# Personalized Movie Recommendation Method Based on Deep Learning

Authors: Jingdong Liu; Won-Ho Choi; Jun Liu
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.


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## Body

## 1. Introduction

In recent years, with the improvement of people’s living standards and the rapid spread of mobile Internet, more and more information is flooding the Internet [1]. Because different users have different hobbies, areas of interest, personal experience, etc., it is difficult for users to filter the information they are interested in from the massive information. How to use the big data that has emerged with the rise of mobile Internet and social media to serve users and carry out personality Chemical recommendation has become the focus of research [2]. Recommendation algorithm is a type of machine learning algorithm that is very closely related to real life. It refers to a type of algorithm that does not require users to provide clear needs but models users’ interests by analyzing their historical behaviors, so as to actively recommend products to users that can meet their interests and needs. Among the various recommendation algorithms, the collaborative filtering algorithm is the most widely used and representative algorithm. The basic idea is to use the preferences of a group with similar interests and common experience to recommend what users are interested in. Individuals give a considerable degree of response (such as scoring) to the information through a cooperative mechanism and record it to achieve the purpose of filtering and help users filter information [3]. The response is not necessarily limited to those of particular interest, and the record of particularly uninteresting information is also very important.Traditional user interest modeling methods are difficult to express the essential information of the data and require manual extraction of features, so that researchers need to spend a lot of time and energy on data labeling, processing, and feature extraction, and different data need to be different. Feature extraction and the extracted features are not necessarily effective. The effect of feature extraction often determines the performance of the algorithm. In recent years, deep learning has been favored by researchers [4]. It can accurately express more necessary data information through multilayer nonlinear computing units and effectively reduce the difficulty of model training through unsupervised learning.Chen found that classification is one of the most popular topics in hyperspectral remote sensing [5]. In the past two decades, experts have proposed many methods to deal with the classification of hyperspectral data, but most of them did not extract hierarchically. Chen introduced the concept of deep learning to the classification of hyperspectral data for the first time, first by following the classification based on classical spectral information to verify the qualifications of stacked autoencoders; secondly, they proposed a new method of spatially dominant information classification; A novel deep learning framework is proposed to integrate these two functions, from which the highest classification accuracy can be obtained. The framework is a mixture of principal component analysis, deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are designed to obtain useful advanced features. This research lacks experimental data support [6]. Alhamid believes that context-aware recommendation offers the potential to use social content and use relevant tags and rating information to personalize content searches in a given context. Recommendation systems solve the problem of trying to identify relevant resources from a large number of online available choices. As a result, Alhamid proposed a new recommendation model that can personalize recommendations and improve the user experience by analyzing the context when users wish to access multimedia content; empirical analysis is performed on the data set to prove use of potential preferences to rank items in a given context; use optimization functions to maximize the average average accuracy of result recommendations. This method is not very innovative [7]. Barman found that search engines have become an indispensable part of people’s daily lives. They can help users find specific information from a large amount of data stored on the Internet. The query recommendation function of search engines can respond to users’ original queries and provide users with Recommend multiple alternative queries. For different users from a specific geographic area, these suggestions remain basically the same, but the acceptability of these alternative queries often varies from person to person [8]. In this work, Barman D proposed a personalized recommendation system based on genetic algorithms [9] and used the search logs of commercial search engines to evaluate the proposed method. This research is not practical and not suitable for popularization in practice [10].The innovations of this paper are as follows: (1) propose a collaborative filtering personalized movie recommendation algorithm; (2) construct a user interest model based on Seq2Seq; (3) design a personalized movie recommendation system based on deep learning.


