Advertising Placement Data Evaluation System and Method Based on Multi-Channel Information Integration

Abstract

The present invention discloses an advertising delivery data evaluation system and method based on multi-channel information integration, which relates to the technical field of advertising delivery. The activity index of users is obtained according to the operation data of historical users before the advertisement starts automatically; the prominence and relevance of the advertisement are obtained based on historical marketing data, and the operation data of users after the advertisement starts automatically is analyzed under the activity index, prominence and relevance; the self-start evaluation value of the advertisement is obtained according to the operation data of users after the advertisement starts automatically, an advertisement self-start control model is established, and the self-start behavior of the advertisement is controlled through the advertisement self-start control model; controlling the self-start behavior of the advertisement can reduce the possibility of users actively closing the advertisement, increase the retention time of the advertisement on the page and the click-through rate of users on the advertisement, and improve the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising placement, and specifically to an advertising placement data evaluation system and method based on multi-channel information integration. Background Art

[0002] With the popularization of the Internet and the progress of technology, web page advertising has become one of the important forms of advertising placement. Web page advertising has the characteristics of wide dissemination range, strong interactivity, precise placement, etc., and thus has received extensive attention and favor from advertisers; it is a common phenomenon to embed advertisements in web pages, and when the user drags the web page to a certain position, the video advertisement starts to play automatically or the picture advertisement starts to switch automatically. This behavior may have an impact on the user experience. When the user does not need the advertisement, every time the web page is dragged to a certain position, the advertisement will start automatically, which seriously affects the user experience and forces the user to manually close the advertisement or the page; therefore, how to control the self-start behavior of the advertisement and improve the user experience has become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of the present invention is to provide an advertising placement data evaluation system and method based on multi-channel information integration to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An advertising placement data evaluation system based on multi-channel information integration, including a page data acquisition module, an advertisement self-start control module, a self-start analysis module, and a data storage module; the output end of the data acquisition module is connected to the input end of the data storage module to obtain page data and the user's operation data on the page; the output end of the data storage module is connected to the input end of the self-start analysis module for storing the user's behavior data on the web page; the output end of the self-start analysis module is connected to the input end of the advertisement self-start control module for analyzing the real-time behavior data of the user on the web page to obtain an advertisement self-start evaluation value, and generating a control instruction for the advertisement self-start according to the self-start evaluation value; the advertisement self-start control module controls the start state of the advertisement according to the received control instruction.

[0005] Specifically, the page data acquisition module further includes a space conversion unit, an operation monitoring unit, a permission acquisition unit, and a keyword extraction unit; the space conversion unit is used to convert the page data into a color space; the operation monitoring unit is used to monitor the user's operation behavior on the page; the permission acquisition unit is used to request permission from the user; the keyword extraction unit is used to obtain the keywords in the page.

[0006] Specifically, the self-start analysis module further includes an activity analysis unit, an attention analysis unit, a prominence analysis unit, and a self-start evaluation unit; the activity analysis unit obtains the user's activity based on the operation data of the user on the page; the attention analysis unit obtains the user's attention to keywords according to the scrolling direction of the page; the prominence analysis unit obtains the prominence of the advertisement after self-start according to the color characteristics of the page, the color characteristics before the advertisement self-starts, and the color characteristics after the advertisement self-starts; the self-start evaluation unit is used to calculate the self-start evaluation value of the advertisement of the real-time behavior data.

[0007] Specifically, the self-start evaluation unit determines the influence directions of the prominence and the activity on the operation data, obtains different clusters according to the prominence and the activity; trains an advertisement self-start control model according to the prominence and the relevance in the same cluster.

[0008] To achieve the above object, the present invention provides the following technical solution: an advertisement placement data evaluation method based on multi-channel information integration, including the following steps:

[0009] Obtain the user's behavior data on the web page through user authorization, and obtain real-time behavior data and historical behavior data; the historical behavior data includes historical marketing data and historical user feedback data; the marketing data includes advertisement data before and after self-start; the user feedback data includes page data, the operation data of the user before the advertisement self-starts, and the operation data of the user after the advertisement self-starts;

[0010] Obtain the user's activity index according to the operation data of the historical user before the advertisement self-starts; obtain the prominence and relevance of the advertisement based on the historical marketing data, and analyze the operation data of the user after the advertisement self-starts under the activity index, prominence, and relevance; obtain the self-start evaluation value of the advertisement according to the operation data of the user after the advertisement self-starts, and establish an advertisement self-start control model, and the advertisement self-start control model is used to control the self-start of the advertisement;

[0011] Obtain the self-start evaluation value of the current advertisement according to the real-time behavior data, and control the self-start behavior of the advertisement through the advertisement self-start control model.

