CN109271604A - A kind of layout method, apparatus and computer equipment - Google Patents
A kind of layout method, apparatus and computer equipment Download PDFInfo
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Abstract
The invention discloses a kind of layout method, apparatus and computer equipments, are applied to internet area.This method comprises: the element characteristic that element characteristic and the interaction of more than two ad elements based on ad elements are constituted, establishes initial layout model, wherein the ad elements being laid out in multiple layout regions constitute banner jointly；Using full size advertisement formwork sample training initial layout model, banner placement model is obtained；Banner placement scheme is generated using layout model.The present invention solves the technical issues of layout design low efficiency.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an advertisement layout method, an advertisement layout device and computer equipment.
Background
Banner advertisements (Banner ads.) were the earliest forms of web advertising and are currently the most common form of advertising. When a user clicks on a banner advertisement, it is typically possible to link to the advertiser's web page. While the design of banner advertisements requires layout and color matching. Layout design is the first step in the layout design, and greatly influences the quality of banner advertisements.
At present, the layout design of banner advertisements is mainly completed by manually utilizing design software such as PS (packet switched) and the like, so that the efficiency is low, time is consumed, and the advertisement putting requirement cannot be met.
Disclosure of Invention
The embodiment of the invention provides an advertisement layout method, an advertisement layout device and computer equipment, and aims to solve the technical problem that the layout design efficiency of banner advertisements in the prior art is low.
In a first aspect, an embodiment of the present invention provides an advertisement layout method, including:
establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
training the initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model;
and generating a banner advertisement layout scheme by using the advertisement layout model.
Optionally, the establishing an initial layout model based on the element features of the advertisement elements and the element features formed by interaction of more than two advertisement elements includes:
abstracting each advertisement element for layout in the advertisement layout area into a corresponding rectangular area;
and performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
Optionally, the training of the initial layout model by using the original-size advertisement template sample to obtain a banner advertisement layout model includes:
constructing an advertisement layout metric function for the initial layout model;
and training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
Optionally, the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality.
Optionally, the training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout metric function is minimum to obtain the banner advertisement layout model includes:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
Optionally, the determining whether the training termination condition is reached includes:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
Optionally, the step of generating the optimal layout scheme through the optimization algorithm includes:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
Optionally, the simulated annealing algorithm includes a plurality of annealing operators associated with the advertisement elements; in the step of generating a preset number of advertisement layout proposals by performing one or more annealing operations based on the simulated annealing algorithm, each advertisement layout proposal is generated by:
selecting one annealing operator from the plurality of annealing operators related to the advertisement elements aiming at the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times;
and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
Optionally, after the generating a banner advertisement layout scheme by using the advertisement layout model, the method further includes:
carrying out variable size processing on the original size advertisement template sample to generate a new size advertisement template sample;
training the initial layout model based on the original-size advertisement template sample and the new-size advertisement template sample together to train a variable-size layout model;
and generating a variable-size advertisement layout scheme by using the variable-size layout model.
In a second aspect, an embodiment of the present invention provides an advertisement layout apparatus, including:
the modeling unit is used for establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
the first model training unit is used for training the initial layout model by utilizing an original-size advertisement template sample to obtain a banner advertisement layout model;
and the first advertisement layout unit is used for generating a banner advertisement layout scheme by utilizing the advertisement layout model.
Optionally, the modeling unit includes:
the abstraction subunit is used for abstracting each advertisement element for being laid out in the advertisement layout area into a corresponding rectangular area;
and the mathematical modeling subunit is used for performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
Optionally, the first model training unit includes:
a function construction subunit, configured to construct an advertisement layout metric function for the initial layout model;
and the sample training subunit is used for training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
Optionally, the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality.
