CN115345670A - Advertisement delivery method and device, electronic equipment and storage medium - Google Patents
Advertisement delivery method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides an advertisement putting method, an advertisement putting device, electronic equipment and a storage medium, wherein the method comprises the steps of obtaining page operation information of a user in a historical time period and storing basic data corresponding to the page operation information; constructing a user model based on the page operation information, wherein objects of the user model comprise a user identification number, a user score and an advertisement putting type; and calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user to be delivered. The method and the device adjust the advertisement putting strategy according to the behavior data of the user so as to realize accurate advertisement putting for the user and improve the advertisement conversion rate of the user.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to an advertisement delivery method and apparatus, an electronic device, and a storage medium.
Background
The internet brings great convenience to people, actively searches and obtains some information, and brings certain economic benefit to advertisement display of a user side to a certain extent. The advertisement delivery forms on the internet generally include web advertisements, pop-up advertisements, message pushing and the like, and because the existing advertisement delivery is more concentrated on advertisement delivery at the same latitude where users are not distinguished, the advertisement delivery is displayed to a user side and cannot be effectively converted. Some even have adverse effects, which lead to the boredom of users, even cause complaints or delete App for the complaints, and the loss of users is serious. Therefore, whether effective and intelligent advertisement delivery can be performed becomes a first problem facing products and technologies.
Disclosure of Invention
The invention provides an advertisement delivery method, an advertisement delivery device, electronic equipment and a storage medium, which are used for solving the problem that effective conversion cannot be realized by blindly delivering advertisements in the prior art.
In a first aspect, the present invention provides an advertisement delivery method, where the method includes:
acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information;
constructing a user model based on the page operation information, wherein objects of the user model comprise a user identification number, a user score and an advertisement putting type;
and calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user to be delivered.
In an embodiment of the present invention, the step of obtaining page operation information of a user in a historical time period and storing basic data corresponding to the page operation information includes:
acquiring page operation information of a user in a historical time period, wherein the page operation information comprises a user identification number, a browsing page identification character string, a searching character string and a page stay time;
and storing the basic data corresponding to the page operation information by taking the user identification number as an index.
In an embodiment of the present invention, before the building the user model based on the page operation information, the method further includes:
acquiring the basic data, and preprocessing the basic data to clean invalid and repeated data;
determining advertisements to be launched, classifying the advertisements to be launched, and coding each type of advertisements to obtain the types and the numbers of the advertisements to be launched.
In an embodiment of the present invention, the step of calculating the basic data according to the calculation policy of the user model to obtain the user score corresponding to each advertisement type includes:
comparing the browsing page identification character string with each type of the advertisement to be delivered;
if the matching is complete, setting the value as a first numerical value and taking the first numerical value as a first matching degree;
if fuzzy matching is carried out and the redundant characters in the matching are within a first preset numerical value, setting the redundant characters as a second numerical value and taking the second numerical value as a first matching degree; if fuzzy matching is carried out and the redundant characters in the matching exceed the first preset numerical value, setting the redundant characters as a third numerical value and taking the third numerical value as a first matching degree, wherein the first numerical value is larger than the second numerical value, and the second numerical value is larger than the third numerical value;
and multiplying the first matching degree by a first preset basic score and then multiplying by the page staying time corresponding to the browsing page identification character string to obtain a first score corresponding to the type.
In an embodiment of the present invention, the step of calculating the basic data according to the calculation policy of the user model to obtain the user score corresponding to each advertisement type further includes:
comparing the search string with each type of advertisement to be delivered;
if the matching is complete, setting the fourth numerical value as a second matching degree;
if the fuzzy matching is carried out and the redundant characters in the matching are within a second preset numerical value, setting the redundant characters as a fifth numerical value and taking the fifth numerical value as a second matching degree; if fuzzy matching is carried out and the redundant characters in the matching exceed the second preset numerical value, setting the redundant characters as a sixth numerical value and taking the sixth numerical value as a second matching degree, wherein the fourth numerical value is larger than the fifth numerical value, and the fifth numerical value is larger than the sixth numerical value;
and multiplying the second matching degree by a second preset basic score to obtain a second score.
