Education enabling method and system based on all-media traffic aggregation

Abstract

The invention provides an education enabling method and system based on all-media traffic aggregation, and relates to the field of data processing, and the system comprises a content delivery module which is used for delivering interactive content on a plurality of media traffic platforms; the data acquisition module is used for acquiring behavior data of the user on the interactive content; the user screening module is used for determining candidate users based on the behavior data of the users on the interaction content; the user determination module is used for determining a target user based on the target historical behavior data of the candidate user through a first artificial intelligence model; the portrait establishing module is used for establishing a user portrait based on the target historical behavior data of the target user through a second artificial intelligence model; and the mechanism matching module is used for matching a target educational training mechanism based on the user portrait, and has the advantages of realizing traffic aggregation of a plurality of media traffic platforms, improving the mining efficiency of user demands, more accurately and comprehensively analyzing the matching degree between the user and the educational training mechanism, and enabling educational training.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an education empowerment method and system based on omnimedia traffic aggregation. Background Art

[0002] Educational training institutions include educational information for all levels from preschool to university, even doctoral or studying abroad, as well as skill training for current workers or laid-off workers. They are specialized training institutions whose main content is to provide educational resources and training information.

[0003] At present, the discovery of user needs mainly relies on offline, and the efficiency of user needs discovery is low. In addition, after discovering a user's needs, the user information is pushed to one or all matching institutions through manual matching of education and training institutions with corresponding services. The matching method is single, either sorted by time or sorted by modes such as rebate ratio. The operator assigns the user to any education and training institution or to all matching education and training institutions based on his or her manual judgment, which may cause the user to terminate the training due to inappropriate recommendations.

[0004] Therefore, it is necessary to provide an education empowerment method and system based on all-media traffic aggregation, to realize traffic aggregation of multiple media traffic platforms, to improve the efficiency of mining user needs, and to more accurately and comprehensively analyze the matching degree between users and education and training institutions, so as to empower education and training. Summary of the invention

[0005] The present invention provides an education empowerment system based on all-media traffic aggregation, including: a content delivery module, used to deliver interactive content on multiple media traffic platforms; a data acquisition module, used to obtain user behavior data on the interactive content; a user screening module, used to determine candidate users based on the user behavior data on the interactive content; the data acquisition module is also used to obtain the target historical behavior data of the candidate users from the multiple media traffic platforms; a user determination module, used to determine the target user through a first artificial intelligence model based on the target historical behavior data of the candidate users obtained from the multiple media traffic platforms; a portrait establishment module, used to establish a user portrait based on the target historical behavior data of the target user through a second artificial intelligence model; an institution matching module, used to match the target education and training institution based on the user portrait.

[0006] Furthermore, the content delivery module delivers interactive content on multiple media traffic platforms, including: for each of the media traffic platforms, performing user sampling on the media traffic platform to obtain multiple sampled users, and establishing a portrait of the media traffic platform based on the basic data and historical behavior data of the multiple sampled users; for each of the education and training institutions, establishing a portrait of the training objects corresponding to each education and training project provided by the education and training institution; for each of the media traffic platforms, based on the portrait of the training objects corresponding to each education and training project and the portrait of the media traffic platform, determining the education and training projects that match the media traffic platform, generating interactive content that matches the media traffic platform based on the education and training projects that match the media traffic platform, and delivering the interactive content on the media traffic platform.

[0007] Furthermore, the user screening module determines candidate users based on the user's behavior data on the interactive content, including: determining the user's interactive activity parameter based on the user's behavior data on the interactive content; determining the candidate users based on the user's interactive activity parameter.

[0008] Furthermore, the user screening module determines the interactive activity parameters of the user based on the user's behavioral data on the interactive content, including: obtaining the user's comment information on the interactive content based on the user's behavioral data on the interactive content; identifying the relevance of the comment information; for relevant comment information, identifying emotional parameters of the relevant comment information; and determining the user's interactive activity parameters based on the emotional parameters of the relevant comment information.

[0009] Furthermore, the data acquisition module obtains the target historical behavior data of the candidate user from the multiple media traffic platforms, including: obtaining the historical behavior data of the candidate user; extracting key features of the historical behavior data; calculating association parameters between the historical behavior data and the interactive content based on the key features of the historical behavior data; and determining whether the historical behavior data is target historical behavior data based on the association parameters between the historical behavior data and the interactive content.