## 2. Methods of Personalized Movie Recommendation Methods Based on Deep Learning

### 2.1. Personalized Recommendation

Through personalized recommendation by extracting the user’s historical information features, it is convenient and accurate for the user to mine the things he may like from the large database and make personalized recommendation for each user [11]. For example, in the e-commerce market [12], personalized recommendation algorithms can stimulate users’ potential purchase desires and reduce the time for users to select products, thereby facilitating users’ shopping methods; in the news or video fields, personalized recommendation algorithms can provide users with recommendations. The information of his “appetite” improves the user’s reading efficiency, reduces the time for users to select product content, and can also attract users’ interest in the product [13]. Nowadays, recommended websites can obtain user behaviors such as length of stay, favorite links, and number of likes. These behaviors are roughly divided into explicit feedback behaviors and implicit feedback behaviors. Explicit feedback behaviors can directly present user preferences. Commonly it is the user’s rating of the item [14].The implicit feedback behavior cannot clarify the user’s preferences [15]. A common implicit feedback behavior is the user’s web browsing record. The user may not be interested in the item when browsing the web but may click and browse the item out of curiosity or unintentionally [16]. Although the record does not clearly know the user’s preferences, the implicit data obtained by the general website account for a large proportion. Therefore, based on the use of explicit feedback data, it is necessary to dig out the implicit feedback data value meaning content, so as to achieve personalized recommendation [17].According to the different data types, the algorithms needed for personalized recommendation are mainly composed of two types: the first is a recommendation algorithm based on content, and the second is a recommendation algorithm based on collaborative filtering. The main idea of the content-based recommendation algorithm is to recommend to users the information with the greatest similarity in the content of the items they like and the information they have followed; the personalized recommendation system basically uses a collaborative filtering algorithm [18], and its core recommendation idea is as follows: the user has other users with similar preferences and then recommends to the user items that other users have purchased but this user has not purchased [19].


### 2.2. Collaborative Filtering Recommendation Algorithm

#### 2.2.1. Similarity Calculation

The measure of similarity between users is generally compared by the method of vector calculation. Among these comparison methods, Pearson similarity and cosine similarity are the most commonly used [20].The calculation formula of cosine similarity is as follows, wheresimx,y represents the similarity between users x and y and N(x) and N(y) represent the collection of ratings of users x and y, respectively:(1)simx,y=Nx×NyNxNy.The calculation formula of Pearson’s correlation coefficient is as follows:(2)simx,y=∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y∑i∈Nx∩NbRxi−R¯x∗Rki−R¯k2∗∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y.When recommending the target userx, similarity calculation is also needed to find the n movies most similar to movie m [21]. Here, it is represented by the set S(m, n); the user x’s interest in movie m can be calculated by the following formula:(3)Xx,n=∑j∈Sm,n∩Nxsimn,jpxj.where Nx represents the set of movies that user x likes and pxj represents the degree of user x’s preference for movie j.


#### 2.2.2. Matrix Decomposition Recommendation Algorithm

The basic principle of the matrix decomposition recommendation algorithm is as follows: analyze the user’s historical information and construct a rating matrix, which is composed of user ID, item ID, and rating [22]. Then, the user item rating matrix is decomposed into two low-dimensional feature matrices through matrix decomposition algorithm. Finally, these two low-dimensional matrices are used to estimate items that users have not commented on [23]. The initial user-item rating matrix Rn×m is decomposed, and the user vector representation and the item vector representation are obtained, respectively, and then two feature matrices are obtained [24], namely, user feature matrix Uk×n and item feature matrix Vk×n, where n is the number of users, m is the number of items, and k is the dimension of the hidden vector feature space [25, 26]. Multiplying the two matrices obtained by decomposition is the predicted score:(4)R∗=UTV.The algorithm trains the model by optimizing the difference between the predicted scoreR and the real score R∗, that is, optimizing the objective function:(5)E=IR−UTV2=∑i=1N∑j=1MIijRij−UiTVj2.To prevent overfitting, redefine the objective function:(6)E=∑i=1N∑j=1MRij−UiTVj2+λUi2+Vi2.Use the stochastic gradient descent method to solve the matricesU and V [27]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(7)∂E∂U=−2V+2λU,∂E∂U=−2U+2λV.Get updatedU and V, where α is the learning rate:(8)U=U+αV−λU,V=V+αU−λV.