[0012] Specifically, the steps of judging the user's state according to the operation data of the historical user before the advertisement self-starts further include the following steps:

[0013] Extract operation features from the operation data of the historical user before the advertisement self-starts, and obtain the user's activity index act according to the user's operation features, act = ∑w i ×p i , where p i represents the i-th operation feature, and w i represents the weight of the i-th operation feature.

[0014] Specifically, analyzing the operation data of users after the advertisement self-starts under the activity index, prominence, and relevance further includes: obtaining the attention of keywords from historical page data, specifically including the following steps:

[0015] Obtain the keywords and the position information of the keywords in the page from the historical page data; obtain the page scrolling direction data and keyword metrics from the operation data of historical users before the advertisement self-starts, and calculate the attention of the keywords according to the following formula, att = ∑u j ×e j , where e j represents the j-th keyword metric, and u j represents the weight of the j-th keyword. Among them, u j = u j 0 + v 1 ×u j 1 + … + v n ×u j n , where u j 0 is the metric of the j-th keyword when the mouse does not perform a rollback operation, and u j 1 , …, u j n represent the metrics of the j-th keyword when the mouse performs 1 time, …, n times of rollback operations, and v 1 , …, v n represent the reward coefficients when the mouse performs 1 time, …, n times of rollback operations, and 1 < v 1 < … < v n .

[0016] Specifically, obtaining the prominence of the advertisement based on historical marketing data further includes the following steps:

[0017] Step 1, obtain the image data of the web page from the historical page data, convert the image data of the web page to the RGB space, and calculate the color feature A1 for each pixel in the image of the web page; obtain the average color feature of the web page according to the color features of each pixel in the image of the web page;

[0018] Step 2, in the same way as in Step 1, obtain the average color feature A2 of the advertisement before self-start based on the advertisement data before self-start, and obtain the prominence BXM of the advertisement before self-start, BXM = A2 / A1;

[0019] Step 3: Obtain the average color features of each image frame of the advertisement after self-start based on the advertisement data before self-start. Generate a first saliency sequence and a second saliency sequence according to the average color features of each image frame. The first saliency is the quotient of the average color feature of each image frame of the advertisement after self-start and the average color feature of the web page. The second saliency is the quotient of the average color feature of the subsequent image frame of the advertisement after self-start and the average color feature of the previous image frame of the advertisement. Take the maximum value in the first saliency sequence and the second saliency sequence to obtain the saliency AXM of the advertisement after self-start.

[0020] Specifically, obtaining the relevance of the advertisement based on historical marketing data further includes the following steps:

[0021] Extract the text feature vector group from the advertisement data after self-start. Obtain the relevance of the advertisement according to the extracted text feature vector group and the user's attention to the keywords on the page.

[0022] Specifically, analyzing the operation data of the user after the advertisement self-starts under the activity index, saliency, and relevance further includes the following steps:

[0023] S100: Determine the influence directions of saliency and activity on the operation data. Obtain the activity index, saliency, relevance, and the operation data of the user after the advertisement self-starts from the historical behavior data to form an operation feature combination [act, xm, gld, fk]. In the formula, xm represents saliency, gld represents relevance, and fk is the operation data of the user after the advertisement self-starts. According to the operation results of the user, assign values to clicking on the advertisement, placing the advertisement, and closing the advertisement respectively, and quantify the operation data of the user after the advertisement self-starts. Concatenate the activity index and relevance to obtain the input feature [act, gld]. Perform unsupervised classification on the input feature, specifically including the following steps:

[0024] Initialize the parameters, and set the neighborhood radius and minimum number of points of the DBSCAN algorithm.

[0025] Data point marking: Use all the input features obtained in step S100 as the data set. Select a data point from the data set, and determine the number of other data points included within the neighborhood radius of the selected data point. If the number is not less than the minimum number of points, mark the selected data point as a core point. If the number is less than the minimum number of points, but the selected data point is within the neighborhood radius of a core point, mark the selected point as a boundary point. Otherwise, mark the selected point as a noise point.