In an embodiment of the present invention, the step of calculating the basic data according to the calculation policy of the user model to obtain the user score corresponding to each advertisement type further includes:
multiplying the second score by a first weighted value to obtain a third score;
multiplying the second score by a second weighted value to obtain a fourth score, wherein the sum of the first weighted value and the second weighted value is equal to 1;
and adding the third score and the fourth score to obtain a fifth score, and taking the fifth score as the user score.
In an embodiment of the present invention, the step of taking the advertisement type corresponding to the highest score as the target advertisement of the user for placement includes:
calculating each type of the advertisement to be delivered according to the steps to obtain user scores corresponding to all advertisement types;
and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for putting.
In a second aspect, the present invention further provides an advertisement delivery device, including:
the information acquisition module is used for acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information;
the model building module is used for building a user model based on the page operation information, and objects of the user model comprise a user identification number, a user score and an advertisement putting type;
and the calculation module is used for calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as the target advertisement of the user to be delivered.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the advertisement delivery method according to the first aspect.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the advertisement delivery method as described in the first aspect above.
According to the advertisement delivery method, the device, the electronic equipment and the storage medium, the page operation information of the user in the historical time period is obtained, the basic data corresponding to the page operation information is analyzed and calculated through the constructed user model, and the advertisement type corresponding to the highest score in the calculated user scores is used as the target advertisement of the user to be delivered. The method and the device adjust the advertisement putting strategy according to the behavior data of the user so as to realize accurate advertisement putting for the user and improve the advertisement conversion rate of the user.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an advertisement delivery method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of calculating a user score according to an embodiment of the present invention;
FIG. 3 is a second flowchart illustrating a process of calculating a user score according to an embodiment of the present invention;
FIG. 4 is a third flowchart illustrating a process of calculating a user score according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an advertisement delivery device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be implemented in other sequences than those illustrated or described herein.
In order to solve the problem that effective conversion cannot be realized by blindly delivering advertisements in the prior art, the invention provides an advertisement delivery method, an advertisement delivery device, electronic equipment and a storage medium. The method and the device adjust the advertisement putting strategy according to the behavior data of the user so as to realize accurate advertisement putting for the user and improve the advertisement conversion rate of the user.
The advertisement delivery method, apparatus, electronic device and storage medium of the present invention are described below with reference to fig. 1 to 6.
Referring to fig. 1, fig. 1 is a schematic flow chart of an advertisement delivery method according to an embodiment of the present invention. A method of advertisement delivery, the method comprising:
and step 110, acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information.
Illustratively, a software development kit can be implanted on a page at a user terminal to acquire page operation information of the user. The page operation information includes, but is not limited to, information such as a user identification number (UserId), a browsing page identification string (UserMap), a search string (UserSearchKey), a page dwell time (UserPageTime), a user operation link, a user search habit, and the like.
Illustratively, basic data corresponding to the page operation information is saved by taking a user identification number (UserId) as an index so as to facilitate the subsequent calculation of the advertisement putting strategy.
And 120, constructing a user model based on the page operation information, wherein the objects of the user model comprise a user identification number, a user score and an advertisement putting type.
Illustratively, a user model is constructed, abstracting an object, the object of the user model including a user identification number (UserId), a user score (UserScore), and an advertisement placement type (UserAdType). Typical users are distinguished according to standardization by constructing a user model for advertising, and then behavior habits of different user groups can be reasonably controlled in the advertising process, and the advertising is carried out in a targeted manner, so that the user experience and the advertising conversion rate can be improved.
Illustratively, prior to building the user model, the method further comprises:
and step 111, acquiring the basic data, and preprocessing the basic data to clean invalid and repeated data.