#### 2.2.3. Probability Matrix Factorization

Probabilistic matrix decomposition is to introduce a probability model on the basis of matrix decomposition to optimize. The introduction of the probability model has greatly improved the performance of matrix factorization and further improved the accuracy of the matrix factorization model [28]. Probability matrix decomposition has two leading assumptions: one is that the difference between the overall rating matrix R of the user and the inner product R of the eigenvectors of the user and the movie obeys the Gaussian distribution of variance [29]; the second is that the eigenvector matrix U of the user and the movie’s elements of the eigenvector matrix V, respectively, obey the Gaussian distribution with the mean value being 0 and the variance being ϕu and ϕv [30,31].According to hypothesis one, the probability density function ofR can be obtained, where Iij is an indicative function representing whether user i has made an evaluation for movie j; if the evaluation has been made, its value is 1; otherwise, it is 0:(9)pR|U,V,ϕ2=∏i=1N∏j=1MNRijUiTVj,ϕ2Iij.According to hypothesis two,U and V probability density functions can be obtained:(10)pU,ϕU2=∏i=1NNUi0,ϕUI,pV,ϕV2=∏i=1MNVj0,ϕVI.The stochastic gradient descent method is used to solve the matricesU and V [32]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(11)∂E∂U=−V+λuU,∂E∂V=−U+λVV.Get updatedU and V, where α is the learning rate:(12)U=U+αV−λuU,V=V+αU−λvV.The method part of this article uses the above method for the research of personalized movie recommendation algorithm based on deep learning. The specific process is shown in Figure1.Figure 1
Part of the technical process of this method.


## 2.1. Personalized Recommendation

Through personalized recommendation by extracting the user’s historical information features, it is convenient and accurate for the user to mine the things he may like from the large database and make personalized recommendation for each user [11]. For example, in the e-commerce market [12], personalized recommendation algorithms can stimulate users’ potential purchase desires and reduce the time for users to select products, thereby facilitating users’ shopping methods; in the news or video fields, personalized recommendation algorithms can provide users with recommendations. The information of his “appetite” improves the user’s reading efficiency, reduces the time for users to select product content, and can also attract users’ interest in the product [13]. Nowadays, recommended websites can obtain user behaviors such as length of stay, favorite links, and number of likes. These behaviors are roughly divided into explicit feedback behaviors and implicit feedback behaviors. Explicit feedback behaviors can directly present user preferences. Commonly it is the user’s rating of the item [14].The implicit feedback behavior cannot clarify the user’s preferences [15]. A common implicit feedback behavior is the user’s web browsing record. The user may not be interested in the item when browsing the web but may click and browse the item out of curiosity or unintentionally [16]. Although the record does not clearly know the user’s preferences, the implicit data obtained by the general website account for a large proportion. Therefore, based on the use of explicit feedback data, it is necessary to dig out the implicit feedback data value meaning content, so as to achieve personalized recommendation [17].According to the different data types, the algorithms needed for personalized recommendation are mainly composed of two types: the first is a recommendation algorithm based on content, and the second is a recommendation algorithm based on collaborative filtering. The main idea of the content-based recommendation algorithm is to recommend to users the information with the greatest similarity in the content of the items they like and the information they have followed; the personalized recommendation system basically uses a collaborative filtering algorithm [18], and its core recommendation idea is as follows: the user has other users with similar preferences and then recommends to the user items that other users have purchased but this user has not purchased [19].