[0010] Furthermore, the user determination module determines the target user through the first artificial intelligence model based on the target historical behavior data of the candidate user obtained from the multiple media traffic platforms, including: determining the matching degree between the candidate user and the training needs corresponding to the interactive content based on the target historical behavior data of the candidate user through the first artificial intelligence model; determining the target user based on the matching degree between the candidate user and the training needs corresponding to the interactive content.

[0011] Furthermore, the portrait establishment module establishes a user portrait based on the basic data and historical behavior data of the target user through a second artificial intelligence model, including: determining multiple known user portrait features of the target user based on the basic data and historical behavior data of the target user; predicting missing user portrait features based on the historical behavior data of the target user through the second artificial intelligence model; and establishing the user portrait based on the multiple known user portrait features and the predicted missing user portrait features.

[0012] Furthermore, the portrait building module predicts missing user portrait features based on the historical behavior data of the target user through the second artificial intelligence model, including: determining similar target users based on the historical behavior data of the target user; and predicting missing user portrait features based on the basic data and historical behavior data of the similar target users through the second artificial intelligence model.

[0013] Furthermore, the institution matching module matches the target education and training institution based on the user portrait, including: for each of the education and training institutions, calculating the demand matching value of the education and training institution based on the user portrait and the training object portrait corresponding to each education and training project provided by the education and training institution, calculating the location matching value of the education and training institution based on the user portrait and the address distribution information of the education and training institution, and calculating the matching priority value of the education and training institution based on the historical user evaluation information and achievement conversion information of the education and training institution; and determining the target education and training institution based on the demand matching value, location matching value and matching priority value of each of the education and training institutions.

[0014] The present invention provides an education empowerment method based on all-media traffic aggregation, including: delivering interactive content on multiple media traffic platforms; obtaining user behavior data on the interactive content; determining candidate users based on the user behavior data on the interactive content; obtaining target historical behavior data of the candidate users from the multiple media traffic platforms; determining target users based on the target historical behavior data of the candidate users obtained from the multiple media traffic platforms; establishing a user portrait based on the basic data and target historical behavior data of the target user through an artificial intelligence model; and matching target education and training institutions based on the user portrait.

[0015] Compared with the prior art, the education empowerment method and system based on omnimedia traffic aggregation provided by the present invention has at least the following beneficial effects:

[0016] 1. By collecting and analyzing the user's behavioral data on interactive content, user groups with potential interest or demand for education and training, namely candidate users, can be accurately identified. Further obtaining the target historical behavior data of candidate users will help to more deeply understand their interest preferences, learning needs and consumption habits. Target users determined based on these data are more likely to have a purchase intention for the products or services of education and training institutions, thereby improving conversion rates and user satisfaction. The user portrait established by the artificial intelligence model can fully reflect the multi-dimensional characteristics of the target users. This enables education and training institutions to provide personalized course recommendations and learning plans based on user portraits, enhance user experience, and improve user stickiness. Relying on data analysis and artificial intelligence models makes the decision-making process of education and training institutions more scientific and objective. Data-based decisions can reduce subjective assumptions and blindness, and improve the effectiveness and accuracy of decisions. With the continuous accumulation of data and continuous optimization of models, education and training institutions can continuously improve their marketing strategies, product services and user experience to adapt to market changes and changes in user needs and maintain competitive advantages.

[0017] 2. Evaluating the interactive activity based solely on simple behavioral data such as user clicks and browsing may not be comprehensive enough. By introducing the relevance and sentiment parameters of comment information, the quality of user interaction can be evaluated more comprehensively. Comments with high relevance indicate that users have in-depth thoughts and interests about the interactive content, while sentiment parameters reveal users' positive or negative attitudes, which are important indicators for evaluating user interactive activity. Users with high interactive activity are often more likely to become potential users of educational training institutions, achieving accurate screening of candidate users.