Optionally, the sample training subunit is specifically configured to:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
Optionally, the sample training subunit is specifically configured to:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
Optionally, in the step of generating the optimal layout scheme through the optimization algorithm, the sample training subunit is specifically configured to:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
Optionally, the simulated annealing algorithm includes a plurality of annealing operators associated with the advertisement elements;
the sample training subunit is configured to, in the step of generating a preset number of advertisement layout proposals by performing one or more annealing operations based on a simulated annealing algorithm, generate each advertisement layout proposal by:
selecting one annealing operator from the plurality of annealing operators related to the advertisement elements aiming at the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times;
and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
Optionally, the apparatus further comprises:
the template processing unit is used for carrying out variable-size processing on the original-size advertisement template sample to generate a new-size advertisement template sample;
the second model training unit is used for training the initial layout model based on the original-size advertisement template sample and the new-size advertisement template sample so as to train a variable-size layout model;
and the second advertisement layout unit is used for generating a variable-size advertisement layout scheme by using the variable-size layout model.
In a third aspect, an embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method in any possible implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method according to any one of the possible implementations of the first aspect.
The technical scheme provided by the embodiment of the invention at least has the following technical effects or advantages:
the advertisement layout method, the device and the computer equipment provided by the embodiment of the invention jointly establish an initial layout model based on the element characteristics of the advertisement elements forming the banner advertisement and the element characteristics formed by interaction of more than two advertisement elements; training an initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model; a banner advertisement layout scheme is generated using the advertisement layout model. Therefore, the advertisement layout model for automatically generating the banner advertisement layout scheme is generated based on machine learning and is used for automatically designing the advertisement layout, and therefore, the layout design efficiency of banner advertisements can be improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow diagram of a method of advertisement placement in an embodiment of the invention;
FIG. 2 is a flow chart illustrating an advertisement placement method in another embodiment of the present invention
FIG. 3 is a schematic diagram showing the structure of an advertisement placement device according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
The technical problem that the layout design efficiency of banner advertisements in the prior art is low is solved. The embodiment of the invention provides an advertisement layout method, an advertisement layout device and computer equipment, and the general idea is as follows:
establishing an initial layout model based on the element characteristics of the advertisement elements forming the banner advertisement and the element characteristics formed by interaction of more than two advertisement elements; training an initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model; a banner advertisement layout scheme is generated using the advertisement layout model. Based on the technical scheme, the advertisement layout model for automatically generating the banner advertisement layout scheme is generated based on machine learning, so that the advertisement layout model is used for automatically designing the advertisement layout, and therefore the layout design efficiency of banner advertisements can be improved.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments. It is noted that the term "plurality" as used herein means "two or more" including "two" and "one" or more.
In a first aspect, an embodiment of the present invention provides an advertisement layout method, which is used to complete automatic advertisement layout design of a banner advertisement. Referring to fig. 1, an advertisement layout method provided by an embodiment of the present invention includes the following steps:
step S101, based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, an initial layout model is established together, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements.
In the embodiment of the present invention, the advertisement elements laid out in the layout area of the banner advertisement include a plurality or all of the following five advertisement elements: a picture of a good, a brand logo, an advertising theme, an advertising subtitle, a button label (e.g., instant purchase, knowledge of details, etc.). Of course, other advertising elements may also be supplemented for different business scenarios, such as: background patterns, decorations, and the like.
In an alternative embodiment, the initial layout model is established based on the element characteristics of the following five advertisement elements and the element characteristics formed by interaction of every two advertisement elements in the following five element characteristics: the key element characteristics of the commodity picture, the brand logo, the advertisement theme, the advertisement subtitle and the button label.
It should be noted that the element features in the embodiment of the present invention are related to the advertisement layout. Specifically, the feature characteristics of the advertisement element include the following three types:
the first method comprises the following steps: the distance between all advertisement elements and the border of the advertisement layout area.
And the second method comprises the following steps: the size of all advertising elements.
And the third is that: all the advertisement elements are characterized by comprising two interactive advertisement elements, specifically: relative positions of all advertising elements.
Of course, other features of elements related to the advertising layout may be added in the implementation.
In a specific embodiment, the initial layout model is established by: abstracting each advertisement element for layout in the advertisement layout area into a corresponding rectangular area, and performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain an initial layout model.
Specifically, each advertisement element is abstracted into a corresponding rectangular area, an initial layout model for the banner advertisement is established with the upper left corner of the advertisement layout area (canvas of the banner advertisement) as the origin of coordinates and the position coordinates and size information of the rectangular area of each advertisement element as the solution.