## 2.2. Collaborative Filtering Recommendation Algorithm

### 2.2.1. Similarity Calculation

The measure of similarity between users is generally compared by the method of vector calculation. Among these comparison methods, Pearson similarity and cosine similarity are the most commonly used [20].The calculation formula of cosine similarity is as follows, wheresimx,y represents the similarity between users x and y and N(x) and N(y) represent the collection of ratings of users x and y, respectively:(1)simx,y=Nx×NyNxNy.The calculation formula of Pearson’s correlation coefficient is as follows:(2)simx,y=∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y∑i∈Nx∩NbRxi−R¯x∗Rki−R¯k2∗∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y.When recommending the target userx, similarity calculation is also needed to find the n movies most similar to movie m [21]. Here, it is represented by the set S(m, n); the user x’s interest in movie m can be calculated by the following formula:(3)Xx,n=∑j∈Sm,n∩Nxsimn,jpxj.where Nx represents the set of movies that user x likes and pxj represents the degree of user x’s preference for movie j.


### 2.2.2. Matrix Decomposition Recommendation Algorithm

The basic principle of the matrix decomposition recommendation algorithm is as follows: analyze the user’s historical information and construct a rating matrix, which is composed of user ID, item ID, and rating [22]. Then, the user item rating matrix is decomposed into two low-dimensional feature matrices through matrix decomposition algorithm. Finally, these two low-dimensional matrices are used to estimate items that users have not commented on [23]. The initial user-item rating matrix Rn×m is decomposed, and the user vector representation and the item vector representation are obtained, respectively, and then two feature matrices are obtained [24], namely, user feature matrix Uk×n and item feature matrix Vk×n, where n is the number of users, m is the number of items, and k is the dimension of the hidden vector feature space [25, 26]. Multiplying the two matrices obtained by decomposition is the predicted score:(4)R∗=UTV.The algorithm trains the model by optimizing the difference between the predicted scoreR and the real score R∗, that is, optimizing the objective function:(5)E=IR−UTV2=∑i=1N∑j=1MIijRij−UiTVj2.To prevent overfitting, redefine the objective function:(6)E=∑i=1N∑j=1MRij−UiTVj2+λUi2+Vi2.Use the stochastic gradient descent method to solve the matricesU and V [27]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(7)∂E∂U=−2V+2λU,∂E∂U=−2U+2λV.Get updatedU and V, where α is the learning rate:(8)U=U+αV−λU,V=V+αU−λV.


### 2.2.3. Probability Matrix Factorization

Probabilistic matrix decomposition is to introduce a probability model on the basis of matrix decomposition to optimize. The introduction of the probability model has greatly improved the performance of matrix factorization and further improved the accuracy of the matrix factorization model [28]. Probability matrix decomposition has two leading assumptions: one is that the difference between the overall rating matrix R of the user and the inner product R of the eigenvectors of the user and the movie obeys the Gaussian distribution of variance [29]; the second is that the eigenvector matrix U of the user and the movie’s elements of the eigenvector matrix V, respectively, obey the Gaussian distribution with the mean value being 0 and the variance being ϕu and ϕv [30,31].According to hypothesis one, the probability density function ofR can be obtained, where Iij is an indicative function representing whether user i has made an evaluation for movie j; if the evaluation has been made, its value is 1; otherwise, it is 0:(9)pR|U,V,ϕ2=∏i=1N∏j=1MNRijUiTVj,ϕ2Iij.According to hypothesis two,U and V probability density functions can be obtained:(10)pU,ϕU2=∏i=1NNUi0,ϕUI,pV,ϕV2=∏i=1MNVj0,ϕVI.The stochastic gradient descent method is used to solve the matricesU and V [32]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(11)∂E∂U=−V+λuU,∂E∂V=−U+λVV.Get updatedU and V, where α is the learning rate:(12)U=U+αV−λuU,V=V+αU−λvV.The method part of this article uses the above method for the research of personalized movie recommendation algorithm based on deep learning. The specific process is shown in Figure1.Figure 1
Part of the technical process of this method.


