# Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

Authors: Feifeng Huang
Journal: Journal of Sensors
 (2021)

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## Abstract

With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the mainF of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.


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## Body

## 1. Introduction

### 1.1. Background

With the development of modern information technology, personalized marketing transaction costs have been greatly reduced, and at the same time, real-time interaction between enterprises and customers has become possible, so personalized marketing has begun to become a competitive marketing method. In 2009, the release of the Vine short video application in the United States was the starting point for large-scale social software to release short video functions. With the advent of the 4G era, the numbers of domestic short video users, such as Meipai, Miaopai, Watermelon, Kuaishou, and Pear Video, have exploded due to the acceleration of the Internet and the reduction of prices. In this case, many companies and individuals have begun to use short videos for marketing, and short videos have become the “standard configuration” of mobile Internet marketing. The process of making short films is simpler, and the barriers to participating in production are lower, which can attract more users to use and share social networks. Short videos are still part of the program but are very useful for communication and dialogue. The basic communication function of short videos is the best among many marketing methods. As the application of short video marketing on social media becomes more and more common, issues related to short video marketing on social media will also be worth discussing. As a new method system, the artificial neural network has the characteristics of distributed parallel processing, nonlinear mapping, adaptive learning, strong robustness, and fault tolerance, which makes it effective in pattern recognition, control optimization, intelligent information processing, and faults. Diagnosis and other aspects have a wide range of applications.


### 1.2. Significance

In today’s new era of fast mobile Internet technology, discussing simple video marketing strategies based on the ideas of mobile Internet is not only a development and supplement to existing marketing theories but also a company’s personal practice, which can also stimulate marketing. Therefore, this article has theoretical and practical guidance. In theory, short video marketing, as a new form of marketing that emerged this year with the development of the mobile Internet, has attracted a lot of attention in academia, but researchers have seen short video marketing and short videos on social media. The internal research on marketing has just begun, and a systematic theoretical system has not yet been formed. Basically, related research is still in its infancy. Data fusion plays an indispensable role in the deep neural network data fusion new media short video personalized marketing recommendation system Therefore, as an exploratory research, this article will enhance the existing theory to a certain extent. From a practical point of view, the advent of the mobile Internet era will affect the traditional business marketing model of enterprises. Challenges were raised. Short film marketing is a new method of mobile Internet marketing, but it is a necessary step in the formation of corporate and personal marketing strategies. Therefore, more and more marketers have begun to carry out short film marketing activities on social media, and the theory of short film marketing research is necessary. A neural network is a highly complex nonlinear dynamic system composed of a large number of processing units with very simple structures and functions, that is, neurons are widely interconnected [1]. Based on the theory of an artificial neural network and using a single hidden layer neural network structure algorithm, a neural network model for marketing mix decision-making is established.


### 1.3. Related Work

In the era of new media, with the rapid development of the Internet, mobile phones have replaced PCs (personal computers) as the main tool for obtaining information. Xu et al.’s research found that short videos are unique in social networks, and their advantage is that the information release mode is concise and clear. And put forward the advantages of college students using new media to develop short video marketing [2]. However, because the experimental environment is not closed, there is a certain deviation in the experimental results. The field of marketing research is very important for the sale of goods. Soria Morillo et al. applied a new method to the field of marketing research. He recognized how the brain activity responds to the visualization of short video ads using discrete classification technology. Through low-cost electroencephalography equipment (EEG), the activation level of some brain regions was studied, and advertisements were displayed to users. You may want to know which is the use of neuroscience knowledge in marketing or can provide neuroscience to the marketing department, or why this method can improve accuracy and end-user acceptance compared to other works. Soria Morillo et al. used discretization technology on the EEG frequency band of the generated data set. C4.5, ANN, and a new recognition system based on discretization algorithm Ameva are applied to obtain the score of each TV ad by the topic. The proposed technique allows an accuracy of more than 75% to be achieved, that is, an excellent result considering the type of EEG sensor used in this work [3]. Although it was in line with expectations in most directions, it did not mention EEG factors due to external environmental factors. In some parts of the forecast, there are errors. In order to study the intrinsic link between the perceived value of mobile short videos and customers’ purchase intentions, Xu et al. explored the potential key variables between the two. And based on the mediating role of user participation and attitudes, it is an early attempt to propose a conceptual framework to understand the impact of perceived value on Chinese consumers’ purchasing intentions. This research uses a quantitative design to collect data from 622 users of China mobile’s short video social application who have experience in mobile short video-related social applications through the snowball sampling method by developing an online questionnaire. In addition, in this study, user participation behavior has a positive impact on consumer attitudes [2]. Although the research perspective is forward-looking, there are still many unachievable parts of the technology.


### 1.4. Innovation

The innovations of this paper are as follows: (1) In order to solve the problem of the low proportion of users watching short video lists after sorting, considering that the convolutional neural network has the function of extracting local features, it can extract key information from sentences, such as N-gram. This feature can also be used. It is transplanted to the feature extraction of the text information of the video. (2) Although the traditional proposition theory focuses on the innovation of the algorithm level of the proposition, this paper not only focuses on the innovation of the algorithm level but also explores the architecture design of the proposition platform and proposes an early stage of the platform, proposed corresponding solutions.


## 1.1. Background

With the development of modern information technology, personalized marketing transaction costs have been greatly reduced, and at the same time, real-time interaction between enterprises and customers has become possible, so personalized marketing has begun to become a competitive marketing method. In 2009, the release of the Vine short video application in the United States was the starting point for large-scale social software to release short video functions. With the advent of the 4G era, the numbers of domestic short video users, such as Meipai, Miaopai, Watermelon, Kuaishou, and Pear Video, have exploded due to the acceleration of the Internet and the reduction of prices. In this case, many companies and individuals have begun to use short videos for marketing, and short videos have become the “standard configuration” of mobile Internet marketing. The process of making short films is simpler, and the barriers to participating in production are lower, which can attract more users to use and share social networks. Short videos are still part of the program but are very useful for communication and dialogue. The basic communication function of short videos is the best among many marketing methods. As the application of short video marketing on social media becomes more and more common, issues related to short video marketing on social media will also be worth discussing. As a new method system, the artificial neural network has the characteristics of distributed parallel processing, nonlinear mapping, adaptive learning, strong robustness, and fault tolerance, which makes it effective in pattern recognition, control optimization, intelligent information processing, and faults. Diagnosis and other aspects have a wide range of applications.