[0018] 3. By comprehensively considering the matching degree between the user portrait and the training object portrait, it can be ensured that the recommended education and training institutions can accurately meet the personalized needs and learning goals of users. This refined matching mechanism helps to improve user satisfaction and learning effects. The calculation of the location matching value allows the selection of education and training institutions to take into account not only their teaching content and quality, but also the convenience of their geographical location. This helps users choose education and training institutions that are closer to them or have more convenient transportation, reducing commuting time and costs. The introduction of historical user evaluation information and achievement conversion information provides an objective and comprehensive reference for the screening of education and training institutions. These indicators can reflect the actual teaching quality, student satisfaction and achievement conversion rate of education and training institutions, thereby helping users to choose more reliable and efficient education and training institutions. Comprehensively considering the matching values ​​of multiple dimensions to determine the target education and training institutions can provide users with a more personalized and considerate service experience. Users can not only find learning content that suits them, but also receive high-quality teaching services in a convenient location, thereby improving the overall learning experience and satisfaction. Through an open and transparent matching mechanism, education and training institutions need to continuously improve their teaching quality, improve service facilities, and optimize geographical location layout to attract more users and increase matching priority values. This competition mechanism helps promote the healthy development and improvement of the entire education and training industry. Determining the target education and training institutions based on multi-dimensional matching values ​​helps to achieve the optimal allocation of educational resources. Users can choose the most suitable education and training institutions according to their needs and conditions, and education and training institutions can also attract more matching user groups based on their own advantages and characteristics, thus achieving a win-win situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0020] Figure 1 It is a module schematic diagram of an education empowerment system based on all-media traffic aggregation according to some embodiments of this specification;

[0021] Figure 2 It is a flow chart of an education empowerment method based on all-media traffic aggregation as shown in some embodiments of this specification. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0023] Figure 1 is a module diagram of an education empowerment system based on all-media traffic aggregation according to some embodiments of this specification, such as Figure 1 As shown, the education empowerment system based on all-media traffic aggregation can include a content delivery module, a data acquisition module, a user screening module, a user determination module, a portrait creation module and an institution matching module.

[0024] The content delivery module can be used to deliver interactive content on multiple media traffic platforms.

[0025] The media traffic platform may be a platform with user social functions, such as Tik Tok, Xiao Hong Shu, Kuaishou, etc.

[0026] Specifically include:

[0027] For each media traffic platform, perform user sampling on the media traffic platform to obtain multiple sampled users, and build a media traffic platform profile based on the basic data and historical behavior data of the multiple sampled users;

[0028] For each education and training institution, establish a training target profile corresponding to each education and training program provided by the education and training institution;

[0029] For each media traffic platform, based on the training target portrait and media traffic platform portrait corresponding to each education and training project, determine the education and training projects that match the media traffic platform. Based on the education and training projects that match the media traffic platform, generate interactive content that matches the media traffic platform, and deliver the interactive content on the media traffic platform.

[0030] Specifically, the basic data of the sampled users may be personal data that is disclosed on the media traffic platform with the user's consent or authorization, such as age, gender, region, occupation, etc. The historical behavior data of the sampled users may be behavior data that is disclosed on the media traffic platform with the user's consent or authorization. For example, browsing history, liked content, favorited content, commented content, published content, etc. The content delivery module can randomly select a certain number of users from all users of the media traffic platform as sampled users. This method can reduce sampling errors and make the results more representative. The basic data and historical behavior data of the sampled users are obtained from the media traffic platform through the API (Application Programming Interface) interface, data export function or crawler of the media traffic platform.

[0031] The content delivery module can collect statistics and analyze basic attributes of sampled users, such as age, gender, region, occupation, etc., to determine the age distribution, gender ratio, regional distribution, occupation distribution and marital and childbearing status of the media traffic platform. Among them, the age distribution can represent the proportion of users in each age group, the regional distribution can represent the proportion of users in each region, the occupation distribution can represent the proportion of users in each occupation, and the marital and childbearing status can represent the proportion of users who are unmarried and non-pregnant, married and childless, and married and have children.

[0032] The content delivery module can analyze the interest preferences of the sampled users based on the historical behavior data of the sampled users. For example, the interest preferences of the users can be analyzed based on the historical behavior data of the sampled users through a data analysis model, wherein the data analysis model can be a Convolutional Neural Network (CNN) model.