Given an example of an advertisement element j of layout scheme X, X (j, 0) is the abscissa of the advertisement element j, X (j, 1) is the ordinate of the advertisement element j, X (j, 2) is the width of the advertisement element j, and X (j, 3) is the height of the advertisement element j. Thus, the location coordinates of advertisement element j are: x (j, 0), X (j, 1); the size information of advertisement element j is: x (j, 2), X (j, 3). With this rule, it is possible to obtain position coordinates and size information of a rectangular area into which each advertisement element is abstracted in a given layout scheme X. Thus, taking a given layout scheme X in which five advertisement elements are laid out as an example, five advertisement element correspondences are abstracted into position coordinates and size information of five rectangular regions, and thus an initial layout model is established for the layout for the five advertisement elements.
And S102, training an initial layout model by using the original-size advertisement template sample to obtain a banner advertisement layout model.
In this embodiment of the present invention, if the layout quality metric function represents the advertisement layout quality, step S102 specifically includes:
and S1021, constructing an advertisement layout metric function aiming at the initial layout model, wherein the value of the advertisement layout metric function is a total energy value obtained by weighting and summing a plurality of energy values related to layout quality.
Several energy values are defined, and in the embodiment of the invention, at least three energy values are included as follows:
firstly, distance energy value: the distances of all ad elements from the boundaries of the ad layout area are characterized.
II, relative position energy value: the relative position of all ad elements (i.e., the position of the ad element compared to another ad element).
Thirdly, the energy value of the size: the size of all advertising elements.
Further, more energy values may be added according to different problem complexities, wherein each energy value includes one or more terms.
In the embodiment of the present invention, the constructed advertisement layout metric function is specifically as follows:
wherein E isiRepresenting the energy value of item i, wiRepresents the weight value corresponding to the ith energy value, αiControlling the smoothness of each energy value function, theta is a specific parameter of the advertisement layout metric function, namely: the parameters to be solved are needed. X represents a given layout scheme, for a given layout based on a sample of full-size advertising templatesThe energy values of the scheme can be calculated. However, the specific parameter θ for the given layout solution needs to be solved by an optimization algorithm. And when the solved characteristic parameter theta is optimal, the energy function value of the corresponding advertisement layout measurement function is minimum.
In the ad placement metric function, the characteristic parameter θ is split into two parts [ w, α ]]W as aboveiAnd αi。
Specifically, each advertisement element laid out in the advertisement layout area is divided into two types of advertisement elements: text elements related to text, image elements related to images. For example, with respect to a product picture, a brand logo, an advertisement subject, an advertisement subtitle, and a button tag, the product picture and the brand logo are image elements, and the advertisement subject, the advertisement subtitle, and the button tag are text elements.
Different types of advertisement elements correspond to different distance energy values, specifically, the distance energy values include: distance energy value of text elementAnd distance energy value of picture element
Distance energy value of text elementIs the average of the closest distances of all text elements in the ad element to the ad layout area boundary.
Wherein,representation and text elementsThe distance energy value of the element is,and the nearest distance between the ith text element and the boundary of the advertisement layout area is represented, n is the number of the text elements, and i belongs to (text) and represents the value of i in the number of the text elements.
Distance energy values of image elements can be obtained in the same mannerThe distance energy value of an image element is the average of the closest distances of all image elements in the ad element to the ad layout area boundary.
Relative position energy value EdistFor the sake of definition: mean of distances between all ad elements and the nearest ad element:
m is the number of all advertisement elements in the advertisement layout area, including: text element and image element, 1-S (min)j∈(all)(dij)；αdist) The distance between the ith advertising element and the nearest advertising element is represented, and i e (all) represents the value within the number of all advertising elements.
Specifically, the magnitude energy value includes: size energy value E of text elementtextSizeThe size energy value E of the picture elementgraphicSize
Energy value E in terms of size of text elementtextSizeIn terms, it can be defined as:
wherein,is defined as Is the height of the text element or elements,the number of rows, h is the height of the advertisement layout area.
