal use data.
Judging whether the pushing moment of the message to be pushed is within the using period, if so, pushing the message to be pushed to the target mobile terminal.
A message push server, the message push server comprising:
the data receiving module is used for receiving a message pushing request sent by the target mobile terminal, wherein the message pushing request comprises an initial keyword group.
The matching degree calculation module is used for calculating the initial keywords in the initial keyword groups: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table.
And the first data screening module screens all the initial keywords based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold value.
The misjudgment checking module is used for checking each suspicious keyword: and calculating the association parameters of the suspicious keywords according to the word types of the suspicious keywords by using a preset misjudgment checking algorithm.
And the second data screening module is used for determining each suspicious keyword with the association parameter larger than a second preset threshold as an alternative keyword, wherein the first preset threshold and the second preset threshold are different.
And the message pushing module is used for determining a message to be pushed according to the keyword group and each alternative keyword and pushing the message to be pushed to the target mobile terminal.
Optionally, the misjudgment checking module is configured to:
and determining word frequency and inverse document frequency of the suspicious keywords according to the word types of the suspicious keywords.
And carrying out product operation on the word frequency and the inverse document frequency by using the preset misjudgment checking algorithm, and determining an operation result as the associated parameter of the suspicious keyword.
Optionally, the misjudgment checking module is configured to, when determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword:
and determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group.
And determining the number of times the suspicious keyword appears in the first numerical value as a second numerical value.
And dividing the second numerical value by the quotient of the first numerical value by using the preset misjudgment checking algorithm, and determining the word frequency of the suspicious keyword.
Optionally, the misjudgment checking module is further configured to, when determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword:
and determining the total number of suspicious keywords in the suspicious keyword group as a third numerical value.
By the formula:
IDF t ＝lg(D n /N t )，
calculating the inverse document frequency IDF of the suspicious keyword t t Wherein the D n Is the third value, the N t And the second value is the second value, and the n is the number of suspicious keywords in the suspicious keyword group.
Optionally, the matching degree calculating module is configured to:
and taking the initial keyword and each word data in the preset word data table as inputs, inputting the inputs into a preset character string matching algorithm, and obtaining the matching degree output by the preset character string matching algorithm, wherein the matching degree characterizes the association degree of the initial keyword and each word data in the preset word data table.
Optionally, the message pushing module is configured to:
and reading the use period in the terminal use data.
Judging whether the pushing moment of the message to be pushed is within the using period, if so, pushing the message to be pushed to the target mobile terminal.
A message push server, the message push server comprising:
a processor;
a memory for storing the processor-executable instructions.
Wherein the processor is configured to execute the instructions to implement a message pushing method as claimed in any one of the preceding claims.
A computer readable storage medium, which when executed by a processor of a message push server, causes the message push server to perform a message push method as claimed in any one of the preceding claims.
According to the message pushing method, the message pushing server and the storage medium, the matching degree of the initial keyword is calculated through the preset matching degree calculation model, and the secondary verification of the word type of the initial keyword can be achieved. Thereby improving the accuracy of determining the word type of the initial keyword. And further, the accuracy of determining the message to be pushed according to the initial keyword is improved. Meanwhile, by introducing a preset misjudgment checking algorithm, the association parameters are calculated, so that the association of the suspicious keywords and the word types is checked. Compared with the prior art, the method and the device avoid the risk of carrying out error filtering on the keywords due to generalization of the machine learning model. The accuracy of determining the word types of the keywords is improved, and the accuracy of pushing the final message is further improved. Therefore, the invention achieves the aim of improving the message pushing accuracy.
Of course, it is not necessary for any one product or method of practicing the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a message pushing method provided in an embodiment of the present invention;
FIG. 2 is a block diagram of a message pushing server provided by an alternative embodiment of the present invention;
fig. 3 is a block diagram of a message pushing server according to another alternative embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the message pushing method, the message pushing server and the storage medium provided by the invention can be used in the field of mobile interconnection or the field of finance. The foregoing is merely an example, and is not intended to limit the application fields of the message pushing method, the message pushing server, and the storage medium provided by the present invention.