[0026] Generate clusters. Select an unvisited data point from the dataset and check whether the data point is marked as a core point. If it is marked as a core point, add the core point and all unvisited data points within the neighborhood radius of the core point to a cluster, and then mark these data points in the cluster as visited. For each newly added core point in the cluster, continue to process the data points within its neighborhood radius. If these neighborhood points have not been visited, check whether they are marked as core points. If they are marked as core points, add the data points within their neighborhood radius to the cluster and mark them as visited. Repeat this step until no new core points can expand the cluster.

[0027] Parameter optimization. For the data points in the cluster, analyze the relationship between the salience xm and the operation data fk of the user after the advertisement starts automatically in the corresponding operation feature combination. Sort [xm fk] in ascending order according to xm, obtain the distribution data of three cases of fk, and obtain the monotonicity based on the distribution data of the three cases of fk. Adjust the neighborhood radius and the minimum number of points of the DBSCAN algorithm based on the monotonicity.

[0028] S200. Obtain the operation feature combination [act, xm, gld, fk] in the same cluster, and construct an advertisement automatic start control model in the cluster according to the operation feature combination: y = a×xm + b×gld + c; where a and b are fitting coefficients, c represents the bias, and a, b, and c are solved by the least squares method; y is the automatic start evaluation value of the advertisement.

[0029] S300. Obtain real-time behavior data, extract the keywords of the web page from the real-time page data, and obtain the attention of the web page keywords according to the operation data of the user before the advertisement starts automatically; obtain real-time salience, relevance, and activity indicators from the real-time marketing data and real-time user feedback data; determine the cluster to which the real-time behavior data belongs; input the real-time behavior data into the advertisement automatic start control model corresponding to the cluster to obtain the automatic start evaluation value of the advertisement of the real-time behavior data; if the automatic start evaluation value of the advertisement is less than k1, control the advertisement not to start automatically; if the automatic start evaluation value is greater than k2, control the advertisement to start automatically; otherwise, keep the current automatic start state of the advertisement without additional automatic start control; where k2 and k1 are the values assigned for the operations of clicking on the advertisement and closing the advertisement.

[0030] Compared with the prior art, the beneficial effects of the present invention are: controlling the automatic start behavior of the advertisement can reduce the possibility of users actively closing the advertisement, increase the retention time of the advertisement on the page and the click-through rate of the advertisement by users, and improve the user experience; analyzing the influence direction of the color of the advertisement in different situations on the user operation behavior, obtaining the automatic start evaluation value of the advertisement, and controlling the automatic start of the advertisement. Description of the Drawings

[0031] Figure 1 This is a schematic structural diagram of an advertising placement data evaluation system based on multi-channel information integration according to the present invention. Specific embodiments

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Embodiment: As Figure 1 shown, the present invention provides a technical solution, an advertising placement data evaluation system based on multi-channel information integration, including a page data acquisition module, an advertisement self-start control module, a self-start analysis module, and a data storage module; the output end of the data acquisition module is connected to the input end of the data storage module to obtain page data and the operation data of the user on the page by the user; the output end of the data storage module is connected to the input end of the self-start analysis module to store the behavior data of the user on the web page; the output end of the self-start analysis module is connected to the input end of the advertisement self-start control module to analyze the real-time behavior data of the user on the web page to obtain the self-start evaluation value of the advertisement, and generate a control instruction for the advertisement self-start according to the self-start evaluation value; the advertisement self-start control module controls the start state of the advertisement according to the received control instruction.

[0034] The page data acquisition module further includes a space conversion unit, an operation monitoring unit, a permission acquisition unit, and a keyword extraction unit; the space conversion unit is used to convert the page data into a color space; the operation monitoring unit is used to monitor the operation behavior of the user on the page; the permission acquisition unit is used to apply for permission from the user; the keyword extraction unit is used to obtain the keywords in the page.

[0035] Advertising data is also a type of page data, and advertising data is also obtained through the page data acquisition module; when the advertisement is in the form of a picture, it is directly obtained through the page data acquisition module; when the advertisement is in the form of a video, the page data acquisition module can collect the advertisement image data under different frames.