Likewise, a size energy value for an image element may be calculated, wherein, when calculating the size energy value for an image element,is defined as Is the height of the picture element or elements,w and h are the width and height, respectively, of the advertising layout area.
In the above equation for calculating the energy values, if each energy value is calculated based on the mapping function S (x; α) ═ arctan (x α)/arctan (α), specifically:
when calculating the distance energy value:
when calculating the relative position energy value:
S(minj∈(all)(dij)；αdist)＝arctan(minj∈(all)(dij)αdist)/arctan(αdist)
when calculating the magnitude energy value:
the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality. Through the steps, each energy value can be obtained, and the total energy value can be obtained based on the weighted summation of the energy values. However, the optimal result of the specific parameter θ of the advertisement layout metric function is unknown, and the optimal result of the specific parameter θ corresponds to minimize the total energy value of the advertisement layout metric function, so that the corresponding real layout scheme X is optimal, i.e. the optimal real layout scheme XT。
And S1022, training an initial layout model by using the original-size advertisement template sample, and finishing training until the energy function value of the advertisement layout measurement function is minimum to obtain a banner advertisement layout model.
In step S1022, the layout quality metric function measures the position coordinates and size information of each advertisement element transformed by the original-size advertisement template sample, thereby measuring the advertisement layout quality.
Wherein, the smaller the energy function value of the layout quality metric function, the higher the representation advertisement layout quality. Therefore, the position coordinates and the size information of each advertisement element are transformed by using the original-size advertisement template sample, and the training is finished until the energy function value of the layout quality measurement function is minimum, so that the trained banner advertisement layout model is obtained.
In an optional embodiment, the process of training the initial layout model by using the original-size advertisement template sample until obtaining the banner advertisement layout model when the energy function value of the advertisement layout metric function is minimum includes the following steps a1-a 4:
step a1, initializing specific parameters of the advertisement layout metric function, and putting the original size advertisement template sample into the candidate solution.
Step a2, taking the solution which minimizes the total energy value of the advertisement layout measurement function in the candidate solutions as the current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution.
And a step a3, updating the specific parameters based on the gradient descent method and judging whether the training termination condition is reached.
Step a4, if the training termination condition is reached, outputting the current optimal solution as a banner advertisement layout model; otherwise, the candidate solution is updated based on the new solution, and the step a2 is returned based on the updated candidate solution and the updated specific parameter.
Specifically, the energy function value of the advertisement layout metric function is a total energy value E (X; theta) obtained by weighted summation of the energy values. In the specific implementation process, the real layout scheme X is optimal according to the specific parameter theta of the optimal result, and the total energy value E (X) corresponding to the optimal specific parameter thetaT(ii) a Theta) is minimum, XTRepresenting the optimal real layout solution. But the value of theta that minimizes the total energy value is not known at first.
In order to obtain a value of θ that minimizes the total energy value, an objective function is set in the embodiment of the present invention. And the advertisement layout is optimal according to the theta value with the minimum total energy value, wherein the objective function represents the difference between the total energy value of the optimal real layout scheme X and the total energy value corresponding to the optimal layout scheme solved by the optimization algorithm:
wherein, E (X)T(ii) a Theta) is the total energy value of the optimal real layout scheme X,as an initial value, λ is the regularization parameter, s determines which parameters need to be regularized. The optimal layout scheme obtained by the optimization algorithm is XS＝minXE (X; theta). Given layout scheme X corresponding to a value θ such that G (θ) is 0TAt this point, the training is completed to obtain the banner advertisement layout model.
Iteratively updating the value of the specific parameter θ based on a gradient descent algorithm to continuously decrease the value G (θ) of the objective function: until a value of θ required to make the objective function G (θ) 0 is obtained, we obtain: optimal results for a particular parameter.
Specifically, iteratively updating the value of the specific parameter θ based on the gradient descent algorithm specifically includes: derivation of the objective function:
and step S103, generating a banner advertisement layout scheme by using the advertisement layout model.
In an optional implementation mode, judging whether the current function value of the target function reaches a preset function value, and if so, determining that a training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through the optimization algorithm.