The embodiment of the invention provides a message pushing method, as shown in fig. 1, which comprises the following steps:
s101, receiving a message pushing request sent by a target mobile terminal, wherein the message pushing request comprises an initial keyword group.
The message push request may be sent by an application plug-in deployed in the target mobile terminal.
Alternatively, in an alternative embodiment of the present invention, the initial keyword group may be a data set composed of keywords characterizing the user's subscription needs or intention. The initial keyword group may include various types of initial keywords such as occupation of the user, public number name of subscription, browser search data, browsing web page titles, browser tab content, etc. The invention filters the push message based on the initial keyword group, thereby improving the accuracy of message push.
S102, for each initial keyword in the initial keyword group: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table.
Alternatively, in an alternative embodiment of the present invention, the word type of the initial keyword may be determined according to a type tag of the initial keyword. For example: judging whether the type label of the initial keyword is matched with data in a preset type data table, if so, determining the word type matched with the type label in the preset type data table as the word type of the initial keyword.
Alternatively, in another optional embodiment of the present invention, the preset word data table may be a data table determined according to historical statistics, where the data table includes a plurality of words with consistent word types. For example, if the word type matched by the current preset word data table is set as "finance", the words in the table may be words such as "crude price", "gold price" and the like.
Optionally, in another optional embodiment of the present invention, the present invention calculates the matching degree of the initial keyword through a preset matching degree calculation model, so as to implement a secondary verification on the word type of the initial keyword. Thereby improving the accuracy of determining the word type of the initial keyword. And further, the accuracy of determining the message to be pushed according to the initial keyword is improved.
S103, screening all the initial keywords based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold.
It should be noted that, in an actual application scenario, the first preset threshold may be determined according to the historical data and the calculation accuracy of the preset matching degree calculation model, and the specific value and the determination manner of the first preset threshold are not excessively limited and described in detail.
S104, for each suspicious keyword: and calculating the association parameters of the suspicious keywords according to the word types of the suspicious keywords by using a preset misjudgment checking algorithm.
The preset misjudgment checking algorithm can be a text mining algorithm constructed based on a term frequency-inverse document frequency (termFrequency-Inverse Document Frequency, TF-IDF) technology.
The association parameter may be a parameter for characterizing a degree of association between the suspicious keyword and the word type.
It should be noted that, the technical concept of TF-IDF is to directly increase the number of times of occurrence of a word in a corpus, and inversely decrease the frequency of occurrence of the word in the corpus. Therefore, in step S103 shown in fig. 1, the matching degree may fluctuate with the number of words in the preset word data table, for example, if the number of words in the table is large, the probability that the initial keyword matches the words in the data table is high. If the number of words in the table is small, the probability of matching the initial keywords with the words in the data table is small. Thus, the suspicious keywords screened out in step S103 shown in fig. 1 are likely to have a certain misjudgment. The correlation parameters are calculated by introducing the preset misjudgment checking algorithm, so that the correlation between the suspicious keywords and the word types is checked. Compared with the prior art, the method and the device avoid the risk of carrying out error filtering on the keywords due to generalization of the machine learning model. The accuracy of determining the word types of the keywords is improved, and the accuracy of pushing the final message is further improved.
S105, determining each suspicious keyword with the association parameter larger than a second preset threshold as an alternative keyword, wherein the first preset threshold and the second preset threshold are different.
Alternatively, in an alternative embodiment of the present invention, the above-mentioned word filtering method using the preset threshold values in step S103 and step S105 shown in fig. 1 may be implemented in various manners, for example, by a random sample consensus (RandomSample Consensus, RANSAC) algorithm.