[0033] For each media traffic platform, the content delivery module can perform statistical analysis on the interest preferences of sampled users to determine the distribution of interest points corresponding to the media traffic platform, wherein the distribution of interest points can represent the proportion of users interested in each interest point.

[0034] For each media traffic platform, the content delivery module can determine the content form distribution corresponding to the media traffic platform based on the historical behavior data of sampled users, where the content form distribution can represent the proportion of various content forms (for example, text, image, video, etc.).

[0035] The media traffic platform portrait may include information such as age distribution, gender ratio, regional distribution, occupational distribution, marital status, interest point distribution and content form distribution.

[0036] For each education and training program provided by an education and training institution, the content delivery module can determine the points of interest corresponding to the education and training program (e.g., art and crafts, music and dance, technology and programming, sports and fitness, language learning, baking and cooking, parent-child education, etc.). For example, the points of interest corresponding to the education and training program can be determined based on the program information of the education and training program (e.g., course information, teaching material information, etc.) through a point of interest determination model. The point of interest determination model can be a convolutional neural network model.

[0037] The training target portrait corresponding to the education and training project includes interest point information, and may also include age group information, occupation information, etc. The age group information, occupation information, etc. may be determined based on the basic data of users who are currently in or have completed the education and training project.

[0038] For each media traffic platform, the content delivery module can determine the education and training projects that match the media traffic platform based on the training target portrait and media traffic platform portrait corresponding to each education and training project through the project matching model, wherein the project matching model can be a convolutional neural network model. According to the distribution of content forms corresponding to the media traffic platform, the optimal content form is determined, and based on the optimal content form and the project information of the education and training project, the interactive content that matches the media traffic platform is generated, wherein the optimal content form can be the content form with the highest proportion.

[0039] The data acquisition module can be used to obtain user behavior data on interactive content.

[0040] Specifically, the data acquisition module can obtain the user's behavioral data on interactive content with the user's consent or authorization through the API interface, data export function or crawler of the media traffic platform, such as the number of views, likes, favorites, comments, and forwarding.

[0041] The user screening module can be used to determine candidate users based on the user's behavioral data on interactive content.

[0042] Specifically include:

[0043] Determine the user's interactive activity parameters based on the user's behavioral data on interactive content;

[0044] Determine candidate users based on the user's interactive activity parameters.

[0045] Specifically, the first activity parameter of the user for the interactive content may be calculated based on the number of types of interactive behaviors of the user for the interactive content:

[0046]

[0047] Among them, P (1,i)is the first activity parameter of the i-th user for the interactive content, N i is the number of types of interactive behaviors of the i-th user on the interactive content, N i The total number of preset interaction behavior types.

[0048] For example, the types of interactive behaviors include browsing, liking, collecting, commenting, and forwarding, and the user's interactive behaviors for interactive content include browsing and liking, then the user's first activity parameter for the interactive content is 2 / 5.

[0049] In some embodiments, the user screening module determines the user's interactive activity parameters based on the user's behavioral data on the interactive content, including:

[0050] Based on the user's behavioral data on the interactive content, obtain the user's comment information on the interactive content;

[0051] Identify the relevance of review information;

[0052] For relevant comment information, identifying sentiment parameters of the relevant comment information;

[0053] Based on the sentiment parameters of relevant comment information, the user's interactive activity parameters are determined.

[0054] Specifically, the user screening module can perform text cleaning on the comment information, remove special characters, invalid spaces and other noise data in the comment information, and use the Chinese word segmentation tool to perform word segmentation on the comment information after noise removal, and remove stop words to reduce redundant information, thereby obtaining multiple valid comment words in the comment information.

[0055] For each interactive content, the user screening module may determine a plurality of comment keywords corresponding to the interactive content through a keyword determination model, wherein the keyword determination model may be a BERT (Bidirectional Encoder Representations from Transformers) model.

[0056] For each valid review word, the semantic distance between the valid review word and each review keyword is calculated to determine the shortest semantic distance corresponding to the valid review word.