## 1.2. Significance

In today’s new era of fast mobile Internet technology, discussing simple video marketing strategies based on the ideas of mobile Internet is not only a development and supplement to existing marketing theories but also a company’s personal practice, which can also stimulate marketing. Therefore, this article has theoretical and practical guidance. In theory, short video marketing, as a new form of marketing that emerged this year with the development of the mobile Internet, has attracted a lot of attention in academia, but researchers have seen short video marketing and short videos on social media. The internal research on marketing has just begun, and a systematic theoretical system has not yet been formed. Basically, related research is still in its infancy. Data fusion plays an indispensable role in the deep neural network data fusion new media short video personalized marketing recommendation system Therefore, as an exploratory research, this article will enhance the existing theory to a certain extent. From a practical point of view, the advent of the mobile Internet era will affect the traditional business marketing model of enterprises. Challenges were raised. Short film marketing is a new method of mobile Internet marketing, but it is a necessary step in the formation of corporate and personal marketing strategies. Therefore, more and more marketers have begun to carry out short film marketing activities on social media, and the theory of short film marketing research is necessary. A neural network is a highly complex nonlinear dynamic system composed of a large number of processing units with very simple structures and functions, that is, neurons are widely interconnected [1]. Based on the theory of an artificial neural network and using a single hidden layer neural network structure algorithm, a neural network model for marketing mix decision-making is established.


## 1.3. Related Work

In the era of new media, with the rapid development of the Internet, mobile phones have replaced PCs (personal computers) as the main tool for obtaining information. Xu et al.’s research found that short videos are unique in social networks, and their advantage is that the information release mode is concise and clear. And put forward the advantages of college students using new media to develop short video marketing [2]. However, because the experimental environment is not closed, there is a certain deviation in the experimental results. The field of marketing research is very important for the sale of goods. Soria Morillo et al. applied a new method to the field of marketing research. He recognized how the brain activity responds to the visualization of short video ads using discrete classification technology. Through low-cost electroencephalography equipment (EEG), the activation level of some brain regions was studied, and advertisements were displayed to users. You may want to know which is the use of neuroscience knowledge in marketing or can provide neuroscience to the marketing department, or why this method can improve accuracy and end-user acceptance compared to other works. Soria Morillo et al. used discretization technology on the EEG frequency band of the generated data set. C4.5, ANN, and a new recognition system based on discretization algorithm Ameva are applied to obtain the score of each TV ad by the topic. The proposed technique allows an accuracy of more than 75% to be achieved, that is, an excellent result considering the type of EEG sensor used in this work [3]. Although it was in line with expectations in most directions, it did not mention EEG factors due to external environmental factors. In some parts of the forecast, there are errors. In order to study the intrinsic link between the perceived value of mobile short videos and customers’ purchase intentions, Xu et al. explored the potential key variables between the two. And based on the mediating role of user participation and attitudes, it is an early attempt to propose a conceptual framework to understand the impact of perceived value on Chinese consumers’ purchasing intentions. This research uses a quantitative design to collect data from 622 users of China mobile’s short video social application who have experience in mobile short video-related social applications through the snowball sampling method by developing an online questionnaire. In addition, in this study, user participation behavior has a positive impact on consumer attitudes [2]. Although the research perspective is forward-looking, there are still many unachievable parts of the technology.


## 1.4. Innovation

The innovations of this paper are as follows: (1) In order to solve the problem of the low proportion of users watching short video lists after sorting, considering that the convolutional neural network has the function of extracting local features, it can extract key information from sentences, such as N-gram. This feature can also be used. It is transplanted to the feature extraction of the text information of the video. (2) Although the traditional proposition theory focuses on the innovation of the algorithm level of the proposition, this paper not only focuses on the innovation of the algorithm level but also explores the architecture design of the proposition platform and proposes an early stage of the platform, proposed corresponding solutions.


## 2. Short Video Personalized Marketing Recommendation Algorithm-Related Technologies

### 2.1. User-Based Collaborative Filtering Algorithm

Through the user’s preference for items, the user’s neighbors in preference are calculated, and then, the user’s preferences can be inferred and recommended according to the preferences of the neighbors [4, 5]. The flow of the user-based collaborative filtering algorithm is shown in Figure 1.Figure 1
User-based collaborative filtering algorithm.Specifically, the algorithm includes three main steps [6]: (1) create a user object evaluation form, (2) calculate the user proximity set, and (3) create related statements. The application is as follows:
(1)
Create user data evaluation table: form users andn projects, obtain user setting functions based on the historical information of existing users and objects, and create an m∗n evaluation board [7](2)
Calculate the user proximity set: based on the project-based user scoreboard, calculate the similarity between the target user and other users and use it as the basis for suggesting related projects [8]. The methods of calculating similarity mainly include cosine similarity, Pearson similarity, and Minkowski distance similarity [9]The formula of cosine similarity is as follows:(1)cosu,v=∑i=1ku∗v∑i=1kv2∗∑i=1ku2.Pearson measures relevance on the basis of users’ common ratings, and its specific formula is as follows:(2)Pearsonu,v=∑i=1kui−u¯vi−v¯∑i=1kui−u¯2∗∑i=1kvi−v¯2.Minkowski distance is a common Euclidean distance. The formula for norm distance whenp=2 is as follows:
(3)distu,v=∑i=0kui−vip1/p.(3)
Generate related recommendations: after calculating users who are similar to the target user, recommend items that similar users like and that the target user does not find to the target user, so as to generate a list of related recommendations [10, 11]


### 2.2. Item-Based Collaborative Filtering Algorithm

An item-based collaborative filtering algorithm does not use the content attributes of items to calculate the similarity between items but uses user behavior data to calculate the similarity between items [12, 13]. It is proposed that there is a greater similarity between item A and item B, because most users who like item A also like item B [14, 15]. Figure 2 is an item-based specific collaborative filtering suggestion process.Figure 2
Collaborative filtering based on items.In terms of specific steps, the ItemCF process is also mainly divided into three steps: first, establish a user-item scoring matrix; then, use the scoring matrix to calculate the set of user neighboring items; finally, generate related recommendations and similarity of items based on the results.The general algorithm for calculating the similarity of items is as follows:(4)Wuv=Nu∩NvNu∩Nv.In these videos,Nu and Nv are the collections of videos liked by user u and user v, respectively [16]. ItemCF is more suitable for recommendation scenarios where user interests are relatively stable, such as book purchase websites. But for the recommendation scenarios where new things often appear and items are updated and iterated quickly, such as news recommendation, it is not suitable for item-based collaborative filtering [17].