S106, determining the message to be pushed according to the keyword groups and the candidate keywords, and pushing the message to be pushed to the target mobile terminal.
Alternatively, in an alternative embodiment of the present invention, there may be a plurality of implementations of step S106 shown in fig. 1, for example:
extracting the type identifier of the word type of each keyword in each candidate keyword and keyword group. And determining the push message corresponding to each type identifier in a preset push message library as the message to be pushed. And then calling a corresponding communication interface according to the identifier of the target mobile terminal. And sending the message to be pushed to the target mobile terminal through the communication interface.
According to the method and the device, the matching degree of the initial keyword is calculated through the preset matching degree calculation model, and the secondary verification of the word type of the initial keyword can be achieved. Thereby improving the accuracy of determining the word type of the initial keyword. And further, the accuracy of determining the message to be pushed according to the initial keyword is improved. Meanwhile, by introducing a preset misjudgment checking algorithm, the association parameters are calculated, so that the association of the suspicious keywords and the word types is checked. Compared with the prior art, the method and the device avoid the risk of carrying out error filtering on the keywords due to generalization of the machine learning model. The accuracy of determining the word types of the keywords is improved, and the accuracy of pushing the final message is further improved. Therefore, the invention achieves the aim of improving the message pushing accuracy.
Optionally, calculating, by using a preset false positive verification algorithm, an association parameter of the suspicious keyword according to a word type of the suspicious keyword, including:
and determining word frequency and inverse document frequency of the suspicious keywords according to the word types of the suspicious keywords.
And carrying out product operation on the word frequency and the inverse document frequency by using a preset false judgment checking algorithm, and determining an operation result as an associated parameter of the suspicious keyword.
Optionally, determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword includes:
and determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group.
The number of occurrences of the suspicious keyword in the first numerical value is determined as a second numerical value.
And dividing the second numerical value by the quotient of the first numerical value by using a preset false judgment checking algorithm, and determining the quotient as the word frequency of the suspicious keyword.
Optionally, determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword, further includes:
and determining the total number of suspicious keywords in the suspicious keyword group as a third numerical value.
By the formula:
IDF t ＝lg(D n /N t )，
calculating the inverse document frequency IDF of the suspicious keyword t t Wherein D is n Is a third value, N t Is a second value, and n is the number of suspicious keywords in the suspicious keyword group.
In the practical application scenario, the above embodiments for determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword are various, and an exemplary embodiment is provided herein:
the current suspicious keyword group is set to comprise 10 suspicious keywords. These keywords coexist in 3 word types. One of the word types is "finance". The number of keywords matching the word type is 3, namely "oil price", "grain price" and "oil price". It should be noted that, since the sources of the initial keywords may be various, the "oil price" may be two keywords extracted from the public number name subscribed by the user and the browser search data, respectively.
Then, for the keyword "cereal price", since there are 3 suspicious keywords consistent with the word type of "cereal price" in the current suspicious keyword group, the first value is 3. Since "grain price" occurs only 1 time, the second value is 1. Then the formula is passed:
TF＝N t /N n ，
the word frequency (TermFrequency, TF) of the "grain price" is 1/3.
And because the current suspicious keyword group comprises 10 suspicious keywords, the third numerical value is 10. The "grain price" pseudo-document frequency is found to be 1 by the above formula.
Optionally, calculating the matching degree of the initial keyword based on the preset word data table includes:
and taking the initial keyword and each word data in the preset word data table as input, inputting the input into a preset character string matching algorithm, and obtaining the matching degree output by the preset character string matching algorithm, wherein the matching degree characterizes the association degree of the initial keyword and each word data in the preset word data table.
It should be noted that, in an actual application scenario, the specific types of The preset string matching Algorithm may be various, for example, the Knuth-Morris-Pratt Algorithm (KMP) Algorithm. The specific construction process of the algorithm is not excessively limited and repeated.