[0036] The self-start analysis module further includes an activity analysis unit, an attention analysis unit, a prominence analysis unit, and a self-start evaluation unit; the activity analysis unit obtains the user's activity based on the operation data of the user on the page; the attention analysis unit obtains the user's attention to keywords according to the scrolling direction of the page; the prominence analysis unit obtains the prominence of the advertisement after self-start according to the color characteristics of the page, the color characteristics before the advertisement self-starts, and the color characteristics after the advertisement self-starts; the self-start evaluation unit calculates the self-start evaluation value of the advertisement of the real-time behavior data.

[0037] The self-start evaluation unit determines the influence directions of the prominence and the activity on the operation data, and obtains different clusters according to the prominence and the activity; trains an advertisement self-start control model according to the prominence and the relevance in the same cluster.

[0038] Embodiment: The present invention provides a technical solution, an advertisement placement data evaluation method based on multi-channel information integration, including the following steps:

[0039] Obtain the user's behavior data on the web page through user authorization, and obtain real-time behavior data and historical behavior data; the historical behavior data includes historical marketing data and historical user feedback data; the marketing data includes advertisement data before and after self-start; the user feedback data includes page data, the operation data of the user before the advertisement self-starts, and the operation data of the user after the advertisement self-starts;

[0040] Obtain the user's activity index according to the operation data of the historical user before the advertisement self-starts; obtain the prominence and relevance of the advertisement based on the historical marketing data, and analyze the operation data of the user after the advertisement self-starts under the activity index, the prominence, and the relevance; obtain the self-start evaluation value of the advertisement according to the operation data of the user after the advertisement self-starts, and establish an advertisement self-start control model, and the advertisement self-start control model is used to control the self-start of the advertisement;

[0041] Obtain the self-start evaluation value of the current advertisement according to the real-time behavior data, and control the self-start behavior of the advertisement through the advertisement self-start control model.

[0042] Judging the user's state according to the operation data of the historical user before the advertisement self-starts further includes the following steps:

[0043] Extract operation features from the operation data of the historical user before the advertisement self-starts, and obtain the user's activity index act according to the user's operation features, act = ∑w i ×p i , where p i represents the i-th operation feature, and w i represents the weight of the i-th operation feature.

[0044] The operation characteristics of users include, but are not limited to: the frequency of clicking the mouse on the page, the speed of mouse movement, the speed of page scrolling, the speed of dragging the page, etc.; the activity index is used to reflect the activity status of users.

[0045] Analyzing the operation data of users after the advertisement starts automatically under the activity index, eye-catching degree, and relevance also includes: obtaining the attention degree of keywords from historical page data, specifically including the following steps:

[0046] Obtain the keywords and their position information in the page from historical page data; obtain the page scrolling direction data and keyword metrics from the historical operation data of users before the advertisement starts automatically, and calculate the attention degree of keywords according to the following formula, att = ∑u j ×e j , where e j represents the jth keyword metric, and u j represents the weight of the jth keyword. Among them, u j = u j 0 + v 1 ×u j 1 +…+ v n ×u j n , where u j 0 is the metric of the jth keyword when the mouse does not perform a rollback operation, and u j 1 , …, u j n represent the metrics of the jth keyword when the mouse performs 1 time, …, n times of rollback operations, and v 1 , …, v n represent the reward coefficients when the mouse performs 1 time, …, n times of rollback operations. 1 < v 1 < … < v n .

[0047] Keyword metrics include, but are not limited to: the number of times a keyword is clicked by the mouse, the time the mouse stays on the keyword, etc.; when the user performs a rollback operation and stops the mouse on a keyword that has already been passed or clicks on a keyword that has already been passed again, it indicates that the user has a certain interest in the keyword. Therefore, a reward coefficient is added to the rollback operation. For the same keyword, the more times of rollback, the greater the reward coefficient.

[0048] Obtaining the eye-catching degree of the advertisement based on historical marketing data also includes the following steps:

[0049] Step 1: Obtain the image data of the web page from the historical page data, convert the image data of the web page to the RGB space, and for each pixel in the image of the web page, calculate the color feature A1; obtain the average color feature of the web page based on the color features of each pixel in the image of the web page.

[0050] Step 2: In the same way as in Step 1, based on the advertisement data before self-start, obtain the average color feature A2 of the advertisement before self-start, and obtain the eye-catching degree BXM of the advertisement before self-start, where BXM = A2 / A1.