[0057] Based on the shortest semantic distance corresponding to each valid comment word, the relevance parameter of the comment information is determined:

[0058]

[0059] Among them, r (i,j) is the relevance parameter of the jth comment information of the i-th user, a 11 、a 12and a 13 are all preset weights, and a 11 +a 12 +a 13 =1,a 11 >0,a 12 >0,a 13 >0,N j is the number of valid comment words in the jth comment information, N0 is the preset number of valid comment words, P (i,j) is the ratio of valid comment words in the jth comment information, P0 is the preset ratio of valid comment words, μ j is the shortest semantic distance mean of the effective comment words of the jth comment information, μ0 is the preset shortest semantic distance mean, σ0 is the preset shortest semantic distance variance, σ i is the shortest semantic distance variance of the effective comment words of the jth comment information, L (j,effective) is the sum of the lengths of the valid comment words in the jth comment information, L j is the length of the jth comment in characters, D (j,n) is the shortest semantic distance corresponding to the nth valid comment word of the jth comment information, and N is the total number of valid comment words included in the jth comment information.

[0060] When the relevance parameter of the comment information is greater than a preset relevance parameter threshold, the comment information is relevant.

[0061] The emotion recognition model can be used to identify the emotion features of the relevant comment information based on multiple valid comment words of the relevant comment information, wherein the emotion features can include the matching probability corresponding to each emotion (e.g., happy, sad, angry, calm, etc.). The emotion recognition model can be a long short-term memory network model.

[0062] The state update of the long short-term memory network model can be described by the following mathematical formula:

[0063] f t =σ(W f ·[h t―1 ,x t ]+b f )

[0064] i t =σ(W i ·[h t―1 ,x t ]+b i )

[0065] C t =tanh(W f ·[h t―1 ,x t ]+bC )

[0066] C t =f t *C t―1 +i t *C t )

[0067] o t =σ(W o ·[h t―1 ,x t ]+b o )

[0068] h t =o t *tanh(C t )

[0069] Among them, f t 、i t and t Represent the activation values ​​of the forget gate, input gate, and output gate respectively, h t―1 is the hidden state of the previous time step, x t is the input of the current time step, C t is the internal state of the current unit, C t is a candidate state, W f , W i , W o and b f 、b i 、b c 、b o is the network parameter, σ is the sigmoid activation function, * represents the product between elements, and tanh() is the hyperbolic tangent function.

[0070] During the training of the LSTM network model, the network parameters of the LSTM network model can be adjusted through the back propagation algorithm:

[0071]

[0072] m t =β1m t―1 +(1―β1)g t

[0073] v t =β2v t―1 +(1―β2)g t 2

[0074] Among them, θ t+1 is the parameter of the LSTM network model at time step t+1, θ tis the parameter of the long short-term memory network model at time step t, η is the learning rate, m t is the moving average of the gradient at time step t, v t is the moving average of the squared gradient at time step t, ε is a constant, β1 is the decay rate, m t―1 is the moving average of the gradient at time step t-1, g t is the gradient, β2 is the decay rate, v t―1 is the moving average of the squared gradient at time step t-1.

[0075] The sentiment parameters of the relevant comment information can be calculated based on the sentiment characteristics of the relevant comment information according to the following formula:

[0076]

[0077] Among them, E j is the sentiment parameter of the jth relevant comment information, a 21 and a 22 are all preset weights, and a 21 +a 22 =1,a 21 >0,a 22 >0, V m is the emotion value corresponding to the mth emotion, for example, happiness is 3, disappointment is 1, P (j,m) is the matching probability of the mth emotion in the emotional features of the jth relevant comment information, and M is the total number of emotion types.

[0078] The second activity parameter of the user for the interactive content can be calculated based on the relevance parameter and sentiment parameter of the relevant comment information according to the following formula:

[0079] P (2,i) =max(a 31 × j +a 32 ×E j ),j=1,2…J

[0080] Among them, P (2,i) is the second activity parameter of the i-th user for the interactive content, a 31 and a 32 are all preset weights, and a 31 +a 32 =1,a 31 >0,a 32 >0, J is the total number of relevant comment information of the ith user on the interactive content, and max() is the maximum value operation.

[0081] In some embodiments, the user screening module determines the user's interactive activity parameter based on the user's behavioral data on the interactive content, and may also include:

[0082] Based on the user's behavioral data on the interactive content, obtain the user's forwarding information on the interactive content;

[0083] Identify the relevance parameters of the user's forwarding information on the interactive content;

[0084] Identify the sentiment parameters of users’ forwarded information about interactive content;

[0085] Based on the relevance parameter and the emotion parameter of the user's forwarding information of the interactive content, a third activity parameter of the user is determined.