## 2.2.1. Similarity Calculation

The measure of similarity between users is generally compared by the method of vector calculation. Among these comparison methods, Pearson similarity and cosine similarity are the most commonly used [20].The calculation formula of cosine similarity is as follows, wheresimx,y represents the similarity between users x and y and N(x) and N(y) represent the collection of ratings of users x and y, respectively:(1)simx,y=Nx×NyNxNy.The calculation formula of Pearson’s correlation coefficient is as follows:(2)simx,y=∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y∑i∈Nx∩NbRxi−R¯x∗Rki−R¯k2∗∑i∈Nx∩NbRxi−R¯x∗Ryi−R¯y.When recommending the target userx, similarity calculation is also needed to find the n movies most similar to movie m [21]. Here, it is represented by the set S(m, n); the user x’s interest in movie m can be calculated by the following formula:(3)Xx,n=∑j∈Sm,n∩Nxsimn,jpxj.where Nx represents the set of movies that user x likes and pxj represents the degree of user x’s preference for movie j.


## 2.2.2. Matrix Decomposition Recommendation Algorithm

The basic principle of the matrix decomposition recommendation algorithm is as follows: analyze the user’s historical information and construct a rating matrix, which is composed of user ID, item ID, and rating [22]. Then, the user item rating matrix is decomposed into two low-dimensional feature matrices through matrix decomposition algorithm. Finally, these two low-dimensional matrices are used to estimate items that users have not commented on [23]. The initial user-item rating matrix Rn×m is decomposed, and the user vector representation and the item vector representation are obtained, respectively, and then two feature matrices are obtained [24], namely, user feature matrix Uk×n and item feature matrix Vk×n, where n is the number of users, m is the number of items, and k is the dimension of the hidden vector feature space [25, 26]. Multiplying the two matrices obtained by decomposition is the predicted score:(4)R∗=UTV.The algorithm trains the model by optimizing the difference between the predicted scoreR and the real score R∗, that is, optimizing the objective function:(5)E=IR−UTV2=∑i=1N∑j=1MIijRij−UiTVj2.To prevent overfitting, redefine the objective function:(6)E=∑i=1N∑j=1MRij−UiTVj2+λUi2+Vi2.Use the stochastic gradient descent method to solve the matricesU and V [27]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(7)∂E∂U=−2V+2λU,∂E∂U=−2U+2λV.Get updatedU and V, where α is the learning rate:(8)U=U+αV−λU,V=V+αU−λV.


## 2.2.3. Probability Matrix Factorization

Probabilistic matrix decomposition is to introduce a probability model on the basis of matrix decomposition to optimize. The introduction of the probability model has greatly improved the performance of matrix factorization and further improved the accuracy of the matrix factorization model [28]. Probability matrix decomposition has two leading assumptions: one is that the difference between the overall rating matrix R of the user and the inner product R of the eigenvectors of the user and the movie obeys the Gaussian distribution of variance [29]; the second is that the eigenvector matrix U of the user and the movie’s elements of the eigenvector matrix V, respectively, obey the Gaussian distribution with the mean value being 0 and the variance being ϕu and ϕv [30,31].According to hypothesis one, the probability density function ofR can be obtained, where Iij is an indicative function representing whether user i has made an evaluation for movie j; if the evaluation has been made, its value is 1; otherwise, it is 0:(9)pR|U,V,ϕ2=∏i=1N∏j=1MNRijUiTVj,ϕ2Iij.According to hypothesis two,U and V probability density functions can be obtained:(10)pU,ϕU2=∏i=1NNUi0,ϕUI,pV,ϕV2=∏i=1MNVj0,ϕVI.The stochastic gradient descent method is used to solve the matricesU and V [32]. Then, the partial derivatives of E with respect to U and V are expressed as the following formulas:(11)∂E∂U=−V+λuU,∂E∂V=−U+λVV.Get updatedU and V, where α is the learning rate:(12)U=U+αV−λuU,V=V+αU−λvV.The method part of this article uses the above method for the research of personalized movie recommendation algorithm based on deep learning. The specific process is shown in Figure1.Figure 1
Part of the technical process of this method.