[0051] Step 3: Based on the advertisement data before self-start, obtain the average color feature of each image frame of the advertisement after self-start, generate the first eye-catching degree sequence and the second eye-catching degree sequence according to the average color feature of each image frame, where the first eye-catching degree is the quotient of the average color feature of each image frame of the advertisement after self-start and the average color feature of the web page, and the second eye-catching degree is the quotient of the average color feature of the subsequent image frame of the advertisement after self-start and the average color feature of the previous image frame of the advertisement; take the maximum value in the first eye-catching degree sequence and the second eye-catching degree sequence to obtain the eye-catching degree AXM of the advertisement after self-start.

[0052] Optionally, convert the image data to the RGB color space, set the color feature as the brightness value, calculate the brightness value of each pixel point according to the red, green, and blue channel values in the RGB color space, and then obtain the brightness value Y of the page according to the brightness values of the pixel points, where Y = 0.2123R + 0.7152G + 0.0722B, and R, G, and B in the formula are the red, green, and blue channel values of the pixel point respectively. The color feature will affect the user's attention result to the advertisement. The greater the difference in color features, the more likely the user is to notice the advertisement. The user's attention may bring positive or negative effects, which depends on the user's state and the content of the advertisement.

[0053] Obtaining the relevance of the advertisement based on historical marketing data further includes the following steps:

[0054] Extract the text feature vector group from the advertisement data after self-start, and obtain the relevance of the advertisement according to the extracted text feature vector group and the user's attention to the keywords on the page.

[0055] Obtain the keyword feature vector of the page according to the keywords on the page, and according to the keyword feature vector, the text feature vector, and the relevance gld of the advertisement, ; where is the attention to the k-th page keyword after linear transformation. After linearly transforming the attention to all page keywords, make the sum of all attentions equal to one to obtain ; represents the k-th keyword feature vector in the page; It represents the k-th text feature vector in the text feature vector group of the m-th image frame after automatic jump; z is the number of text feature vector groups, and M is the number of image frames after automatic jump.

[0056] Analyzing the operation data of users after the advertisement starts automatically under the activity index, prominence, and relevance also includes the following steps:

[0057] S100, determine the influence directions of prominence and activity on the operation data, obtain the activity index, prominence, relevance, and the operation data of users after the advertisement starts automatically from the historical behavior data, and form an operation feature combination [act, xm, gld, fk], where xm represents prominence, gld represents relevance, and fk is the operation data of users after the advertisement starts automatically; according to the operation results of users, assign values to clicking on the advertisement, placing the advertisement, and closing the advertisement respectively, and quantify the operation data of users after the advertisement starts automatically; splice the activity index and relevance to obtain the input feature [act, gld], and perform unsupervised classification on the input feature;

[0058] Optionally, assign values of 0, 1, and 2 to clicking on the advertisement, placing the advertisement, and closing the advertisement respectively;

[0059] Specifically, it includes the following steps:

[0060] Initialize the parameters, and set the neighborhood radius and the minimum number of points of the DBSCAN algorithm;

[0061] Data point marking: Take all the input features obtained in step S100 as the data set, select a data point from the data set, and determine the number of other data points within the neighborhood radius of the selected data point. If the number is not less than the minimum number of points, mark the selected data point as a core point; if the number is less than the minimum number of points, but the selected data point is within the neighborhood radius of a core point, mark the selected point as a boundary point; otherwise, mark the selected point as a noise point;

[0062] Generate clusters: Select an unvisited data point from the data set, check whether the data point is marked as a core point. If it is marked as a core point, add the core point and all unvisited data points within the neighborhood radius of the core point to a cluster, and then mark these data points in the cluster as visited; for each newly added core point in the cluster, continue to process the data points within its neighborhood radius. If these neighborhood points have not been visited, check whether they are marked as core points. If they are marked as core points, add the data points within their neighborhood radius to the cluster and mark them as visited; repeat this step until no new core points can expand the cluster;

[0063] Parameter optimization: For the data points in the cluster, analyze the relationship between the salience xm and the operation data fk of the user after the advertisement starts automatically in the corresponding operation feature combination. Sort [xm, fk] in ascending order according to xm, obtain the distribution data of three cases of fk, obtain the monotonicity based on the distribution data of the three cases of fk, and adjust the neighborhood radius and the minimum number of points of the DBSCAN algorithm based on the monotonicity;