### 2.3. Recommendation Algorithm Based on Implicit Semantic Model

Suppose there is already a rating matrixRm,n, which contains the ratings of n items by user m [18]. The scoring matrix should be large and sparse, because it is impossible for every user to evaluate all the data. ru,i represent the user evaluation u of object i. The matrix decomposition method allows matrix Rm,n to be decomposed into matrix P and matrix Q, as shown in the following:
(5)Rm,n=Pm,F∗QF,n.Among them,F is the number of hidden factors, each row in matrix P represents the preference of each user for different hidden factors, and each column in matrix Q represents the possibility of assigning each element to different hidden factors [19, 20]. For each rating item, the corresponding predicted value can be decomposed from the matrix to obtain
(6)r^u,i=∑f=1FPuf·Qfi.The goal of prediction is to make the predicted score as close to the real score as possible, so the objective function of the solution is as follows.The purpose of prediction is to make the prediction as close to the actual as possible, so the objective function of the solution is(7)min:Loss=∑ru,j≠0r^u,i−ru,i2.At the same time, in order to prevent overfitting, a regular term is added:(8)min:Loss=∑ru,j≠0r^u,i−ru,i2+γ∑Puf2+∑Qfi2=fP,Q.Next, we need to solve the loss function. Gradient descent is usually used to solve this problem. The values ofP and Q in iteration t+1 are
(9)Pt+1=Pt0−α∂Loss∂Pt.After obtainingP and Q using the gradient descent method, the final predicted item score is
(10)r^u,i=PuTQm=∑k=1kPukQmk.Compared with the two collaborative filtering algorithms introduced above, the principle of the recommendation algorithm based on the implicit semantic model is very good [21]. By adjusting the objective function, the objective function can be continuously optimized through logic optimization techniques. Essentially, this is a machine learning problem, and collaborative neighborhood filtering is more like a statistical method that does not involve the learning process [22].


### 2.4. Evaluation Indicators for Personalized Marketing Recommendations of Short Videos

For short video marketing recommendation systems, corresponding indicators are also needed to evaluate the quality of these algorithms. The following introduces various indicators based on offline solutions and online suggestions.(1)
Offline evaluation indexGenerally, there are two types of indicators that can be used to predict scores: baseline square error (RMSE) and mean absolute error (MAE) [23]. ru,i shows the user u’s rating of item i, and the predicted value corresponding to its recommendation algorithm. The type of RMSE is
(11)RMSE=∑u,i∈Tr^u,i−ru,i2T.MAE uses the absolute value to predict the error, and its formula is(12)MAE=∑u,i∈Tr^u,i−ru,iT,where T is the number of records rated by the user.
(2)
Online evaluation indicatorsThe online prediction and evaluation indicators should be followed based on the actual proposed plan. For example, in a video recommendation system, a list of videos is recommended to users and click to watch user comments that they can only receive as a recommendation system [24, 25]. At present, consider entering the click-through rate as an online indicator. The calculation types are
(13)CTR=NclickN.Among them is the number of times the user clicked on the video, andN is the total number of video impressions.For all the videos that are clicked or watched, the time the user watches is, which means the time the useru watches the video i, and the total time of each video i is
(14)τui=tuiTi.The CTR indicator calculation formula at this time is(15)CTR=∑τui>τ0kNiN.


## 2.1. User-Based Collaborative Filtering Algorithm

Through the user’s preference for items, the user’s neighbors in preference are calculated, and then, the user’s preferences can be inferred and recommended according to the preferences of the neighbors [4, 5]. The flow of the user-based collaborative filtering algorithm is shown in Figure 1.Figure 1
User-based collaborative filtering algorithm.Specifically, the algorithm includes three main steps [6]: (1) create a user object evaluation form, (2) calculate the user proximity set, and (3) create related statements. The application is as follows:
(1)
Create user data evaluation table: form users andn projects, obtain user setting functions based on the historical information of existing users and objects, and create an m∗n evaluation board [7](2)
Calculate the user proximity set: based on the project-based user scoreboard, calculate the similarity between the target user and other users and use it as the basis for suggesting related projects [8]. The methods of calculating similarity mainly include cosine similarity, Pearson similarity, and Minkowski distance similarity [9]The formula of cosine similarity is as follows:(1)cosu,v=∑i=1ku∗v∑i=1kv2∗∑i=1ku2.Pearson measures relevance on the basis of users’ common ratings, and its specific formula is as follows:(2)Pearsonu,v=∑i=1kui−u¯vi−v¯∑i=1kui−u¯2∗∑i=1kvi−v¯2.Minkowski distance is a common Euclidean distance. The formula for norm distance whenp=2 is as follows:
(3)distu,v=∑i=0kui−vip1/p.(3)
Generate related recommendations: after calculating users who are similar to the target user, recommend items that similar users like and that the target user does not find to the target user, so as to generate a list of related recommendations [10, 11]


## 2.2. Item-Based Collaborative Filtering Algorithm

An item-based collaborative filtering algorithm does not use the content attributes of items to calculate the similarity between items but uses user behavior data to calculate the similarity between items [12, 13]. It is proposed that there is a greater similarity between item A and item B, because most users who like item A also like item B [14, 15]. Figure 2 is an item-based specific collaborative filtering suggestion process.Figure 2
Collaborative filtering based on items.In terms of specific steps, the ItemCF process is also mainly divided into three steps: first, establish a user-item scoring matrix; then, use the scoring matrix to calculate the set of user neighboring items; finally, generate related recommendations and similarity of items based on the results.The general algorithm for calculating the similarity of items is as follows:(4)Wuv=Nu∩NvNu∩Nv.In these videos,Nu and Nv are the collections of videos liked by user u and user v, respectively [16]. ItemCF is more suitable for recommendation scenarios where user interests are relatively stable, such as book purchase websites. But for the recommendation scenarios where new things often appear and items are updated and iterated quickly, such as news recommendation, it is not suitable for item-based collaborative filtering [17].


