CN108876430B - Advertisement pushing method based on crowd characteristics, electronic equipment and storage medium - Google Patents
Advertisement pushing method based on crowd characteristics, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an advertisement pushing method based on crowd characteristics, which comprises the following steps: and (3) product classification binding: acquiring a video image of a user crowd watching a product advertisement for more than a set time period, extracting user characteristics of the user crowd, and binding product classification to which the product advertisement belongs with common user characteristics of the user crowd; user feature extraction: acquiring a user video image, and extracting user characteristics of a user in the video image; and (3) advertisement pushing step: and determining common user characteristics corresponding to the user characteristics of the user, and pushing product advertisements under the product categories bound with the common user characteristics to the user for watching. The invention further discloses electronic equipment and a storage medium, and the advertisement push method based on the crowd characteristics, the electronic equipment and the storage medium can provide more accurate product advertisements according to the user characteristics and the crowd characteristic analysis.
Description
Technical Field
The invention relates to the field of data analysis and pushing, in particular to an advertisement pushing method based on crowd characteristics, electronic equipment and a storage medium.
Background
At present, with the continuous development of social economy, the living standard of people is continuously improved, and the development of the advertising industry is promoted while the labor consumption and the purchasing power are continuously improved. In order to guide consumption, promote transaction and improve profit, the advertisement putting plays an important role; the outdoor advertisement released by the display screen is popular with many event hosts by virtue of the characteristics of strong visual impact and high repeated reading rate. However, currently, the advertisement has a wide popularity and is not targeted well. Aiming at unspecified people, the following negative effects are brought: for the commercial tenant, the accurate touch of the real target user is not facilitated, and the marketing efficiency is low; for consumers, the wide reception of many advertisements irrelevant to the demands of the consumers can make the consumers feel bored and is not beneficial to product popularization. Therefore, special customized advertisements provided according to personalized settings such as user characteristics and preferences appear on the market, the advertisements are generally obtained by analyzing big data based on the internet, and the interests or requirements of the user can be better known by matching with the behavior characteristics of the user on the internet according to personal related information of the user, so that accurate special advertisement delivery is realized.
However, the steps of personalized setting are complicated, complicated and time-consuming, so that the use threshold of the user is greatly improved, and beginners or users without operation bases can simply and directly get off; in addition, the collection of the user characteristics is completed based on the internet, but the collection of the characteristics in the two aspects of age group and gender is generally limited in the online process, the judgment accuracy is fuzzy, and the real requirements of the user are not comprehensively considered from multiple aspects. Therefore, for offline advertisement placement, providing customized product advertisements based on comprehensive crowd characteristics is a problem that advertisers are keenly concerned about.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the objectives of the present invention is to provide an advertisement push method based on the crowd characteristics, which can provide more accurate product advertisements according to the user characteristics in combination with the crowd characteristic analysis.
One of the purposes of the invention is realized by adopting the following technical scheme:
an advertisement pushing method based on crowd characteristics comprises the following steps: and (3) product classification binding: acquiring a video image of a user crowd watching a product advertisement for more than a set time period, extracting user characteristics of the user crowd, and binding product classification to which the product advertisement belongs with common user characteristics of the user crowd; user feature extraction: acquiring a user video image, and extracting user characteristics of a user in the video image; and (3) advertisement pushing step: and determining common user characteristics corresponding to the user characteristics of the user, and pushing product advertisements under the product categories bound with the common user characteristics to the user for watching.
Further, in the product classification binding step, the user characteristics of each user of the user population are obtained, and a plurality of identical characteristic contents are extracted as the common user characteristics of the user population.
Further, when the number of the acquired user characteristics of the user group exceeds a set value, all the user characteristics are grouped, common user characteristics are determined group by group, intersection processing is performed on the common user characteristics of all the groups, and a plurality of same characteristic contents are extracted to serve as the common user characteristics of the user group.
Further, the user characteristics include age, gender, hair style, peer, and wearing apparel characteristics. Further, the user characteristics determine the classification of each characteristic content of the user characteristics by comparing with the user characteristic database.
Further, the user characteristic database comprises an age database, a gender database, a hair style database, a fellow staff database and a wearing clothes database.