[0064] The influence of salience on operation behavior may be a promoting effect or a hindering effect. For example, when the user's activity index is large, it means that the user is relatively busy. When the content of the advertisement after automatic startup has a low relevance to the content of the page, if the salience of the advertisement after automatic startup is high at this time, it may affect the user and prompt the user to close the advertisement. In this case, it is not appropriate to start the advertisement automatically. When the user's activity index is low, it means that the user is relatively idle. When the content of the advertisement after automatic startup has a high relevance to the content of the page, if the salience of the advertisement after automatic startup is high at this time, the user is more likely to click on the advertisement after noticing the advertisement. At this time, the salience plays a promoting role. The purpose of unsupervised classification is to obtain the influence direction of salience on operation behavior under different combinations of activity indexes and relevance degrees. To improve the classification effect, parameters need to be optimized. A fixed number of [xm, fk] can be regarded as a whole, and then the distribution data of three cases of fk in each whole in the sequence can be obtained, that is, the distribution of 0, 1, and 2, and the frequencies of 0, 1, and 2 appearing in each whole, to obtain the frequency change of 0, 1, and 2. If the distribution of 0, 1, and 2 changes in the same direction as xm increases, for example, the frequency of 0 decreases, and the frequencies of 1 and 2 increase, it means that the monotonicity is good. On the contrary, the frequency of 0 increases, and the frequencies of 1 and 2 decrease, which also means that the monotonicity is good. If the monotonicity is not obvious, it means that the classification effect is poor, and the neighborhood radius and the minimum number of points are adjusted.

[0065] S200: Obtain the operation feature combination [act, xm, gld, fk] in the same cluster, and construct an advertisement automatic startup control model in the cluster according to the operation feature combination: y = a × xm + b × gld + c; where a and b are fitting coefficients, c represents the bias, and a, b, and c are solved by the least squares method; y is the automatic startup evaluation value of the advertisement;

[0066] Use historical user feedback data to solve the parameters, obtain xm, gld, and fk in the historical data, use xm and gld as inputs and fk as outputs for training to obtain the fitting coefficients and the bias. During the training process, if the fitting value and the true value of the model are in the same category, the error is set to zero, otherwise the error is retained. For example, fk = 0 means that the user has closed the automatically started advertisement. If the output fitting result of the model is between (0, 1), it means that the model classification is correct, so no error is generated.

[0067] S300, obtain real-time behavior data, extract keywords of the web page from the real-time page data, and obtain the attention of the web page keywords based on the operation data of the user before the advertisement self-starts; obtain real-time prominence, relevance, and activity indicators from the real-time marketing data and real-time user feedback data; determine the cluster to which the real-time behavior data belongs; input the real-time behavior data into the advertisement self-start control model corresponding to the cluster to obtain the self-start evaluation value of the advertisement of the real-time behavior data; if the self-start evaluation value of the advertisement is less than k1, then control the advertisement not to self-start; if the self-start evaluation value is greater than k2, then control the advertisement to self-start; otherwise, maintain the current self-start state of the advertisement and do not perform additional self-start control; where k2 and k1 are the values assigned to the operations of clicking on the advertisement and closing the advertisement.