Optionally, the message pushing request further includes terminal usage data of the mobile terminal, and pushing the message to be pushed to the target mobile terminal includes:
and reading the use period in the terminal use data.
And judging whether the pushing moment of the message to be pushed is within the using period, and if so, pushing the message to be pushed to the target mobile terminal.
The terminal usage data may include a browser usage period, terminal location data, and the like, in addition to the usage period. The invention does not limit the specific type of the terminal usage data too much.
Optionally, in an optional embodiment of the present invention, the type in the message to be pushed belongs to personal privacy data of the user. Therefore, in order to avoid the problem of privacy disclosure caused by theft of the message to be pushed, the pushing of the message to be pushed can be performed through the blockchain. The specific implementation modes of the method can be as follows:
and compressing the message to be pushed into a data block, and performing uplink operation on the data block. And sets a corresponding transmission trigger condition. When the triggering requirement of the triggering condition is met at the current moment, the block chain link point storing the data block sends the data block to an application plug-in deployed in the target mobile terminal through a block chain.
Corresponding to the above method embodiment, the present invention also provides a message push server, as shown in fig. 2, where the message push server includes:
the data receiving module 201 is configured to receive a message push request sent by a target mobile terminal, where the message push request includes an initial keyword group.
The matching degree calculating module 202 is configured to, for each initial keyword in the initial keyword group: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table.
The first data filtering module 203 filters each initial keyword based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold.
The misjudgment checking module 204 is configured to, for each suspicious keyword: and calculating the association parameters of the suspicious keywords according to the word types of the suspicious keywords by using a preset misjudgment checking algorithm.
The second data filtering module 205 is configured to determine each suspicious keyword with the association parameter greater than a second preset threshold as an alternative keyword, where the first preset threshold and the second preset threshold are different.
The message pushing module 206 is configured to determine a message to be pushed according to the keyword group and each candidate keyword, and push the message to be pushed to the target mobile terminal.
Optionally, the false positive verification module 204 is configured to:
and determining word frequency and inverse document frequency of the suspicious keywords according to the word types of the suspicious keywords.
And carrying out product operation on the word frequency and the inverse document frequency by using a preset false judgment checking algorithm, and determining an operation result as an associated parameter of the suspicious keyword.
Optionally, the misjudgment checking module 204 is configured to, when determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword:
and determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group.
The number of occurrences of the suspicious keyword in the first numerical value is determined as a second numerical value.
And dividing the second numerical value by the quotient of the first numerical value by using a preset false judgment checking algorithm, and determining the quotient as the word frequency of the suspicious keyword.
Optionally, the misjudgment checking module 204 is further configured to, when determining the word frequency and the inverse document frequency of the suspicious keyword according to the word type of the suspicious keyword:
and determining the total number of suspicious keywords in the suspicious keyword group as a third numerical value.
By the formula:
IDF t ＝lg(D n /N t )，
calculating the inverse document frequency IDF of the suspicious keyword t t Wherein D is n Is a third value, N t Is a second value, and n is the number of suspicious keywords in the suspicious keyword group.
Optionally, the matching degree calculating module 202 is configured to:
and taking the initial keyword and each word data in the preset word data table as input, inputting the input into a preset character string matching algorithm, and obtaining the matching degree output by the preset character string matching algorithm, wherein the matching degree characterizes the association degree of the initial keyword and each word data in the preset word data table.
Optionally, the message pushing module 206 is configured to:
and reading the use period in the terminal use data.
And judging whether the pushing moment of the message to be pushed is within the using period, and if so, pushing the message to be pushed to the target mobile terminal.
The embodiment of the invention also provides a message pushing server, as shown in fig. 3, which comprises:
a processor 301;
a memory 302 for storing instructions executable by the processor 301.
Wherein the processor 301 is configured to execute instructions to implement a message pushing method as any one of the above.