Further, the user characteristics are obtained through user face information, the number of users and the shape of the users.
Further, extracting user characteristics from the obtained video image, and if the user characteristics have corresponding common user characteristics, directly executing the advertisement pushing step; and if the user characteristics do not have the corresponding common user characteristics, executing the product classification binding step.
Another object of the present invention is to provide an electronic device, which can provide more accurate product advertisement according to user characteristics and by combining with crowd characteristic analysis.
One of the purposes of the invention is realized by adopting the following technical scheme:
an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a demographics-based advertisement push method according to one of the objects of the invention when executing the program.
It is a further object of the present invention to provide a storage medium that can provide more accurate product advertising based on user characteristics in combination with demographic analysis.
The third purpose of the invention is realized by adopting the following technical scheme:
a computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements a demographics-based advertisement push method as one of the objects of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
according to the advertisement pushing method based on the crowd characteristics, the electronic equipment and the storage medium, the common user characteristics of the user crowd are bound with the product classification to which the product advertisement watched by the user crowd belongs, and then the product advertisement under the product classification bound with the common user characteristics can be pushed to the user with the corresponding user characteristics according to the common user characteristics, so that the product advertisement is pushed more accurately, and the pertinence of advertisement pushing is effectively improved.
Drawings
Fig. 1 is a schematic flow chart of an advertisement push method based on crowd characteristics.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
The first embodiment is as follows:
an embodiment provides an advertisement push method based on crowd characteristics, as shown in fig. 1, including the following steps:
s1: acquiring a video image of a user crowd watching a product advertisement for more than a set time period, extracting user characteristics of the user crowd, and binding product classification to which the product advertisement belongs with common user characteristics of the user crowd;
s2: acquiring a user video image, and extracting user characteristics of a user in the video image;
s3: and determining common user characteristics corresponding to the user characteristics of the user, and pushing product advertisements under the product categories bound with the common user characteristics to the user for watching.
In the online product exhibition and sale area, firstly, user video images collected by a camera are obtained, user characteristic information is extracted through the user video images, at the moment, if the user characteristics have corresponding common user characteristics, the advertisement pushing step is directly executed, and corresponding product advertisements are displayed to the user through a display screen for watching; and if the user characteristics do not have the corresponding common user characteristics, executing the product classification binding step. In the product classification binding step, for each user watching a product advertisement displayed on a display screen and exceeding a preset time period, a camera shoots the user advertisement, user characteristics are extracted from the user image by acquiring the user image of the camera, and then a plurality of identical characteristic contents existing in each user characteristic of a user crowd are extracted to serve as common user characteristics of the user crowd. It should be noted that after the extracted user features of the user population reach the set number, the common user features can be extracted, and the product classification to which the product advertisement belongs is bound with the common user features of the user population. If the feature content of the extracted user feature has the same portion as the common user feature, the advertisement push step is performed at S3. In addition, the establishment of the common user characteristics depends on the characteristic contents with the same characteristics of all the users of the user group, the number of the same characteristic contents meeting the establishment of the common user characteristics can be set by self-definition, and the embodiment determines that the three or more same characteristic contents are the common user characteristics when the characteristic contents meet three or more. When the number of the acquired user characteristics of the user crowd exceeds a set value, all the user characteristics are grouped, common user characteristics are determined group by group, intersection processing is carried out on the common user characteristics of all the groups, and a plurality of same characteristic contents are extracted to serve as the common user characteristics of the user crowd. In order to obtain more accurate common user features, a certain number of user features are required, if the common features of a plurality of user features are directly extracted, the common user features may not exist, the common user features are firstly grouped and extracted, and then the intersection processing is performed on the common user features after the common user features are extracted, so that the final common user features are obtained.
The user characteristics of the present embodiment include age, gender, hair style, peer, and wearing apparel characteristics. The user characteristics are obtained through user face information, the number of users and the appearance of the users. And determining the classification of each characteristic content of the user characteristics by comparing the user characteristics with the user characteristic database. The user characteristic database comprises an age database, a gender database, a hair style database, a fellow staff database and a wearing clothes database. The database establishment method is as follows:
1. and (5) establishing an age database.