Step 112, determining the advertisements to be delivered, classifying the advertisements to be delivered and coding each type of advertisements to obtain the types and numbers of the advertisements to be delivered.
For example, the advertisement to be delivered is coded according to six types of games, automobiles, beauty, emotion, medical care and health maintenance, and each type is coded, for example, the number corresponding to "game" is T01, the number corresponding to "automobile" is T02, the number corresponding to "beauty" is T03, the number corresponding to "emotion" is T04, the number corresponding to "medical care" is T05, and the number corresponding to "health maintenance" is T06. It should be noted that the present invention is not limited to these advertisement types and encoding methods, and the encoding can be performed according to the actual product conditions of the user.
And step 130, calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as the target advertisement of the user to be delivered.
Illustratively, the calculation strategy comprises the steps of calculating the matching degree between a user browsing page record and an advertisement type and calculating the matching degree between a user searching operation and the advertisement type, then carrying out weighted calculation on the matching degrees in the two aspects, finally obtaining a user score corresponding to each advertisement type, taking the advertisement type corresponding to the highest score in the user scores as the UserAdType of the user, and carrying out accurate advertisement putting on the user.
In conclusion, the advertisement delivery strategy is adjusted according to the behavior data of the user, so that the user can be accurately delivered with the advertisement, and the advertisement conversion rate of the user is improved.
The above steps are specifically described below.
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating a process of calculating a user score according to an embodiment of the present invention. In the step 130, the step of calculating the basic data according to the calculation strategy of the user model to obtain the user score corresponding to each advertisement type includes:
Illustratively, the browsing page identification character string (UserMap) is compared with each type of the advertisement to be delivered in step 112, for example, if the browsing page identification character string UserMap of the user a is "health maintenance", then "health maintenance" is compared with each type of the advertisement to be delivered, and the corresponding advertisement type is health maintenance (i.e., number T06).
In step 132, if the matching is complete, the first value is set as the first matching degree.
For example, if the user a views the page identification string (UserMap) as "health", the user a considers that the user a is completely matched with the "health" type of the advertisement to be delivered, and sets the user a as a first numerical value (for example, the first numerical value is 1), and uses the first numerical value as a first matching degree between the viewed page identification string and the health type of the advertisement to be delivered.
For example, if the user B browses the page identification character string (UserMap) to be "health maintenance diet", it is considered that the "health maintenance" type of the advertisement to be delivered is fuzzy matching, and if the redundant character "diet" is within the character string of the first preset numerical value (for example, the first preset numerical value is 3), it is set to be a second numerical value (for example, the second numerical value is 0.8), and the second numerical value is used as a first matching degree of the browsed page identification character string and the health maintenance type of the advertisement to be delivered.
In step 134, if the fuzzy matching is performed and the redundant characters in the matching exceed the first preset value, the third value is set and used as the first matching degree.
Illustratively, the first value is greater than the second value, which is greater than the third value.
For example, if the user C browses the page identification character string (UserMap) is "health preserving knowledge encyclopedia", it is considered that the "health preserving" type of the advertisement to be delivered is fuzzy matching, and if the redundant character "knowledge encyclopedia" exceeds the character string with the first preset numerical value (for example, the first preset numerical value is 3), it is set to the third numerical value (for example, the third numerical value is 0.4), and the third numerical value is used as the first matching degree of the browsed page identification character string and the health preserving type of the advertisement to be delivered.
And step 135, multiplying the first matching degree by a first preset basic score and then multiplying by the page stay time corresponding to the browsing page identification character string to obtain a first score corresponding to the type.
For example, if the user a browses the page identification string (UserMap) is "health preserving", the first matching degree is 1, the first preset base score is 100 minutes, and the page staying time corresponding to the user a browses the page identification string is 9 seconds, then the first score = the first matching degree × the first preset base score = the page staying time =1 × 100 × 9=90 minutes. Note that the page stay time is preferably in units of seconds.