In the embodiment of the invention, the used optimization algorithm can be a simulated annealing algorithm, and the current optimal layout scheme is obtained by solving based on the simulated annealing algorithm.
The optimization algorithm used to solve the current optimal layout scheme may be a simulated annealing algorithm. In a specific embodiment. Specifically, the step of generating the optimal layout scheme based on the simulated annealing algorithm includes steps b 1-b 2:
and b1, executing more than one annealing operation based on the simulated annealing algorithm to generate a preset number of advertisement layout proposals.
The simulated annealing algorithm in the embodiment of the invention comprises one or more annealing operators related to the advertisement elements. Specifically, one or more of the following annealing operators related to the advertisement elements are included: changing the position of an advertising element, changing the size of an advertising element, swapping two advertising elements. In the specific implementation process, other annealing operators can be extended to the simulated annealing algorithm according to the service scenario, for example: the alignment between the ad elements is changed.
In the specific implementation process, the element positions are changed, specifically: by adding a gaussian distribution offset to the current position of the advertisement element. The change of element size is specifically: by adding a gaussian distribution offset at the current altitude.
In step b1: and respectively setting a proposal upper limit for each annealing operation, wherein when the number of the advertisement layout proposals generated based on the current annealing operation reaches the corresponding proposal upper limit, the current annealing operation is ended, the next annealing operation is carried out, and the process is circulated until each annealing operation is completed so as to generate the advertisement layout proposals with the preset number.
Specifically, each annealing operation includes a preset number of proposed generation operations. Each proposal generation operation corresponds to one annealing operator, and the probability of which annealing operator is adopted in each proposal generation operation can be equal or unequal. For example, the annealing operator may be selected based on roulette algorithms.
Each proposal generation operation generates a corresponding advertisement layout proposal. Selecting one annealing operator from a plurality of annealing operators related to advertisement elements according to the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times; and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
And generating one advertisement layout proposal in each proposal generating operation, so that the number of the advertisement layout proposals generated in the current annealing operation reaches the corresponding proposal upper limit based on the preset times of proposal generating operations. For example, if the upper limit of proposals set for the current annealing operation is p, p advertisement layout proposals are generated based on the proposal generation operation p times in the current annealing operation.
After step b1, a step b2 is then performed to determine an optimal placement solution from a preset number of advertisement placement proposals.
More specifically, the implementation process for generating the optimal layout scheme based on the simulated annealing algorithm includes the following steps:
step 1, initializing and setting a cooling process. Specifically, an initial solution is randomly generated as a candidate solution proposal and an initial annealing temperature. A preset number of advertisement layout proposals are put into the candidate solution.
And 2, selecting a solution which enables the energy value of the advertisement layout energy function to be minimum from the candidate solution proposals.
And 3, generating an advertisement layout proposal based on the currently selected annealing operator.
And 4, judging whether the generated advertisement layout proposal is acceptable, if so, executing the step s5, otherwise, executing the step s 6.
And 5, whether the total energy value of the new solution generated based on the candidate solution is lower than the total energy value corresponding to the candidate solution or not is judged, if yes, step 8 is executed, and if not, step 7 is executed.
And 6, judging whether the number of generated advertisement layout proposals reaches the upper limit of the proposals, if so, executing the step 9, otherwise, selecting the next annealing operator and returning to the step 3.
And 7, judging whether to accept bad solutions or not based on the Metropolis criterion, if so, executing the step 8, and otherwise, executing the step 9.
And 8, adding the new solution into the candidate solution to obtain an updated candidate solution and executing the step 8.
And 9, judging whether the optimization termination condition is reached, if so, executing the step 10, otherwise, cooling and returning to the step 2.
And step 10, outputting the solution with the minimum total energy value at the current time as an optimal layout scheme.
Further, an embodiment of the present invention further provides a technical solution for a variable-size advertisement layout, and referring to fig. 2, an implementation process of the variable-size advertisement layout includes the following steps:
s201, performing variable-size processing on the original-size advertisement template sample to generate a new-size advertisement template sample.