Through a neural network algorithm, the users are divided into four age groups of '15-25 years old, 25-35 years old, 35-45 years old and over 45 years old', and the users are classified according to the attributes of the age groups. The method comprises the steps of acquiring a large number of facial photos of users in four age groups of '15-25 years old, 25-35 years old, 35-45 years old and over 45 years old', preprocessing, feature extraction and repeated recognition training the acquired facial photos of the users, storing the extracted feature vectors corresponding to all age group intervals, and completing the establishment of a user age database of the four age groups.
2. And (4) establishing a gender database.
And identifying the facial information of the user by a face identification technology, thereby completing the establishment of a user gender database.
3. And (5) establishing a hair style database.
The method comprises the steps of obtaining a large number of pictures of various hairstyles to carry out preprocessing, feature extraction and learning training, and storing extracted feature vectors corresponding to different hairstyle types, so that the establishment of a user hairstyle database is completed.
4. And (5) establishing a peer database.
The face information of the user is identified through a face identification technology, so that the characteristics of the user and the shopping preference of the user are judged. For example, the users of pushchairs have a great deal of purchasing preference in relation to infant products.
5. And establishing a wearing clothes database.
The method comprises the steps of obtaining a large number of photos of each brand of clothes and bags for preprocessing, feature extraction and learning training, and storing the extracted feature vectors corresponding to different purchasing power intervals, so that the establishment of a purchasing power database of a user is completed.
After the database is established, the five characteristic contents of the age, the sex, the hair style, the fellow staff and the wearing clothes of the user characteristics can be identified. The identification mode of each characteristic content is as follows:
1. age identification
And extracting the face images of the crowd in the exhibition and sales market video, preprocessing, extracting characteristics, and comparing the preprocessed face information of the user with an age database to obtain the age group of the video user.
2. Gender identification
The method comprises the steps of extracting face images of crowds in an exhibition and sales market video, preprocessing, extracting characteristics, and comparing the preprocessed face information of a user with a gender database through a face recognition technology to obtain the gender of the user.
3. Hair style identification
Extracting the face images of the crowd in the exhibition and sales market video, preprocessing, extracting characteristics, and comparing the preprocessed user face information with a hair style database to obtain the hair style of the video user.
4. Peer identification
The method comprises the steps of extracting facial images of crowds in sales and exhibition venues, preprocessing, extracting characteristics, and obtaining the number of people in the same row, so that the situation that a user purchases alone or holds a baby in arms and pushes a baby carriage, carries the traveling of the old, lovers, multiple people and the like is judged, the number of people in the same row is obtained, the situations that the people in the same row all have the images, and the users and the user groups are labeled, so that the shopping preference of the users is obtained.
5. Wearable garment identification
And (3) extracting the wearing clothes of the crowd in the exhibition and sales market video, comparing the wearing clothes with a wearing clothes database, thereby obtaining a purchasing power interval corresponding to the clothes and handbag types of the user, and carrying out purchasing power classification marking on the user.
And the classification of each characteristic content of the user characteristics can be determined by comparing the user characteristics with the user characteristic database.
The establishment of the common user characteristics will be described below with reference to table 1 as an example. As shown in table 1:
TABLE 1
Table 1 shows four user characteristics of the four categories to which the determined characteristic contents belong, and if three or more identical characteristic contents are extracted by using the common user characteristics, the common user characteristics bound to the product a are as follows: 20-25 years old, female, short hair. If the user characteristics acquired from the camera include the characteristic content, the advertisement is pushed to the user corresponding to the user characteristics, and the advertisement is displayed on the display screen of the exhibition area for the user to watch.
It should be noted that after the product advertisement is pushed to the user, there may be a situation that the user is not interested in the product advertisement, the user video of the product advertisement received by a certain product classification is continuously obtained, the user base number of the user watching the display screen for more than a set time period (in this embodiment, 1.5 seconds) is extracted, the user base number is regarded as the user base number interested in the product classification, the interested user ratio is further calculated, when the interested user ratio is lower than a preset value, it is indicated that an error exists in the previous product classification binding, at this time, the product classification binding step S1 needs to be performed again, so as to ensure that the advertisement is pushed to the user more accurately.