## 2.3. Recommendation Algorithm Based on Implicit Semantic Model

Suppose there is already a rating matrixRm,n, which contains the ratings of n items by user m [18]. The scoring matrix should be large and sparse, because it is impossible for every user to evaluate all the data. ru,i represent the user evaluation u of object i. The matrix decomposition method allows matrix Rm,n to be decomposed into matrix P and matrix Q, as shown in the following:
(5)Rm,n=Pm,F∗QF,n.Among them,F is the number of hidden factors, each row in matrix P represents the preference of each user for different hidden factors, and each column in matrix Q represents the possibility of assigning each element to different hidden factors [19, 20]. For each rating item, the corresponding predicted value can be decomposed from the matrix to obtain
(6)r^u,i=∑f=1FPuf·Qfi.The goal of prediction is to make the predicted score as close to the real score as possible, so the objective function of the solution is as follows.The purpose of prediction is to make the prediction as close to the actual as possible, so the objective function of the solution is(7)min:Loss=∑ru,j≠0r^u,i−ru,i2.At the same time, in order to prevent overfitting, a regular term is added:(8)min:Loss=∑ru,j≠0r^u,i−ru,i2+γ∑Puf2+∑Qfi2=fP,Q.Next, we need to solve the loss function. Gradient descent is usually used to solve this problem. The values ofP and Q in iteration t+1 are
(9)Pt+1=Pt0−α∂Loss∂Pt.After obtainingP and Q using the gradient descent method, the final predicted item score is
(10)r^u,i=PuTQm=∑k=1kPukQmk.Compared with the two collaborative filtering algorithms introduced above, the principle of the recommendation algorithm based on the implicit semantic model is very good [21]. By adjusting the objective function, the objective function can be continuously optimized through logic optimization techniques. Essentially, this is a machine learning problem, and collaborative neighborhood filtering is more like a statistical method that does not involve the learning process [22].


## 2.4. Evaluation Indicators for Personalized Marketing Recommendations of Short Videos

For short video marketing recommendation systems, corresponding indicators are also needed to evaluate the quality of these algorithms. The following introduces various indicators based on offline solutions and online suggestions.(1)
Offline evaluation indexGenerally, there are two types of indicators that can be used to predict scores: baseline square error (RMSE) and mean absolute error (MAE) [23]. ru,i shows the user u’s rating of item i, and the predicted value corresponding to its recommendation algorithm. The type of RMSE is
(11)RMSE=∑u,i∈Tr^u,i−ru,i2T.MAE uses the absolute value to predict the error, and its formula is(12)MAE=∑u,i∈Tr^u,i−ru,iT,where T is the number of records rated by the user.
(2)
Online evaluation indicatorsThe online prediction and evaluation indicators should be followed based on the actual proposed plan. For example, in a video recommendation system, a list of videos is recommended to users and click to watch user comments that they can only receive as a recommendation system [24, 25]. At present, consider entering the click-through rate as an online indicator. The calculation types are
(13)CTR=NclickN.Among them is the number of times the user clicked on the video, andN is the total number of video impressions.For all the videos that are clicked or watched, the time the user watches is, which means the time the useru watches the video i, and the total time of each video i is
(14)τui=tuiTi.The CTR indicator calculation formula at this time is(15)CTR=∑τui>τ0kNiN.


## 3. Model Building and Experimental Design

### 3.1. Data Acquisition and Preprocessing

Data fusion is the process of real-time and complete evaluation of the situation and threats and their importance. Add it to better understand the article. The data of this experiment comes from the video data in a short video app. It is taken from the user video click, watch log, and user and video information table in the HDFS (Hadoop Distributed File System) of the Hadoop platform. All fields have been completed, desensitization treatment.The experimental environment of this article is based on the Linux16.04 system, using Python 3.6 and TensorFlow 1.2 for data processing and model training. Python is an interpreted language, which is widely used because of its object-oriented programming, dynamic data types, and a large number of third-party library support. This article is based on the Python 3.6 version and is mainly used for the preliminary data preprocessing. TensorFlow is Google’s second-generation open-source artificial intelligence learning system. It is a built-in framework learning software library used to implement neural networks. This article is based on TensorFlow1.2 to process the embedded features, the video title is based on TextCNN for feature extraction, and TensorFlow1.2 is used to build the network structure of the model training stage.Before using these data to construct a recommendation model, the data needs to be preprocessed first. The data preprocessing of this part mainly includes the following 3 aspects:(1)
Classification data type conversion. For the fields in the user information table, one-hot encoding is performed for categorical variables, including gender, age category, province and city location, and other categorical variables(2)
Word segmentation of video title content. Word segmentation is the process of recombining consecutive word sequences into word sequences according to certain specifications. Due to the long content of the title, it needs to be segmented first, and then, the subsequent text vectorization process(3)
Sampling of positive and negative samples. Since the click-to-view of the video is easily affected by the title party, it is included in the field that considers the viewing time ratio. Only when the user’s viewing time ratio exceeds 35% will be included in the positive sample, and the list of recommendations for each user the unwatched video in the video is negatively sampled, and the sampling ratio of positive and negative samples is 1 : 4


### 3.2. Embedded Feature Mapping

For categorical features of IDs such as video ID and user ID, if feature conversion is also performed through one-hot encoding, since the number of users and videos is very large, the features will eventually become high-dimensional sparse feature sequences. Therefore, here, for the ID features, this article adopts the embedded feature processing method to deal with ID features. The specific processing method is as follows. First, change the characteristics of the ID type to a numeric type. For example, after the user “U102983434” is converted, it becomes “102983434,” and the string type is converted to a numeric type. Since the ID of each user is unique, the converted value can be used as a unique index to generate a matrix with dimensions (N, 128), where N is the total number of IDs and 128 is the number of columns in the matrix. Then, use this matrix as the initialization data of the input layer of the neural network.