Specifically, a variable-size layout metric function is constructed for the initial layout model, and the variable-size layout metric function can be described as:
wherein, XpThe definitions of other parameters refer to the definitions in the layout metric function, which are not described herein for brevity of the description.
The size layout metric function includes an energy value describing an original condition of an advertisement element and also includes the same energy value as that in the advertisement layout metric function, that is: for brevity of the description, a plurality of energy values related to layout quality are not described again. Specifically, the energy function value of the variable-size layout metric function is a total energy value obtained by weighted summation of a distance energy value of a text element, a distance energy value of an image element, a relative position energy value of all advertisement elements, a size energy value of a text element, a small energy value of an image element, and an energy value describing an original condition of an advertisement element.
Specifically, the energy value of the original condition of the advertisement element is described as follows:
wherein,andrespectively the new height dimension and the original height dimension of the ith advertisement element,andthe relative position in the new advertisement layout and the relative position in the original advertisement layout of the center of the advertisement element are respectively.
S202, training the initial layout model based on the original size advertisement template sample and the new size advertisement template sample to train a variable size layout model.
Assume that there are 12 pairs of training sample data: the method comprises the steps that 12 original-size advertisement template samples and 12 new-size advertisement template samples are formed, in the specific implementation process, the implementation mode for solving the parameter theta in the size layout measurement function can refer to the implementation mode for solving the specific parameter theta in the size layout measurement function, so that the final result of the parameter theta in the size layout measurement function is obtained through learning, and similarly, the variable-size optimal layout scheme in the variable-size layout optimization process is calculated according to the simulated annealing algorithm, so that the new-size advertisement template samples are obtained.
And S203, generating a variable-size advertisement layout scheme by using the variable-size layout model.
In a second aspect, an embodiment of the present invention provides an advertisement layout apparatus, and referring to fig. 3, the advertisement layout apparatus provided in the embodiment of the present invention includes:
a modeling unit 301, configured to jointly establish an initial layout model based on the element features of the advertisement elements and the element features formed by interaction of more than two advertisement elements, where the advertisement elements arranged in multiple advertisement arrangement areas form a banner advertisement;
a first model training unit 302, configured to train the initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model;
a first advertisement layout unit 303, configured to generate a banner advertisement layout scheme using the advertisement layout model.
In an optional embodiment, the modeling unit 301 includes:
the abstraction subunit is used for abstracting each advertisement element for being laid out in the advertisement layout area into a corresponding rectangular area;
and the mathematical modeling subunit is used for performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
In an optional embodiment, the first model training unit 302 includes:
a function construction subunit, configured to construct an advertisement layout metric function for the initial layout model;
and the sample training subunit is used for training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
In an alternative embodiment, the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality.
In an optional embodiment, the sample training subunit is specifically configured to:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
In an optional embodiment, the sample training subunit is specifically configured to:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
In an optional implementation manner, in the step of generating the optimal layout scheme through the optimization algorithm, the sample training subunit is specifically configured to:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
In an alternative embodiment, the simulated annealing algorithm includes a plurality of annealing operators associated with the advertisement elements;
the sample training subunit is configured to, in the step of generating a preset number of advertisement layout proposals by performing one or more annealing operations based on a simulated annealing algorithm, generate each advertisement layout proposal by:
selecting one annealing operator from the plurality of annealing operators related to the advertisement elements aiming at the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times;
and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
In an optional embodiment, the apparatus further comprises:
the template processing unit is used for carrying out variable-size processing on the original-size advertisement template sample to generate a new-size advertisement template sample;
the second model training unit is used for training the initial layout model based on the original-size advertisement template sample and the new-size advertisement template sample so as to train a variable-size layout model;
and the second advertisement layout unit is used for generating a variable-size advertisement layout scheme by using the variable-size layout model.
In a third aspect, an embodiment of the present invention provides a computer device, as shown in fig. 4, including a memory 404, a processor 402, and a computer program stored on the memory 404 and executable on the processor 402, where the processor 402 executes the computer program to implement the steps of any of the foregoing advertisement layout method embodiments.