Embodiments of the present invention also provide a computer readable storage medium, which when executed by a processor of a message push server, enables the message push server to perform a message push method as described in any one of the above.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (7)
1. A message pushing method, characterized in that the message pushing method comprises:
receiving a message pushing request sent by a target mobile terminal, wherein the message pushing request comprises an initial keyword group;
for each initial keyword in the initial keyword group: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table;
screening all the initial keywords based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold value;
for each suspicious keyword: determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group; determining the number of occurrences of the suspicious keyword in the first numerical value as a second numerical value; determining the word frequency of the suspicious keyword by utilizing a preset misjudgment checking algorithm and dividing the second numerical value by the quotient of the first numerical value; performing product operation on the word frequency and the inverse document frequency by using the preset misjudgment checking algorithm, and determining an operation result as an associated parameter of the suspicious keyword;
determining each suspicious keyword with the association parameter larger than a second preset threshold as an alternative keyword, wherein the first preset threshold and the second preset threshold are different;
and determining a message to be pushed according to the keyword group and each alternative keyword, and pushing the message to be pushed to the target mobile terminal.
2. The message pushing method according to claim 1, wherein the generation process of the inverse document frequency includes:
determining the total number of suspicious keywords in the suspicious keyword group as a third numerical value;
by the formula:
IDF t ＝lg(D n /N t )，
calculating the inverse document frequency IDF of the suspicious keyword t t Wherein the D n Is the third value, the N t And the second value is the second value, and the n is the number of suspicious keywords in the suspicious keyword group.
3. The message pushing method according to claim 1, wherein the calculating the matching degree of the initial keyword based on the preset word data table includes:
and taking the initial keyword and each word data in the preset word data table as inputs, inputting the inputs into a preset character string matching algorithm, and obtaining the matching degree output by the preset character string matching algorithm, wherein the matching degree characterizes the association degree of the initial keyword and each word data in the preset word data table.
4. The message pushing method according to claim 1, wherein the message pushing request further includes terminal usage data of the mobile terminal, and the pushing the message to be pushed to the target mobile terminal includes:
reading a use period in the terminal use data;
judging whether the pushing moment of the message to be pushed is within the using period, if so, pushing the message to be pushed to the target mobile terminal.
5. A message push server, the message push server comprising:
the data receiving module is used for receiving a message pushing request sent by a target mobile terminal, wherein the message pushing request comprises an initial keyword group;
the matching degree calculation module is used for calculating the initial keywords in the initial keyword groups: determining the word type of the initial keyword, calling a preset word data table corresponding to the word type, and calculating the matching degree of the initial keyword based on the preset word data table;
the first data screening module screens all the initial keywords based on the matching degree to obtain a keyword group and a suspicious keyword group, wherein the suspicious keyword group comprises a plurality of suspicious keywords, and the matching degree of the suspicious keywords is smaller than a first preset threshold value;
the misjudgment checking module is used for checking each suspicious keyword: determining a first numerical value according to the word type of the suspicious keyword, wherein the first numerical value is the number of suspicious keywords which are consistent with the word type of the suspicious keyword in the suspicious keyword group; determining the number of occurrences of the suspicious keyword in the first numerical value as a second numerical value; determining the word frequency of the suspicious keyword by utilizing a preset misjudgment checking algorithm and dividing the second numerical value by the quotient of the first numerical value; performing product operation on the word frequency and the inverse document frequency by using the preset misjudgment checking algorithm, and determining an operation result as an associated parameter of the suspicious keyword;
the second data screening module is used for determining each suspicious keyword with the association parameter being larger than a second preset threshold as an alternative keyword, wherein the first preset threshold and the second preset threshold are different;
and the message pushing module is used for determining a message to be pushed according to the keyword group and each alternative keyword and pushing the message to be pushed to the target mobile terminal.
6. A message push server, the message push server comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the message pushing method of any of claims 1 to 4.
7. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of a message push server, enable the message push server to perform the message push method of any of claims 1 to 4.
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