[0086] Specifically, the user screening module can perform text cleaning on the user's forwarded information on interactive content, remove noise data such as special characters and invalid spaces in the forwarded information, and use a Chinese word segmentation tool to perform word segmentation on the forwarded information after noise removal, and remove stop words to reduce redundant information, thereby obtaining multiple valid comment words for the forwarded information.

[0087] Based on multiple valid comment words of the forwarded information, the relevance parameter and sentiment parameter of the user's forwarded information on the interactive content are calculated.

[0088] The method of calculating the relevance parameters and sentiment parameters of the user's forwarding information on the interactive content is similar to the method of calculating the relevance parameters and sentiment parameters of the relevant comment information, and will not be repeated here.

[0089] The user's interactive activity parameter is calculated based on the following formula:

[0090] P i =a 41 ×P (1,i) +a 42 ×P (2,i) +a 43 ×P (3,i)

[0091] Among them, P i is the interactive activity parameter of the i-th user, a 41 、a 42 and a 43 are all preset weights, and a 41 +a 42 +a 43 =1,a 41 >0,a 42 >0,a 43 >0,P (3,i) is the third activity parameter of the i-th user for the interactive content.

[0092] The data acquisition module can also be used to obtain target historical behavior data of candidate users from multiple media traffic platforms.

[0093] Specifically include:

[0094] Obtain the historical behavior data of candidate users, such as the content they liked, the content they collected, the content they published, the content they forwarded, etc.

[0095] Extract key features of historical behavior data, where the key features of historical behavior data may include words of content corresponding to the historical behavior. Text cleaning may be performed on the content corresponding to the historical behavior to remove noise data such as special characters and invalid spaces in the content corresponding to the historical behavior. A Chinese word segmentation tool may be used to perform word segmentation on the content corresponding to the historical behavior after noise removal, and stop words may be removed to reduce redundant information, thereby obtaining multiple valid words of the content corresponding to the historical behavior.

[0096] Based on the key features of historical behavior data, calculate the correlation parameters between historical behavior data and interactive content;

[0097] Based on the correlation parameters between the historical behavior data and the interactive content, it is determined whether the historical behavior data is the target historical behavior data.

[0098] Specifically, for each valid word in the content corresponding to the historical behavior, the semantic distance between the valid word and each valid word in the interactive content is calculated to determine the shortest semantic distance corresponding to the valid word. The association parameter between the historical behavior data and the interactive content is calculated based on the shortest semantic distance of each valid word in the content corresponding to the historical behavior according to the following formula:

[0099]

[0100] Among them, r (i,e) is the correlation parameter between the e-th historical behavior data and interactive content of the i-th candidate user, D ((i,e),f) is the shortest semantic distance corresponding to the fth valid word in the content corresponding to the eth historical behavior of the ith candidate user, and F is the total number of valid words in the content corresponding to the eth historical behavior of the ith candidate user.

[0101] When the correlation parameter between the historical behavior data and the interactive content is greater than a preset correlation parameter threshold, the historical behavior data is determined to be target historical behavior data.

[0102] The user determination module can be used to determine the target user based on the target historical behavior data of the candidate users obtained from multiple media traffic platforms through the first artificial intelligence model.

[0103] Specifically include:

[0104] Determining the matching degree between the candidate user and the training requirements corresponding to the interactive content through a first artificial intelligence model based on the target historical behavior data of the candidate user, wherein the first artificial intelligence model may be a Transformer model;

[0105] Based on the matching degree between the candidate users and the training requirements corresponding to the interactive content, the target users are determined. For example, the candidate users whose matching degree is greater than a preset matching degree threshold are taken as the target users.

[0106] The portrait building module can be used to build a user portrait based on the target user's basic data and target historical behavior data through a second artificial intelligence model.

[0107] Specifically include:

[0108] Based on the target user's basic data and historical behavior data, determine multiple known user profile features of the target user, such as age, occupation, living area, etc.;

[0109] Predicting missing user portrait features (e.g., occupation, etc.) based on historical behavior data of the target user through a second artificial intelligence model, wherein the second artificial intelligence model may be a convolutional neural network model;

[0110] Create a user profile based on multiple known user profile features and predicted missing user profile features.