It should be noted that the first numerical value, the first preset numerical value, the second numerical value, and the third numerical value according to the present invention may be set according to practical situations, and are not limited to the above examples.
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating a process of calculating a user score according to an embodiment of the present invention. In the step 130, the step of calculating the basic data according to the calculation policy of the user model to obtain the user score corresponding to each advertisement type further includes:
Illustratively, the search string (UserSearchKey) is compared with each type of the advertisement to be delivered in step 112, for example, if the search string (UserSearchKey) of the user a is "health maintenance", then "health maintenance" is compared with each type of the advertisement to be delivered, and the corresponding advertisement type is health maintenance (i.e., number T06).
And 137, if the matching is complete, setting the fourth value as the second matching degree.
For example, if the search string (usersearchckey) of the user a is "health preserving", the search string is considered to be completely matched with the "health preserving" type of the advertisement to be delivered, the search string is set to be a fourth numerical value (for example, the fourth numerical value is 1), and the fourth numerical value is used as a second matching degree of the search string and the health preserving type of the advertisement to be delivered.
In step 138, if the fuzzy matching is performed and the redundant characters in the matching are within the second predetermined value, the fuzzy matching is performed, and the fifth value is set as the second matching degree.
For example, if the search string (usersearchckey) of the user B is "health preserving diet", it is considered that the "health preserving" type of the advertisement to be delivered is fuzzy matching, and if the redundant character "diet" is within the string of the second preset numerical value (for example, the second preset numerical value is 3), it is set as the fifth numerical value (for example, the fifth numerical value is 0.8), and the fifth numerical value is used as the second matching degree between the search string and the health preserving type of the advertisement to be delivered.
And step 139, if the fuzzy matching is performed and the redundant characters in the matching exceed the second preset numerical value, setting the redundant characters as a sixth numerical value and taking the sixth numerical value as a second matching degree.
Illustratively, the fourth value is greater than the fifth value, which is greater than the sixth value.
For example, if the search string (UserSearchKey) of the user C is "health preserving knowledge encyclopedia", it is considered that the "health preserving" type of the advertisement to be delivered is fuzzy matching, and if the redundant character "knowledge encyclopedia" exceeds a second preset numerical value (for example, the second preset numerical value is 3), the redundant character is set to a sixth numerical value (for example, the sixth numerical value is 0.4), and the sixth numerical value is used as a second matching degree between the search string and the health preserving type of the advertisement to be delivered.
And 140, multiplying the second matching degree by a second preset basic score to obtain a second score.
For example, assuming that the search string (UserSearchKey) of the user a is "health food", the second matching degree thereof is 0.8, and the second preset base score is 100 points, the second score = the second matching degree ×, the second preset base score =0.8 × 100=80 points.
It should be noted that the fourth numerical value, the second preset numerical value, the fifth numerical value, and the sixth numerical value according to the present invention can be set according to practical applications, and are not limited to the above examples.
Referring to fig. 4, fig. 4 is a third schematic flowchart illustrating a process of calculating a user score according to an embodiment of the present invention. In the step 130, the step of calculating the basic data according to the calculation policy of the user model to obtain the user score corresponding to each advertisement type further includes:
For example, assuming that the first score of the user a is 90 points and the first weighting value is set to 0.6 in the above step 135, the third score =90 × 0.6=54 points.
For example, assuming that the second score of the user a is 80 points and the second weighting value is set to 0.4 in the above step 140, the fourth score =80 × 0.4=32 points. The first weight + the second weight =0.6+0.4=1.
And 143, adding the third score and the fourth score to obtain a fifth score, and taking the fifth score as the user score.
For example, the third score and the fourth score of user a in the above steps 141 and 142 are added, and assuming that the third score is 54 points and the fourth score is 32 points, the fifth score =54+32=86 points, i.e., the user score (UserScore) =86 points. It should be noted that the user score herein refers to a user score of a certain advertisement type, for example, 86 scores herein refers to a user score corresponding to a "health" advertisement type.