### 3.3. Dnn Network Construction and Model Training

After obtaining user embedded features, video embedded features, and video title features, we started to build network architecture for video recommendation score prediction.For video features, we embed the ID and tag of the video into the feature vector: splicing it with the feature vector of the text of the video, and output a 128-dimensional vector through the fully connected layer. For some user features, directly import the user’s embedded feature vector into the fully connected layer, which also outputs a 128-dimensional vector.When using the fully connected layer to calculate the above video and user feature vectors, the activation function used is RELU. Among them, the RELU function is an activation function commonly used in artificial neural networks, which lays a solid foundation for deep neural network data fusion, and the optimization algorithm is dam. The Adam algorithm is the adaptive moment estimation method (Adaptive Moment Estimation), which can calculate the adaptive learning rate of each parameter.The relevant model parameter configuration is shown in Table1.Table 1
Video basic information table.
ParameterParameter valueEpoch200Batchy_size256Learning_rate0.01Dropout_keep0.5Epoch is the number of times to train a complete training set sample, batch_size is the sample size of each batch of data, dropout_keep is the probability of the dropout mechanism, and learning_rate is the learning rate, used to control the step size of each gradient, and the final convergence. The results are closely related.After designing the network structure, it is necessary to perform score prediction on the final user features and video features. The calculation method for score prediction here is vector multiplication. A prediction value is obtained by multiplying two 128-dimensional vectors, and this value is obtained. To do regression fitting with the real value, the loss function used is the mean square error withL regularity that has been introduced in the previous chapter:
(16)Ly,fx=y−fx2+γ2w2.The architecture of the entire network is shown in Figure3.Figure 3
The architecture of the entire network.


## 3.1. Data Acquisition and Preprocessing

Data fusion is the process of real-time and complete evaluation of the situation and threats and their importance. Add it to better understand the article. The data of this experiment comes from the video data in a short video app. It is taken from the user video click, watch log, and user and video information table in the HDFS (Hadoop Distributed File System) of the Hadoop platform. All fields have been completed, desensitization treatment.The experimental environment of this article is based on the Linux16.04 system, using Python 3.6 and TensorFlow 1.2 for data processing and model training. Python is an interpreted language, which is widely used because of its object-oriented programming, dynamic data types, and a large number of third-party library support. This article is based on the Python 3.6 version and is mainly used for the preliminary data preprocessing. TensorFlow is Google’s second-generation open-source artificial intelligence learning system. It is a built-in framework learning software library used to implement neural networks. This article is based on TensorFlow1.2 to process the embedded features, the video title is based on TextCNN for feature extraction, and TensorFlow1.2 is used to build the network structure of the model training stage.Before using these data to construct a recommendation model, the data needs to be preprocessed first. The data preprocessing of this part mainly includes the following 3 aspects:(1)
Classification data type conversion. For the fields in the user information table, one-hot encoding is performed for categorical variables, including gender, age category, province and city location, and other categorical variables(2)
Word segmentation of video title content. Word segmentation is the process of recombining consecutive word sequences into word sequences according to certain specifications. Due to the long content of the title, it needs to be segmented first, and then, the subsequent text vectorization process(3)
Sampling of positive and negative samples. Since the click-to-view of the video is easily affected by the title party, it is included in the field that considers the viewing time ratio. Only when the user’s viewing time ratio exceeds 35% will be included in the positive sample, and the list of recommendations for each user the unwatched video in the video is negatively sampled, and the sampling ratio of positive and negative samples is 1 : 4


## 3.2. Embedded Feature Mapping

For categorical features of IDs such as video ID and user ID, if feature conversion is also performed through one-hot encoding, since the number of users and videos is very large, the features will eventually become high-dimensional sparse feature sequences. Therefore, here, for the ID features, this article adopts the embedded feature processing method to deal with ID features. The specific processing method is as follows. First, change the characteristics of the ID type to a numeric type. For example, after the user “U102983434” is converted, it becomes “102983434,” and the string type is converted to a numeric type. Since the ID of each user is unique, the converted value can be used as a unique index to generate a matrix with dimensions (N, 128), where N is the total number of IDs and 128 is the number of columns in the matrix. Then, use this matrix as the initialization data of the input layer of the neural network.


## 3.3. Dnn Network Construction and Model Training

After obtaining user embedded features, video embedded features, and video title features, we started to build network architecture for video recommendation score prediction.For video features, we embed the ID and tag of the video into the feature vector: splicing it with the feature vector of the text of the video, and output a 128-dimensional vector through the fully connected layer. For some user features, directly import the user’s embedded feature vector into the fully connected layer, which also outputs a 128-dimensional vector.When using the fully connected layer to calculate the above video and user feature vectors, the activation function used is RELU. Among them, the RELU function is an activation function commonly used in artificial neural networks, which lays a solid foundation for deep neural network data fusion, and the optimization algorithm is dam. The Adam algorithm is the adaptive moment estimation method (Adaptive Moment Estimation), which can calculate the adaptive learning rate of each parameter.The relevant model parameter configuration is shown in Table1.Table 1
Video basic information table.
ParameterParameter valueEpoch200Batchy_size256Learning_rate0.01Dropout_keep0.5Epoch is the number of times to train a complete training set sample, batch_size is the sample size of each batch of data, dropout_keep is the probability of the dropout mechanism, and learning_rate is the learning rate, used to control the step size of each gradient, and the final convergence. The results are closely related.After designing the network structure, it is necessary to perform score prediction on the final user features and video features. The calculation method for score prediction here is vector multiplication. A prediction value is obtained by multiplying two 128-dimensional vectors, and this value is obtained. To do regression fitting with the real value, the loss function used is the mean square error withL regularity that has been introduced in the previous chapter:
(16)Ly,fx=y−fx2+γ2w2.The architecture of the entire network is shown in Figure3.Figure 3
The architecture of the entire network.


## 4. Result Data Analysis and Discussion

In this chapter, Spss24.0, Amos, and other tools will be used to process and analyze the collected big data samples, complete statistical analysis, factor analysis, reliability and validity testing, and variable correlation, as well as analysis, hypothesis testing, etc. Draw conclusions and discuss the theoretical assumptions of the results.