[0111] In some embodiments, the portrait building module predicts missing user portrait features based on the historical behavior data of the target user through the second artificial intelligence model, including:

[0112] Identify similar target users based on the target user’s historical behavior data;

[0113] The second artificial intelligence model is used to predict missing user portrait features based on the basic data and historical behavior data of similar target users.

[0114] Specifically, the behavior similarity between two target users can be determined based on the historical behavior data of the two target users through a similarity determination model. When the behavior similarity between the two target users is greater than a preset behavior similarity threshold, the two target users can be similar target users to each other. The similarity determination model can be a convolutional neural network model.

[0115] The institution matching module can be used to match target education and training institutions based on user portraits.

[0116] Specifically include:

[0117] For each education and training institution, the demand matching value of the education and training institution is calculated based on the user portrait and the training target portrait corresponding to each education and training project provided by the education and training institution. The location matching value of the education and training institution is calculated based on the user portrait and the address distribution information of the education and training institution. The matching priority value of the education and training institution is calculated based on the historical user evaluation information and achievement transformation information of the education and training institution.

[0118] Determine the target education and training institutions based on the demand matching value, location matching value and matching priority value of each education and training institution.

[0119] Specifically, the demand matching value of the education and training institution can be calculated by the demand matching prediction model based on the user portrait and the training object portrait corresponding to each education and training project provided by the education and training institution. The demand matching prediction model can be a convolutional neural network model.

[0120] The information on the transformation of results can represent the growth of users’ relevant skills after they have received training from educational and training institutions.

[0121] The institution matching module can perform sentiment analysis on historical user evaluation information, and calculate the matching priority value of education and training institutions based on the sentiment analysis results and achievement transformation information of historical user evaluation information.

[0122] The demand matching value, location matching value and matching priority value of the education and training institution may be weighted and summed to determine the matching value of the education and training institution, and the education and training institution with the largest matching value may be selected as the target education and training institution.

[0123] Figure 2 is a flowchart of an education empowerment method based on all-media traffic aggregation according to some embodiments of this specification, such as Figure 2 As shown, the education empowerment method based on all-media traffic aggregation may include the following steps.

[0124] Step 210, delivering interactive content on multiple media traffic platforms;

[0125] Step 220, obtaining user behavior data on interactive content;

[0126] Step 230, determining candidate users based on the user's behavior data on the interactive content;

[0127] Step 240, obtaining target historical behavior data of candidate users from multiple media traffic platforms;

[0128] Step 250, determining a target user based on target historical behavior data of candidate users obtained from multiple media traffic platforms;

[0129] Step 260, establishing a user profile based on the basic data of the target user and the target historical behavior data through an artificial intelligence model;

[0130] Step 270, matching target education and training institutions based on user portraits.

[0131] The education empowerment method based on all-media traffic aggregation can be executed by the education empowerment system based on all-media traffic aggregation. For more descriptions of the education empowerment method based on all-media traffic aggregation, please refer to the relevant descriptions of the education empowerment system based on all-media traffic aggregation, which will not be repeated here.

[0132] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. Education empowerment system based on all-media traffic aggregation, It is characterized in that include: Content delivery module, used to deliver interactive content on multiple media traffic platforms; A data acquisition module, used to acquire user behavior data on the interactive content; A user screening module, used to determine candidate users based on the user's behavior data on the interactive content; The data acquisition module is also used to acquire the target historical behavior data of the candidate user from the multiple media traffic platforms; A user determination module, configured to determine a target user based on the target historical behavior data of the candidate users obtained from the multiple media traffic platforms through a first artificial intelligence model; A portrait building module, used to build a user portrait based on the basic data of the target user and the target historical behavior data through a second artificial intelligence model; The institution matching module is used to match the target education and training institution based on the user portrait.