It should be noted that, the first weighting value and the second weighting value of the present invention may be set according to practical situations, and are not limited to the above examples.
In some embodiments of the present invention, in the step 130, the step of taking the advertisement type corresponding to the highest score as the target advertisement of the user for placement includes:
and 144, calculating each type of the advertisement to be delivered according to the steps 131 to 143 to obtain user scores corresponding to all advertisement types.
Finally, user scores corresponding to all advertisement types of the user A are obtained, for example, the user score of the game type is 76 points, the user score of the automobile type is 83 points, the user score of the beauty type is 66 points, the user score of the emotion type is 72 points, the user score of the medical type is 85 points, and the user score of the health maintenance type is 92 points. As can be seen, for user a, the "health" type of user scores the highest.
And 145, taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for delivery.
For example, assuming that the advertisement type corresponding to the highest score in the user scores is "health preserving", the "health preserving" advertisement type is used as the advertisement placement type (UserAdType) of the user a. That is to say, the advertisement of the health preserving type is obtained by analyzing according to the behavior data of the user a, so that the user a is interested in the advertisement of the health preserving type, and therefore, the advertisement of the health preserving type can be accurately delivered to the user a.
In summary, the behavior data of the user is deeply analyzed, reasonable calculation is carried out according to the original basic data and the accumulated empirical weight values, the attention core of the user can be accurately identified through the technology, and the user is intelligently delivered according to the advertisement strategy on the basis, so that the essential requirements of the user can be grasped, and the conversion rate of the advertisement display of the user side is maximally improved.
In addition, the user model can continuously optimize the data of the user, real and effective online data can be continuously provided for the service end through the data information accumulated by the user and the calculation strategy of the user model, and the advertisement putting strategy type of the user end can be timely adjusted by the calculation strategy according to the processing of the data, so that the intelligent learning capability is achieved. The calculation strategy is matched with similar data carrying containers such as advertisement positions and the like, and the advertisement conversion rate of the user can be improved to the maximum extent. The calculation core data of the strategy is the original basic data of the user, the reliability is high, the guidance is strong, and the effectiveness of the strategy is better.
The following describes the advertisement delivery device provided by the present invention, and the advertisement delivery device described below and the advertisement delivery method described above may be referred to in correspondence with each other.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an advertisement delivery device according to an embodiment of the present invention. An advertisement delivery apparatus 500 includes an information acquisition module 510, a model construction module 520, and a calculation module 530.
Illustratively, the information obtaining module 510 is configured to obtain page operation information of a user in a historical time period and save basic data corresponding to the page operation information.
Illustratively, the model building module 520 is configured to build a user model based on the page operation information, wherein objects of the user model include a user identification number, a user score and an advertisement putting type.
Illustratively, the calculation module 530 is configured to calculate the basic data according to a calculation policy of the user model to obtain a user score corresponding to each advertisement type, and take an advertisement type corresponding to a highest score value in the user scores as a target advertisement of the user for delivery.
Illustratively, the information obtaining module 510 is further configured to:
acquiring page operation information of a user in a historical time period, wherein the page operation information comprises a user identification number, a browsing page identification character string, a searching character string and page staying time;
and storing the basic data corresponding to the page operation information by taking the user identification number as an index.
Illustratively, the advertisement delivery apparatus further comprises a preprocessing module, which is configured to:
acquiring the basic data, and preprocessing the basic data to clean invalid and repeated data;
determining the advertisements to be delivered, classifying the advertisements to be delivered and coding each class of advertisements to obtain the types and the numbers of the advertisements to be delivered.
Illustratively, the calculation module 530 is further configured to:
comparing the browsing page identification character string with each type of the advertisement to be delivered;
if the matching is complete, setting the first numerical value as a first matching degree;
if fuzzy matching is carried out and the redundant characters in the matching are within a first preset numerical value, setting the redundant characters as a second numerical value and taking the second numerical value as a first matching degree; if fuzzy matching is carried out and the redundant characters in matching exceed the first preset numerical value, setting the third numerical value as a first matching degree, wherein the first numerical value is larger than the second numerical value, and the second numerical value is larger than the third numerical value;
and multiplying the first matching degree by a first preset basic score and then multiplying by the page stay time corresponding to the browsing page identification character string to obtain a first score corresponding to the type.