[0068] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An advertising placement data evaluation method based on multi-channel information integration, characterized in that, The method includes the following steps: Obtain the user's behavioral data on the web page through user authorization, and acquire real-time behavioral data and historical behavioral data; the historical behavioral data includes historical marketing data and historical user feedback data; the marketing data includes advertisement data before and after self-start; the user feedback data includes page data, the operation data of the user before the advertisement self-starts, and the operation data of the user after the advertisement self-starts; Obtain the user's activity index according to the operation data of the user before the advertisement self-starts; obtain the eye-catching degree and relevance of the advertisement based on the historical marketing data, and analyze the operation data of the user after the advertisement self-starts under the activity index, eye-catching degree, and relevance; Obtain the self-start evaluation value of the advertisement according to the operation data of the user after the advertisement self-starts, and establish an advertisement self-start control model, which is used to control the self-start of the advertisement; Obtain the self-start evaluation value of the current advertisement according to the real-time behavioral data, and control the self-start behavior of the advertisement through the advertisement self-start control model; The analysis of the operation data of the user after the advertisement self-starts under the activity index, eye-catching degree, and relevance further includes the following steps: S100, determine the influence direction of the eye-catching degree and activity on the operation data, obtain the activity index, eye-catching degree, relevance, and the operation data of the user after the advertisement self-starts from the historical behavioral data to form an operation feature combination [act, xm, gld, fk], where xm represents the eye-catching degree, gld represents the relevance, and fk is the operation data of the user after the advertisement self-starts; according to the user's operation result, assign values to clicking on the advertisement, placing the advertisement, and closing the advertisement respectively, and quantify the operation data of the user after the advertisement self-starts; splice the activity index and relevance to obtain an input feature [act, gld], and perform unsupervised classification on the input feature, which specifically includes the following steps: Initialize the parameters, and set the neighborhood radius and minimum number of points of the DBSCAN algorithm; Data point marking, use all the input features obtained in step S100 as a data set, select a data point from the data set, determine the number of other data points included within the neighborhood radius of the selected data point, if the number is not less than the minimum number of points, mark the selected data point as a core point; if the number is less than the minimum number of points, but the selected data point is within the neighborhood radius of the core point, mark the selected point as a boundary point; otherwise, mark the selected point as a noise point; Generate clusters. Select an unvisited data point from the dataset and check whether the data point is marked as a core point. If it is marked as a core point, add the core point and all unvisited data points within the neighborhood radius of the core point to a cluster, and then mark these data points in the cluster as visited. For each newly added core point in the cluster, continue to process the data points within its neighborhood radius. If these neighborhood points have not been visited, check whether they are marked as core points. If they are marked as core points, add the data points within their neighborhood radius to the cluster and mark them as visited. Repeat this step until no new core points can expand the cluster. Parameter optimization. For the data points in the cluster, analyze the relationship between the salience xm and the operation data fk of the user after the advertisement is auto-started in the corresponding operation feature combination. Sort [xm fk] in ascending order according to xm, obtain the distribution data of three cases of fk, obtain the monotonicity based on the distribution data of three cases of fk, and adjust the neighborhood radius and the minimum number of points of the DBSCAN algorithm based on the monotonicity. S200. Obtain the operation feature combination [act, xm, gld, fk] in the same cluster, and construct an advertisement auto-start control model in the cluster according to the operation feature combination: y = a×xm + b×gld + c; where a and b are fitting coefficients, c represents the bias, and a, b, and c are solved by the least squares method; y is the auto-start evaluation value of the advertisement. S300. Obtain real-time behavior data. Extract the keywords of the web page from the real-time page data, and obtain the attention degree of the web page keywords according to the operation data of the user before the advertisement is auto-started. Obtain real-time salience, relevance, and activity indicators from real-time marketing data and real-time user feedback data. Determine the cluster to which the real-time behavior data belongs. Input the real-time behavior data into the advertisement auto-start control model corresponding to the cluster to obtain the auto-start evaluation value of the advertisement of the real-time behavior data. If the auto-start evaluation value of the advertisement is less than k1, control the advertisement not to auto-start. If the auto-start evaluation value is greater than k2, control the advertisement to auto-start. Otherwise, keep the current auto-start state of the advertisement without additional auto-start control; where k2 and k1 are the values assigned for the operations of clicking on the advertisement and closing the advertisement.

2. The advertising placement data evaluation method based on multi-channel information integration according to claim 1, characterized in that The step of judging the state of the user according to the operation data of the historical user before the advertisement is auto-started further includes the following steps: Extract operation features from the operation data of historical users before the advertisement starts automatically, and obtain the user activity index act according to the operation features of the user, act = ∑w i ×p i , where p i represents the i-th operation feature, and w i represents the weight of the i-th operation feature.

3. The advertising placement data evaluation method based on multi-channel information integration according to claim 2, wherein The step of analyzing the operation data of the user after the advertisement is auto-started under the activity indicator, salience, and relevance further includes: obtaining the attention degree of the keywords from the historical page data, specifically including the following steps: Obtain the keywords and their position information in the page from the historical page data; obtain the page scrolling direction data and keyword metrics from the historical user operation data before the advertisement starts automatically, and calculate the attention degree of the keywords according to the following formula, att = ∑u j ×e j , where e j represents the j-th keyword metric, and u j represents the weight of the j-th keyword. Among them, u j = u j 0 + v 1 ×u j 1 + … + v n ×u j n , where u j 0 is the metric of the j-th keyword when the mouse does not perform a rollback operation, and u j 1 , …, u j n represent the metrics of the j-th keyword when the mouse performs 1 time, …, n times of rollback operations, and v 1 , …, v n represent the reward coefficients when the mouse performs 1 time, …, n times of rollback operations, and 1 < v 1 < … < v n .