2. According to claim 1, the education empowerment system based on all-media traffic aggregation, It is characterized in that The content delivery module delivers interactive content on multiple media traffic platforms, including: For each of the media traffic platforms, user sampling is performed on the media traffic platform to obtain a plurality of sampled users, and a portrait of the media traffic platform is established based on basic data and historical behavior data of the plurality of sampled users; For each of the education and training institutions, a training target profile corresponding to each education and training program provided by the education and training institution is established; For each of the media traffic platforms, based on the training target portrait corresponding to each education and training project and the media traffic platform portrait, the education and training projects matching the media traffic platform are determined; based on the education and training projects matching the media traffic platform, interactive content matching the media traffic platform is generated, and the interactive content is delivered on the media traffic platform.

3. According to claim 1, the education empowerment system based on all-media traffic aggregation, It is characterized in that The user screening module determines candidate users based on the user's behavior data on the interactive content, including: Determining an interactive activity parameter of the user based on the user's behavioral data on the interactive content; The candidate user is determined based on the interactive activity parameter of the user.

4. According to claim 3, the education empowerment system based on all-media traffic aggregation, It is characterized in that The user screening module determines the interactive activity parameter of the user based on the user's behavior data on the interactive content, including: Based on the user's behavior data on the interactive content, obtaining the user's comment information on the interactive content; Identifying the relevance of the review information; For relevant comment information, identifying sentiment parameters of the relevant comment information; Based on the sentiment parameters of the relevant comment information, the interactive activity parameters of the user are determined.

5. According to any one of claims 1 to 4, the education empowerment system based on all-media traffic aggregation, It is characterized in that The data acquisition module acquires the target historical behavior data of the candidate user from the multiple media traffic platforms, including: Obtaining historical behavior data of the candidate user; Extracting key features of the historical behavior data; Based on the key features of the historical behavior data, calculating association parameters between the historical behavior data and the interactive content; Based on the association parameters between the historical behavior data and the interactive content, it is determined whether the historical behavior data is target historical behavior data.

6. The education empowerment system based on all-media traffic aggregation according to any one of claims 1 to 4, It is characterized in that The user determination module determines the target user through the first artificial intelligence model based on the target historical behavior data of the candidate users obtained from the multiple media traffic platforms, including: Determining, by the first artificial intelligence model and based on the target historical behavior data of the candidate user, a matching degree between the candidate user and the training needs corresponding to the interactive content; The target user is determined based on the matching degree between the candidate user and the training needs corresponding to the interactive content.

7. The education empowerment system based on all-media traffic aggregation according to any one of claims 1 to 4, It is characterized in that The portrait building module builds a user portrait based on the basic data and historical behavior data of the target user through a second artificial intelligence model, including: Based on the basic data and historical behavior data of the target user, determine multiple known user portrait features of the target user; Predicting missing user portrait features based on the historical behavior data of the target user by the second artificial intelligence model; The user profile is established based on the multiple known user profile features and the predicted missing user profile features.

8. The education empowerment system based on all-media traffic aggregation according to any one of claims 1 to 4, It is characterized in that The portrait building module predicts missing user portrait features based on the historical behavior data of the target user through the second artificial intelligence model, including: Determine similar target users based on the historical behavior data of the target user; The second artificial intelligence model predicts missing user portrait features based on the basic data and historical behavior data of the similar target users.

9. The education empowerment system based on all-media traffic aggregation according to any one of claims 1 to 4, It is characterized in that The institution matching module matches the target education and training institution based on the user portrait, including: For each of the education and training institutions, based on the user portrait and the training target portrait corresponding to each education and training program provided by the education and training institution, the demand matching value of the education and training institution is calculated; based on the user portrait and the address distribution information of the education and training institution, the location matching value of the education and training institution is calculated; based on the historical user evaluation information and achievement conversion information of the education and training institution, the matching priority value of the education and training institution is calculated; The target education and training institution is determined based on the demand matching value, location matching value and matching priority value of each of the education and training institutions.

10. Education empowerment method based on all-media traffic aggregation, It is characterized in that include: Deliver interactive content on multiple media traffic platforms; Obtaining user behavior data on the interactive content; Determine candidate users based on the user's behavior data on the interactive content; Acquire target historical behavior data of the candidate user from the multiple media traffic platforms; Determine a target user based on the target historical behavior data of the candidate users obtained from the multiple media traffic platforms; Establishing a user profile based on the basic data of the target user and the target historical behavior data through an artificial intelligence model; Based on the user portrait, match the target education and training institution.

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