### 4.1. Descriptive Statistical Analysis

The questionnaire in this article is for people who have shopping experience after watching short video marketing content. This time, the online questionnaire survey tool was used to distribute the questionnaire. A total of 340 questionnaires were distributed, and the number of questionnaires returned was 290. Incomplete and other invalid data, the final number of valid questionnaires was 268, reaching an effective recovery rate of 79%. This questionnaire is issued to collect statistics on the basic information of the respondent’s gender, age, educational background, per capita monthly income, etc. At the same time, in order to ensure the accuracy and authenticity of the survey data, the introductory part of the questionnaire promises to be used only for academic inquiry and can be completely based on individual actual conditions. Fill in, the answer is right or wrong. See details in Table2.Table 2
Descriptive statistical analysis of samples.
FrequencyPercentageCumulative percentageAgeUnder 2520877.677.625-30511996.631-4062.298.9Above 4031.1100EducationJunior high school20.70.7High school134.95.6Undergraduate13048.554.1Master’s degree or above12345.9100Monthly incomeBelow 200011241.841.82001-30005721.363.1Above 30019936.9100Total268100The proportion of boys in the survey group is 32.5%, and the proportion of female surnames is 67.5%. This is more in line with the actual situation that women tend to shop online. The age of the survey group is concentrated under 30 years old, and the overall population of this age group is younger highly active online, good at accepting new things. In terms of academic qualifications, the interviewees are mostly undergraduates and postgraduates, accounting for 48.5% and 43.3%, respectively. Such groups generally have a relatively high level of education and can understand the questionnaire items more accurately, answering academic questionnaires earnestly, and guaranteeing a certain degree the reliability and validity of the questionnaire collected in the questionnaire. In terms of disposable monthly income, the highest tax rate of 2000 yuan and below is 39.8%, and 59.8% of the surveyed’s cumulative monthly disposable income is less than 3000 yuan. Both 2000-3000 yuan and 4000 yuan people are more than 20%. It can be seen that the interviewees generally have certain spending power and space.


### 4.2. Exploratory Factor Analysis

#### 4.2.1. Exploratory Factor Analysis of Content Marketing

First, perform Bartlett’s sphericity test on the 11 items involved in content marketing. The Bartlett sphere test method is based on the correlation coefficient matrix. Its null hypothesis is that the correlation coefficient matrix is a unit matrix, that is, all the diagonal elements of the correlation coefficient matrix are 1, and all the elements on the off-diagonal line are the statistic of the Bartlett sphere test method which is obtained according to the determinant of the correlation coefficient matrix. From Table3, it can be seen that the KMO value of the scale is 0.865, and the corresponding P value is lower than the significant level, indicating that the test can be used as a factor analysis. The content marketing factors were extracted by the principal component analysis method, and a total of 3 factors were extracted. The analysis results are shown in Table 4. The load value of each factor is greater than 0.5, indicating that the content marketing is effective.Table 3
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.865Bartlett sphericity testApproximate chi-square1736.512Degree of freedom55Significance0.000Table 4
Factor loading after rotation.
Gn1Ingredient 1Ingredient 2Ingredient 3Gn20.872Gn30.840Gn40.880Yl10.827Yl20.848Yl30.823Yl40.838Sj10.840Sj20.842Sj30.859


#### 4.2.2. Exploratory Factor Analysis of Psychological Distance

Exploratory factor analysis of psychological distance, as can be seen from Table5, the KM0 value of the scale is 0.938, and the corresponding P value is lower than the significant level, indicating that the test has passed and factor analysis can be done. Principal component analysis was used to extract the psychological distance factor, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the psychological distance validity is good.Table 5
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.938Bartlett sphericity testApproximate chi-square1781.512Degree of freedom16Significance0.000Table 6
Factor loading after rotation.
Ingredient 1gm10.924gm20.912gm30.931gm40.902gm50.931gm60.905


#### 4.2.3. Exploratory Factor Analysis of Product Involvement

Exploratory factor analysis of product involvement is carried out, and the result of the data set is shown in Figure4.Figure 4
Exploratory factor of product involvement.It can be seen from Figure4 that the KM0 value of the scale is 0.793, and the corresponding P value is lower than the significant level, indicating that the test is passed and factor analysis can be done. The product involvement factor was extracted by the principal component analysis method, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the crystal production has good involvement degree validity.


### 4.3. Comparison of Experimental Results of Marketing Recommendation Algorithm

Through the VRBCH algorithm, people can be aware of dense and sparse areas and discover global distribution patterns and interesting interrelationships between data attributes. In order to better evaluate the performance of the proposed VRBCH algorithm, this paper chooses sequential matrix factorization (SequentialMF), which is based on sequential matrix factorization (UserCF) and collaborative filtering algorithm with uncertain neighbors (UNCF). In the time series matrix decomposition, the parameters will affect the relationship. If the data is less than 99%, a user network will be created based on the time the user watches the video, and the relationship will be created using the network diagram and the probability table. It will be calculated, and the suggested video search is calculated using this model. The algorithm divides the data set into a set of test and training sets, calculates the similarity of usersK=10 through the training set, and obtains the similarity of the user matrix, which is then similar to 10 videos in the video. Calculate user similarity through a total of 10 interesting videos, and recommend 20 videos to users. The collaborative filtering algorithm uses video or user similarity to calculate the neighborhood factor. If the adjustment parameter is 20, the prediction factor is calculated and recommendations are made based on the proximity factor. Use RMSE scoring index and F to divide the experiment into two parts. Run the experiment using percentages from different data sets, and run the percentage values continuously at certain intervals. The first part of the experiment compares the performance of each recommendation algorithm under the MovieLens Latest Datasets data set, as shown in Figure 5.Figure 5
MovieLens Latest Datasets data set.As shown in Figure5, the RESE performance of the VRBCH algorithm and time series matrix decomposition, time series matrix decomposition, and coordinated uncertain adjacency filtering algorithm are quite different in the early stage, but the final result is relatively ideal. If the amount of data is too small, the error will be large. Experiments show that as the percentage of data continues to increase, the trend shows a downward trend. After reaching 50%, the downward trend is slow. When the RESE performance reaches 80%, it remains almost unchanged.The second part of the experiment compares the performance of each recommendation algorithm under the YouTube data set, as shown in Figure6.Figure 6
YouTube data set.Figure6 shows that due to the relatively large weakness of the YouTube data set, the VRBCH algorithm initially outperformed other algorithms. As the F table of the VRBCH algorithm performs best, the performance of the algorithm will increase as the percentage of the data set increases. When the data volume reaches 80%, the performance of the VRBCH algorithm is steadily improved. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased dramatically by 30% to 35%. Compared with other algorithms, it is not based on user evaluation. The F-meter performance is lower than the VRBCH performance, because there are few SequentialMF, UserCF, and UNCF algorithms that have high requirements for data failure.