4. The advertising placement data evaluation method based on multi-channel information integration according to claim 3, characterized in that The step of obtaining the salience of the advertisement based on the historical marketing data further includes the following steps: Step 1. Obtain the image data of the web page from the historical page data, convert the image data of the web page to the RGB space, and calculate the color feature A1 for each pixel in the image of the web page. Obtain the average color feature of the web page according to the color features of each pixel in the image of the web page. Step 2: In the same way as in Step 1, based on the advertisement data before self-start, obtain the average color feature A2 of the advertisement before self-start, and obtain the eye-catching degree BXM of the advertisement before self-start, where BXM = A2 / A1; Step 3: Based on the advertisement data before self-start, obtain the average color feature of each image frame of the advertisement after self-start, and generate a first eye-catching degree sequence and a second eye-catching degree sequence according to the average color feature of each image frame. Among them, the first eye-catching degree is the quotient of the average color feature of each image frame of the advertisement after self-start and the average color feature of the web page, and the second eye-catching degree is the quotient of the average color feature of the subsequent image frame of the advertisement after self-start and the average color feature of the previous image frame of the advertisement; Take the maximum value in the first eye-catching degree sequence and the second eye-catching degree sequence to obtain the eye-catching degree AXM of the advertisement after self-start.

5. The method for evaluating advertising placement data based on multi-channel information integration according to claim 4, wherein The obtaining of the relevance of the advertisement based on historical marketing data further includes the following steps: Extract the text feature vector group from the advertisement data after self-start, and obtain the relevance of the advertisement according to the extracted text feature vector group and the user's attention to the keywords on the page.

6. An advertising placement data evaluation system based on multi-channel information integration, which is used to use the advertising placement data evaluation method based on multi-channel information integration as described in claim 1, and is characterized in that, It includes a page data acquisition module, an advertisement self-start control module, a self-start analysis module, and a data storage module; The output end of the data acquisition module is connected to the input end of the data storage module to obtain page data and the user's operation data on the page; The output end of the data storage module is connected to the input end of the self-start analysis module, which is used to store the user's behavior data on the web page; The output end of the self-start analysis module is connected to the input end of the advertisement self-start control module, which is used to analyze the real-time behavior data of the user on the web page to obtain the self-start evaluation value of the advertisement, and generate a control instruction for the self-start of the advertisement according to the self-start evaluation value; The advertisement self-start control module controls the start state of the advertisement according to the received control instruction.

7. The advertising placement data evaluation system based on multi-channel information integration according to claim 6, wherein The page data acquisition module further includes a space conversion unit, an operation monitoring unit, a permission acquisition unit, and a keyword extraction unit; The space conversion unit is used to convert the page data into a color space; The operation monitoring unit is used to monitor the user's operation behavior on the page; The permission acquisition unit is used to apply for permission from the user; The keyword extraction unit is used to obtain the keywords on the page.

8. The advertising placement data evaluation system based on multi-channel information integration according to claim 7, wherein, The self-start analysis module further includes an activity analysis unit, an attention analysis unit, an eye-catching degree analysis unit, and a self-start evaluation unit; The activity analysis unit obtains the user's activity based on the user's operation data on the page; The attention analysis unit obtains the user's attention to the keywords according to the scrolling direction of the page; The eye-catching degree analysis unit obtains the eye-catching degree of the advertisement after self-start according to the color feature of the page, the color feature before the advertisement self-starts, and the color feature after the advertisement self-starts; The self-start evaluation unit is used to calculate the self-start evaluation value of the advertisement of the real-time behavior data.

9. The advertising placement data evaluation system based on multi-channel information integration according to claim 8, characterized in that, The self-start evaluation unit determines the influence direction of the eye-catching degree and the activity on the operation data, and obtains different clusters according to the eye-catching degree and the activity; Train the advertisement self-start control model according to the eye-catching degree and the relevance in the same cluster.

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