According to the advertisement push method based on the crowd characteristics, the common user characteristics of the user crowd are bound with the product classifications to which the product advertisements watched by the user crowd belong, and then the product advertisements under the product classifications bound with the common user characteristics can be pushed to the users with the corresponding user characteristics according to the common user characteristics, so that the product advertisements are pushed more accurately, the pertinence of advertisement push is effectively improved, meanwhile, an offline marketing mode of recommending the products based on the crowd characteristics in an individualized manner is realized, the consumption enthusiasm of the users is increased, and the purchasing enthusiasm is promoted.
Example two:
the second embodiment discloses an electronic device, which includes a processor, a memory and a program, where the processor and the memory may be one or more of the above, the program is stored in the memory and configured to be executed by the processor, and when the processor executes the program, the method for pushing advertisements based on the demographic characteristics of the first embodiment is implemented.
Example three:
the third embodiment discloses a readable computer storage medium, which is used for storing a program, and when the program is executed by a processor, the advertisement push method based on the crowd characteristic in the first embodiment is realized.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (8)
1. An advertisement pushing method based on crowd characteristics is characterized by comprising the following steps:
and (3) product classification binding: acquiring a video image of a user crowd watching a product advertisement for more than a set time period, extracting user characteristics of the user crowd, acquiring the user characteristics of each user of the user crowd, extracting a plurality of same characteristic contents as common user characteristics of the user crowd, when the number of the acquired user characteristics of the user crowd exceeds a set value, grouping all the user characteristics, determining the common user characteristics group by group, performing intersection processing on the common user characteristics of each group, extracting a plurality of same characteristic contents as the common user characteristics of the user crowd, and binding the product classification to which the product advertisement belongs and the common user characteristics of the user crowd;
user feature extraction: acquiring a user video image, and extracting user characteristics of a user in the video image;
and (3) advertisement pushing step: and determining common user characteristics corresponding to the user characteristics of the user, and pushing product advertisements under the product categories bound with the common user characteristics to the user for watching.
2. The method of claim 1, wherein the advertisement push method based on the demographics is characterized in that: the user characteristics include age, gender, hairstyle, peer, and wearing apparel characteristics.
3. The method of claim 2, wherein the advertisement push method based on the demographics is characterized in that: the user characteristics are compared with a user characteristic database to determine the classification of each characteristic content of the user characteristics, wherein the user characteristic database comprises an age database, a gender database, a hair style database, a peer personnel database and a clothing database.
4. The method of claim 2, wherein the advertisement push method based on the demographics is characterized in that: the user characteristics are obtained through user face information, the number of users and the appearance of the users.
5. The method of claim 1, wherein the advertisement push method based on the demographics is characterized in that: extracting user characteristics from the obtained video image, and if the user characteristics have corresponding common user characteristics, directly executing the advertisement pushing step; and if the user characteristics do not have the corresponding common user characteristics, executing the product classification binding step.
6. The method of claim 1, wherein the advertisement push method based on the demographics is characterized in that: and after the advertisement pushing step, acquiring a video image of a user receiving the product advertisement, and when the ratio of the user group receiving the product advertisement under the product classification to the product advertisement is lower than a set value, performing the product classification binding step on the product classification again.
7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the processor executes the program to implement a crowd characteristic-based advertisement delivery method according to any one of claims 1 to 6.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements a demographics-based advertisement push method as claimed in any one of claims 1-6.
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| CN109951740A (en) * | 2019-04-03 | 2019-06-28 | 北京市商汤科技开发有限公司 | Method for processing video frequency and device, electronic equipment and storage medium |
| CN110310592A (en) * | 2019-06-28 | 2019-10-08 | 杭州勇电照明有限公司 | A kind of Intelligent LED lamp control system |
| CN111768263A (en) * | 2020-03-31 | 2020-10-13 | 北京京东尚科信息技术有限公司 | Information pushing method and device, server and storage medium |
| CN112182365A (en) * | 2020-09-11 | 2021-01-05 | 北京三快在线科技有限公司 | Recommendation information generation method and device, electronic equipment and storage medium |
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