## 4.1. Descriptive Statistical Analysis

The questionnaire in this article is for people who have shopping experience after watching short video marketing content. This time, the online questionnaire survey tool was used to distribute the questionnaire. A total of 340 questionnaires were distributed, and the number of questionnaires returned was 290. Incomplete and other invalid data, the final number of valid questionnaires was 268, reaching an effective recovery rate of 79%. This questionnaire is issued to collect statistics on the basic information of the respondent’s gender, age, educational background, per capita monthly income, etc. At the same time, in order to ensure the accuracy and authenticity of the survey data, the introductory part of the questionnaire promises to be used only for academic inquiry and can be completely based on individual actual conditions. Fill in, the answer is right or wrong. See details in Table2.Table 2
Descriptive statistical analysis of samples.
FrequencyPercentageCumulative percentageAgeUnder 2520877.677.625-30511996.631-4062.298.9Above 4031.1100EducationJunior high school20.70.7High school134.95.6Undergraduate13048.554.1Master’s degree or above12345.9100Monthly incomeBelow 200011241.841.82001-30005721.363.1Above 30019936.9100Total268100The proportion of boys in the survey group is 32.5%, and the proportion of female surnames is 67.5%. This is more in line with the actual situation that women tend to shop online. The age of the survey group is concentrated under 30 years old, and the overall population of this age group is younger highly active online, good at accepting new things. In terms of academic qualifications, the interviewees are mostly undergraduates and postgraduates, accounting for 48.5% and 43.3%, respectively. Such groups generally have a relatively high level of education and can understand the questionnaire items more accurately, answering academic questionnaires earnestly, and guaranteeing a certain degree the reliability and validity of the questionnaire collected in the questionnaire. In terms of disposable monthly income, the highest tax rate of 2000 yuan and below is 39.8%, and 59.8% of the surveyed’s cumulative monthly disposable income is less than 3000 yuan. Both 2000-3000 yuan and 4000 yuan people are more than 20%. It can be seen that the interviewees generally have certain spending power and space.


## 4.2. Exploratory Factor Analysis

### 4.2.1. Exploratory Factor Analysis of Content Marketing

First, perform Bartlett’s sphericity test on the 11 items involved in content marketing. The Bartlett sphere test method is based on the correlation coefficient matrix. Its null hypothesis is that the correlation coefficient matrix is a unit matrix, that is, all the diagonal elements of the correlation coefficient matrix are 1, and all the elements on the off-diagonal line are the statistic of the Bartlett sphere test method which is obtained according to the determinant of the correlation coefficient matrix. From Table3, it can be seen that the KMO value of the scale is 0.865, and the corresponding P value is lower than the significant level, indicating that the test can be used as a factor analysis. The content marketing factors were extracted by the principal component analysis method, and a total of 3 factors were extracted. The analysis results are shown in Table 4. The load value of each factor is greater than 0.5, indicating that the content marketing is effective.Table 3
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.865Bartlett sphericity testApproximate chi-square1736.512Degree of freedom55Significance0.000Table 4
Factor loading after rotation.
Gn1Ingredient 1Ingredient 2Ingredient 3Gn20.872Gn30.840Gn40.880Yl10.827Yl20.848Yl30.823Yl40.838Sj10.840Sj20.842Sj30.859


### 4.2.2. Exploratory Factor Analysis of Psychological Distance

Exploratory factor analysis of psychological distance, as can be seen from Table5, the KM0 value of the scale is 0.938, and the corresponding P value is lower than the significant level, indicating that the test has passed and factor analysis can be done. Principal component analysis was used to extract the psychological distance factor, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the psychological distance validity is good.Table 5
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.938Bartlett sphericity testApproximate chi-square1781.512Degree of freedom16Significance0.000Table 6
Factor loading after rotation.
Ingredient 1gm10.924gm20.912gm30.931gm40.902gm50.931gm60.905


### 4.2.3. Exploratory Factor Analysis of Product Involvement

Exploratory factor analysis of product involvement is carried out, and the result of the data set is shown in Figure4.Figure 4
Exploratory factor of product involvement.It can be seen from Figure4 that the KM0 value of the scale is 0.793, and the corresponding P value is lower than the significant level, indicating that the test is passed and factor analysis can be done. The product involvement factor was extracted by the principal component analysis method, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the crystal production has good involvement degree validity.


## 4.2.1. Exploratory Factor Analysis of Content Marketing

First, perform Bartlett’s sphericity test on the 11 items involved in content marketing. The Bartlett sphere test method is based on the correlation coefficient matrix. Its null hypothesis is that the correlation coefficient matrix is a unit matrix, that is, all the diagonal elements of the correlation coefficient matrix are 1, and all the elements on the off-diagonal line are the statistic of the Bartlett sphere test method which is obtained according to the determinant of the correlation coefficient matrix. From Table3, it can be seen that the KMO value of the scale is 0.865, and the corresponding P value is lower than the significant level, indicating that the test can be used as a factor analysis. The content marketing factors were extracted by the principal component analysis method, and a total of 3 factors were extracted. The analysis results are shown in Table 4. The load value of each factor is greater than 0.5, indicating that the content marketing is effective.Table 3
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.865Bartlett sphericity testApproximate chi-square1736.512Degree of freedom55Significance0.000Table 4
Factor loading after rotation.
Gn1Ingredient 1Ingredient 2Ingredient 3Gn20.872Gn30.840Gn40.880Yl10.827Yl20.848Yl30.823Yl40.838Sj10.840Sj20.842Sj30.859