Illustratively, the calculation module 530 is further configured to:
comparing the search string with each type of advertisement to be delivered;
if the matching is complete, setting the value as a fourth numerical value and taking the fourth numerical value as a second matching degree;
if the fuzzy matching is carried out and the redundant characters in the matching are within a second preset numerical value, setting the redundant characters as a fifth numerical value and taking the fifth numerical value as a second matching degree; if fuzzy matching is carried out and the redundant characters in the matching exceed the second preset numerical value, setting the redundant characters as a sixth numerical value and taking the sixth numerical value as a second matching degree, wherein the fourth numerical value is larger than the fifth numerical value, and the fifth numerical value is larger than the sixth numerical value;
and multiplying the second matching degree by a second preset basic score to obtain a second score.
Illustratively, the calculation module 530 is further configured to:
multiplying the second score by a first weighted value to obtain a third score;
multiplying the second score by a second weighted value to obtain a fourth score, wherein the sum of the first weighted value and the second weighted value is equal to 1;
and adding the third score and the fourth score to obtain a fifth score, and taking the fifth score as the user score.
Illustratively, the calculation module 530 is further configured to:
calculating each type of the advertisement to be delivered according to the steps to obtain user scores corresponding to all advertisement types;
and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for putting.
It should be noted that, the advertisement delivery device provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in this embodiment are omitted here.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a Processor (Processor) 610, a communication Interface (Communications Interface) 620, a Memory (Memory) 630 and a communication bus 640, wherein the Processor 610, the communication Interface 620 and the Memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform the method of advertisement delivery, the method comprising:
acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information;
constructing a user model based on the page operation information, wherein objects of the user model comprise a user identification number, a user score and an advertisement putting type;
and calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user to be delivered.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the advertisement delivery method provided by the above methods.
In still another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the advertisement delivery method provided in the foregoing.
In the electronic device, the computer program product, and the processor-readable storage medium provided in the embodiments of the present invention, the computer program stored on the computer program enables the processor to implement all the method steps implemented by the foregoing method embodiments, and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiments in this embodiment are not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple 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. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An advertisement delivery method, the method comprising:
acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information;
constructing a user model based on the page operation information, wherein objects of the user model comprise a user identification number, a user score and an advertisement putting type;
and calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for delivery.
2. The advertisement delivery method according to claim 1, wherein the step of acquiring page operation information of the user in a historical time period and storing basic data corresponding to the page operation information comprises:
acquiring page operation information of a user in a historical time period, wherein the page operation information comprises a user identification number, a browsing page identification character string, a searching character string and a page stay time;
and storing the basic data corresponding to the page operation information by taking the user identification number as an index.
3. The advertisement delivery method according to claim 2, wherein before the building a user model based on the page operation information, the method further comprises:
acquiring the basic data, and preprocessing the basic data to clean invalid and repeated data;
determining advertisements to be launched, classifying the advertisements to be launched, and coding each type of advertisements to obtain the types and the numbers of the advertisements to be launched.
4. The method of claim 1, wherein the step of calculating the basic data according to the calculation strategy of the user model to obtain the user score corresponding to each advertisement type comprises:
comparing the browsing page identification character string with each type of the advertisement to be delivered;
if the matching is complete, setting the first numerical value as a first matching degree;
if the fuzzy matching is carried out and the redundant characters in the matching are within a first preset numerical value, setting the second numerical value as a second numerical value and taking the second numerical value as a first matching degree; if fuzzy matching is carried out and the redundant characters in matching exceed the first preset numerical value, setting the third numerical value as a first matching degree, wherein the first numerical value is larger than the second numerical value, and the second numerical value is larger than the third numerical value;
and multiplying the first matching degree by a first preset basic score and then multiplying by the page staying time corresponding to the browsing page identification character string to obtain a first score corresponding to the type.