Where in fig. 4 a bus architecture (represented by bus 400) is shown, bus 400 may include any number of interconnected buses and bridges, and bus 400 links together various circuits including one or more processors, represented by processor 402, and memory, represented by memory 404. The bus 400 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 405 provides an interface between the bus 400 and the receiver 401 and transmitter 403. The receiver 401 and the transmitter 403 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 402 is responsible for managing the bus 400 and general processing, while the memory 404 may be used for storing data used by the processor 402 in performing operations.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the embodiments of the advertisement placement method described above.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
the advertisement layout method, the device and the computer equipment provided by the embodiment of the invention jointly establish an initial layout model based on the element characteristics of the advertisement elements forming the banner advertisement and the element characteristics formed by interaction of more than two advertisement elements; training an initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model; a banner advertisement layout scheme is generated using the advertisement layout model. Therefore, the advertisement layout model for automatically generating the advertisement layout scheme is generated based on machine learning and is used for automatically designing the advertisement layout, and therefore, the layout design efficiency of the banner advertisement can be improved.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a rich media advertisement production device according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The invention discloses an A1 advertisement layout method, comprising the following steps:
establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
training the initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model;
and generating a banner advertisement layout scheme by using the advertisement layout model.
A2, the method according to a1, wherein the establishing an initial layout model based on the element characteristics of the advertisement elements and the element characteristics of the interaction of more than two advertisement elements comprises:
abstracting each advertisement element for layout in the advertisement layout area into a corresponding rectangular area;
and performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
A3, the method as in A1 or A2, wherein the training the initial layout model by using the original-size advertisement template sample to obtain the banner advertisement layout model comprises:
constructing an advertisement layout metric function for the initial layout model;
and training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
A4, the method according to A3, wherein the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality.
A5, the method of A4, the training the initial layout model with the full-size advertisement template samples until the energy function value of the advertisement layout metric function is minimal to obtain the banner advertisement layout model, comprising:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
A6, the method of A5, wherein the determining whether the training termination condition is reached includes:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
A7, the method as in a6, wherein the step of generating the optimal layout solution by the optimization algorithm includes:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
A8, the method of A7, the simulated annealing algorithm comprising a plurality of annealing operators associated with advertisement elements; in the step of generating a preset number of advertisement layout proposals by performing one or more annealing operations based on the simulated annealing algorithm, each advertisement layout proposal is generated by:
selecting one annealing operator from the plurality of annealing operators related to the advertisement elements aiming at the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times;
and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
A9, the method as in any A1-A8, further comprising, after the generating a banner advertisement layout solution using the advertisement layout model:
carrying out variable size processing on the original size advertisement template sample to generate a new size advertisement template sample;
training the initial layout model based on the original-size advertisement template sample and the new-size advertisement template sample together to train a variable-size layout model;
and generating a variable-size advertisement layout scheme by using the variable-size layout model.
The invention discloses B10, an advertisement layout device, comprising:
the modeling unit is used for establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
the first model training unit is used for training the initial layout model by utilizing an original-size advertisement template sample to obtain a banner advertisement layout model;
and the first advertisement layout unit is used for generating a banner advertisement layout scheme by utilizing the advertisement layout model.
B11, the apparatus as described in B10, the modeling unit comprising:
the abstraction subunit is used for abstracting each advertisement element for being laid out in the advertisement layout area into a corresponding rectangular area;
and the mathematical modeling subunit is used for performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
B12, the apparatus as described in B10 or B11, the first model training unit comprising:
a function construction subunit, configured to construct an advertisement layout metric function for the initial layout model;
and the sample training subunit is used for training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
B13, the apparatus according to B12, wherein the energy function value of the advertisement layout metric function is a total energy value obtained by weighted summation of a plurality of energy values related to layout quality.
B14, the apparatus of B13, the sample training subunit being configured to:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
B15, the apparatus of B14, the sample training subunit being configured to:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
B16, the apparatus as in B15, wherein the sample training subunit, in the step of generating the optimal layout scheme by the optimization algorithm, is specifically configured to:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
B17, the apparatus as described in B16, the simulated annealing algorithm includes a plurality of annealing operators related to advertisement elements;
the sample training subunit is configured to, in the step of generating a preset number of advertisement layout proposals by performing one or more annealing operations based on a simulated annealing algorithm, generate each advertisement layout proposal by:
selecting one annealing operator from the plurality of annealing operators related to the advertisement elements aiming at the current proposal generation operation in the current annealing operation, wherein the current annealing operation comprises the proposal generation operation for a preset number of times;
and executing the current proposal generating operation by using a simulated annealing algorithm containing the currently selected annealing operator to generate a corresponding advertisement layout proposal.