## 4.2.2. Exploratory Factor Analysis of Psychological Distance

Exploratory factor analysis of psychological distance, as can be seen from Table5, the KM0 value of the scale is 0.938, and the corresponding P value is lower than the significant level, indicating that the test has passed and factor analysis can be done. Principal component analysis was used to extract the psychological distance factor, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the psychological distance validity is good.Table 5
KMO and Bartlett test.
Sampling adequacy of KMO metrics0.938Bartlett sphericity testApproximate chi-square1781.512Degree of freedom16Significance0.000Table 6
Factor loading after rotation.
Ingredient 1gm10.924gm20.912gm30.931gm40.902gm50.931gm60.905


## 4.2.3. Exploratory Factor Analysis of Product Involvement

Exploratory factor analysis of product involvement is carried out, and the result of the data set is shown in Figure4.Figure 4
Exploratory factor of product involvement.It can be seen from Figure4 that the KM0 value of the scale is 0.793, and the corresponding P value is lower than the significant level, indicating that the test is passed and factor analysis can be done. The product involvement factor was extracted by the principal component analysis method, and one factor was extracted. The analysis results are shown in Table 6. The factor loading values are all greater than 0.5, indicating that the crystal production has good involvement degree validity.


## 4.3. Comparison of Experimental Results of Marketing Recommendation Algorithm

Through the VRBCH algorithm, people can be aware of dense and sparse areas and discover global distribution patterns and interesting interrelationships between data attributes. In order to better evaluate the performance of the proposed VRBCH algorithm, this paper chooses sequential matrix factorization (SequentialMF), which is based on sequential matrix factorization (UserCF) and collaborative filtering algorithm with uncertain neighbors (UNCF). In the time series matrix decomposition, the parameters will affect the relationship. If the data is less than 99%, a user network will be created based on the time the user watches the video, and the relationship will be created using the network diagram and the probability table. It will be calculated, and the suggested video search is calculated using this model. The algorithm divides the data set into a set of test and training sets, calculates the similarity of usersK=10 through the training set, and obtains the similarity of the user matrix, which is then similar to 10 videos in the video. Calculate user similarity through a total of 10 interesting videos, and recommend 20 videos to users. The collaborative filtering algorithm uses video or user similarity to calculate the neighborhood factor. If the adjustment parameter is 20, the prediction factor is calculated and recommendations are made based on the proximity factor. Use RMSE scoring index and F to divide the experiment into two parts. Run the experiment using percentages from different data sets, and run the percentage values continuously at certain intervals. The first part of the experiment compares the performance of each recommendation algorithm under the MovieLens Latest Datasets data set, as shown in Figure 5.Figure 5
MovieLens Latest Datasets data set.As shown in Figure5, the RESE performance of the VRBCH algorithm and time series matrix decomposition, time series matrix decomposition, and coordinated uncertain adjacency filtering algorithm are quite different in the early stage, but the final result is relatively ideal. If the amount of data is too small, the error will be large. Experiments show that as the percentage of data continues to increase, the trend shows a downward trend. After reaching 50%, the downward trend is slow. When the RESE performance reaches 80%, it remains almost unchanged.The second part of the experiment compares the performance of each recommendation algorithm under the YouTube data set, as shown in Figure6.Figure 6
YouTube data set.Figure6 shows that due to the relatively large weakness of the YouTube data set, the VRBCH algorithm initially outperformed other algorithms. As the F table of the VRBCH algorithm performs best, the performance of the algorithm will increase as the percentage of the data set increases. When the data volume reaches 80%, the performance of the VRBCH algorithm is steadily improved. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased dramatically by 30% to 35%. Compared with other algorithms, it is not based on user evaluation. The F-meter performance is lower than the VRBCH performance, because there are few SequentialMF, UserCF, and UNCF algorithms that have high requirements for data failure.


## 5. Conclusions

The recommendation system uses special information filtering technology to recommend different items or content to users who may be interested in them. The empirical results show that the entertainment performance of short videos is generally higher than that of other e-commerce platforms. Short videos include text, images, and music. The image of the product is more three-dimensional and intuitive, and the display form can stimulate consumers, cause emotional reactions, and purchase enthusiasm. The combination of humor and exaggeration, the irony of the plot upside down, and modern language is easy to be accepted and imitated, which caused the market to promote. Embedding ads in such videos can make it easier to attract consumers and participate in the experience. The more innovative and entertaining the video content is, the more diverse it can stimulate the curiosity and psychology of consumers seeking excitement, thereby having a positive impact on consumers’ emotional enjoyment and awakening. In the context of mobile short video marketing, consumer psychological emotions have a positive effect on impulsive purchases, and there is a mediating effect in the influence of stimulus variables on impulsive purchases. Emotional changes are an important internal driving factor for consumers to make impulsive purchases. Both pleasure and arousal affect consumers’ impulse to purchase products. When the user’s attitude towards short video marketing is positive and open, the effect of advertising marketing will be more significant, and the user will have a high acceptance of the marketing form, and it will be easier to try recommended products. The dynamic content of short video is usually short, concise, and vivid. It is more attractive than static pictures and texts. When combined with emotional background music, substituting intonation and direct copywriting content, it is usually good for consumers. Emotions have a great impact and build emotional bonds. Everyone is reluctant to watch naked advertisements but rarely refuses to listen to an infectious story. Therefore, implant marketing that is imperceptible in the output of feelings is easier to be accepted by people. Emotion is an abstract feeling, which needs to be conveyed with the help of external concrete expressions. Therefore, in marketing activities, the emotional performance characteristics can be sorted out and then used. For example, when it is found that consumers are showing pleasant emotions and aroused emotions are stimulated, adding introductory discourse or marketing temptation can enhance consumers’ desire to buy and stimulate impulsive purchases. The amount of calculation is the problem of clustering. This article only clusters a small amount of low-dimensional data. In the big data environment, it is necessary to introduce a distributed computing method. This article intends to perform clustering attempts under the Spark platform as the next step. In the ranking model, the processing of historical data is rough, which reduces the recommendation accuracy and user experience.



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*Source: 3638071-2021-11-16.xml*