5. The method of claim 4, wherein the step of calculating the basic data according to the calculation strategy of the user model to obtain the user score corresponding to each advertisement type further comprises:
comparing the search string with each type of advertisement to be delivered;
if the matching is complete, setting the fourth numerical value as a second matching degree;
if the fuzzy matching is carried out and the redundant characters in the matching are within a second preset numerical value, setting the fuzzy matching as a fifth numerical value and taking the fifth numerical value as a second matching degree; if fuzzy matching is carried out and the redundant characters in the matching exceed the second preset numerical value, setting the redundant characters as a sixth numerical value and taking the sixth numerical value as a second matching degree, wherein the fourth numerical value is larger than the fifth numerical value, and the fifth numerical value is larger than the sixth numerical value;
and multiplying the second matching degree by a second preset basic score to obtain a second score.
6. The method of claim 5, wherein the step of calculating the basic data according to the calculation strategy of the user model to obtain the user score corresponding to each advertisement type further comprises:
multiplying the second score by a first weighted value to obtain a third score;
multiplying the second score by a second weighted value to obtain a fourth score, wherein the sum of the first weighted value and the second weighted value is equal to 1;
and adding the third score and the fourth score to obtain a fifth score, and taking the fifth score as the user score.
7. The method of claim 6, wherein the step of selecting the advertisement type corresponding to the highest score as the target advertisement for the user comprises:
calculating each type of the advertisement to be delivered according to the steps of the claims 4 to 6 to obtain user scores corresponding to all advertisement types;
and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for putting.
8. An advertising device, the device comprising:
the information acquisition module is used for acquiring page operation information of a user in a historical time period and storing basic data corresponding to the page operation information;
the model building module is used for building a user model based on the page operation information, and objects of the user model comprise a user identification number, a user score and an advertisement putting type;
and the calculation module is used for calculating the basic data according to the calculation strategy of the user model to obtain a user score corresponding to each advertisement type, and taking the advertisement type corresponding to the highest score in the user scores as a target advertisement of the user for delivery.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and run on the processor, wherein the processor when executing the program implements the steps of the advertisement delivery method according to any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the advertisement delivery method according to any one of claims 1 to 7.
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| CN202211006003.7A CN115345670A (en) | 2022-08-22 | 2022-08-22 | Advertisement delivery method and device, electronic equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308538A (en) * | 2023-03-01 | 2023-06-23 | 深圳市明源云科技有限公司 | Method for processing release of message, electronic equipment and readable storage medium |
| CN116402553A (en) * | 2023-06-07 | 2023-07-07 | 江西时刻互动科技股份有限公司 | Advertisement effect evaluation method, device and readable storage medium |
| CN118469646A (en) * | 2024-07-15 | 2024-08-09 | 广州手拉手互联网股份有限公司 | Advertisement putting optimization method and system based on big data |
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2022
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308538A (en) * | 2023-03-01 | 2023-06-23 | 深圳市明源云科技有限公司 | Method for processing release of message, electronic equipment and readable storage medium |
| CN116308538B (en) * | 2023-03-01 | 2024-03-15 | 深圳市明源云科技有限公司 | Method for processing release of message, electronic equipment and readable storage medium |
| CN116402553A (en) * | 2023-06-07 | 2023-07-07 | 江西时刻互动科技股份有限公司 | Advertisement effect evaluation method, device and readable storage medium |
| CN116402553B (en) * | 2023-06-07 | 2023-08-18 | 江西时刻互动科技股份有限公司 | Advertisement effect evaluation method, device and readable storage medium |
| CN118469646A (en) * | 2024-07-15 | 2024-08-09 | 广州手拉手互联网股份有限公司 | Advertisement putting optimization method and system based on big data |
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