B18, the apparatus as in any one of B10-B17, the apparatus further comprising:
the template processing unit is used for carrying out variable-size processing on the original-size advertisement template sample to generate a new-size advertisement template sample;
the second model training unit is used for training the initial layout model based on the original-size advertisement template sample and the new-size advertisement template sample so as to train a variable-size layout model;
and the second advertisement layout unit is used for generating a variable-size advertisement layout scheme by using the variable-size layout model.
The invention discloses C19, a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods A1-A9 when executing the program.
The invention discloses D20, a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described in a1-a 9.
Claims (10)
1. An advertisement layout method, comprising:
establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
training the initial layout model by using an original-size advertisement template sample to obtain a banner advertisement layout model;
and generating a banner advertisement layout scheme by using the advertisement layout model.
2. The method of claim 1, wherein the jointly building an initial layout model based on the element features of the advertisement elements and the element features of the interaction of more than two advertisement elements comprises:
abstracting each advertisement element for layout in the advertisement layout area into a corresponding rectangular area;
and performing mathematical modeling by taking the position coordinates and the size information of each rectangular area as a solution to obtain the initial layout model.
3. The method of claim 1 or 2, wherein the training of the initial layout model using the full-size advertisement template samples to obtain a banner advertisement layout model comprises:
constructing an advertisement layout metric function for the initial layout model;
and training the initial layout model by using the original-size advertisement template sample until the energy function value of the advertisement layout measurement function is minimum, so as to obtain the banner advertisement layout model.
4. The method of claim 3, wherein the energy function value of the advertising layout metric function is a total energy value obtained by weighted summation of a plurality of energy values associated with layout quality.
5. The method of claim 4, wherein said training the initial layout model using the full-size advertisement template samples until the energy function value of the advertisement layout metric function is minimized to obtain the banner advertisement layout model comprises:
initializing specific parameters of the advertisement layout measurement function, and putting the original-size advertisement template sample into a candidate solution;
taking the solution which enables the total energy value of the advertisement layout metric function to be minimum in the candidate solutions as a current optimal solution, and performing reverse optimization processing based on the current optimal solution to obtain a new solution;
updating the specific parameters based on a gradient descent method and judging whether a training termination condition is reached;
if the training termination condition is reached, outputting the current optimal solution as the banner advertisement layout model; otherwise, updating the candidate solution based on the new solution, and returning a solution which minimizes the total energy value of the advertisement layout metric function in the candidate solutions as a current optimal solution based on the updated candidate solution and the updated specific parameters.
6. The method of claim 5, wherein the determining whether a training termination condition is reached comprises:
judging whether the current function value of the target function reaches a preset function value or not, and if so, determining that the training termination condition is reached; the objective function is used for representing the difference between the total energy value of the advertisement layout template corresponding to the current optimal solution and the total energy value of the optimal layout scheme generated through an optimization algorithm.
7. The method of claim 6, wherein the step of generating the optimal layout solution by the optimization algorithm comprises:
executing more than one annealing operation based on a simulated annealing algorithm to generate a preset number of advertisement layout proposals;
and determining the optimal layout scheme from the preset number of advertisement layout proposals.
8. An advertisement layout device, comprising:
the modeling unit is used for establishing an initial layout model together based on the element characteristics of the advertisement elements and the element characteristics formed by interaction of more than two advertisement elements, wherein the advertisement elements arranged in a plurality of advertisement layout areas form banner advertisements;
the first model training unit is used for training the initial layout model by utilizing an original-size advertisement template sample to obtain a banner advertisement layout model;
and the first advertisement layout unit is used for generating a banner advertisement layout scheme by utilizing the advertisement layout model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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