## 3. Experiment on Personalized Movie Recommendation Method Based on Deep Learning

### 3.1. Based on Seq2seq User Interest Model Construction

The personalization of personalized search is reflected in the different search results obtained by different users. First, obtain user interests through related algorithms and express and describe the user’s personalized information. Secondly, the personalized information is integrated into the search algorithm or related operations such as expanding the query sentence during the query process and dynamically adjusting the search results [33]. The Seq2Seq model in deep learning is used to characterize the user’s interest, and then it is integrated into the sorting algorithm of the search engine. The coding end of the Seq2Seq model is based on the LSTM recurrent neural network, which has a longer memory capacity than RNN. The decoder also uses LSTM recurrent neural network [34].First, classify the movie titles according to the authors, put together the movie titles of the same director, and construct them into short texts. All authors in the data set are made into short text data sets; secondly, the data are input into the Seq2Seq model, and the input and output are both It is the same data for training until the loss converges and stabilizes. This process is to adjust the weight parameters of the neural network through training, and let the model capture the input semantic information; again input the relevant short text of each director into the trained code In the device, the output result at the last time step is the interest feature vector of the current audience, and it is saved for subsequent use [35]. Finally, each movie title of the search result must be input into the encoder, the generated vector and the interest vector of the audience currently using the system are calculated for similarity, and the calculation result is merged into the Lucene sorting algorithm, and then the search result is reproduced. Sort to achieve personalized search results [36].


#### 3.1.1. Model Structure

This article uses the Seq2Seq model to express user interests. The whole model constructed in this paper is mainly divided into four levels. The first level is the data embedding layer, which embeds the text data in a vectorized manner. This paper chooses the word embedding method instead of the one-hot method. The second layer is the coding layer, which is based on the LSTM neural network and encodes text data. The third layer is the attention layer. Through the attention layer, the decoder can use a different intermediate vector at each time step. The fourth layer is the decoding layer, and the decoding layer also uses LSTM recurrent neural network to decode and output according to the intermediate vector [37, 38].


#### 3.1.2. Data Preprocessing Vocabulary Construction

The original data need to be processed by removing the stop words, and the stop word list used is the stop word list for English text processing in the natural language processing toolkit (NLTK) related to python. Before the vocabulary is built, punctuation marks in the text are eliminated, which helps to reduce the number of unregistered words. In the process of constructing the vocabulary, the case of words is distinguished, which helps to understand semantics and enhance the expression effect. Construct the vocabulary according to the word frequency and arrange it according to the size of the word frequency. After that, the word is represented by a unique ID, that is, its sorted position in the word frequency database. The word ID is built into a batch and sent to the network, and the network performs word embedding into embedded computing that it is used for subsequent network training [39].


#### 3.1.3. Embedding Processing Layer

The Embedding layer uses the training skip-gram model to construct word vectors. First, the movie title corpus data are counted on word frequency and sorted according to word frequency, and a fixed position is determined for each word. Next, perform one-hot encoding. Finally, a word vector matrix is generated through neural network training, where the row vector corresponding to each position is the distributed word vector of the current word [40].


#### 3.1.4. Attention Layer Construction

It is the introduction the attention mechanism into the model. The basic idea of the attention mechanism is to break the limitation that the traditional encoder-decoder depends on a fixed vector output at the last time step of the encoding stage when decoding. Attention mechanism is realized by saving the intermediate output results generated by the encoder according to the input sequence and then training a model to selectively learn these inputs and associate the output sequence with it when the model is output [41].


### 3.2. Design a Personalized Movie Recommendation System Based on Deep Learning

#### 3.2.1. Demand Analysis

(1) User Needs. The functions that users can use generally include account management, movie search, user ratings, user reviews, personalized movie recommendations, movie nonpersonalized recommendations, and user information management. Account management includes account login, account logout, and account registration; movie search includes movie search and movie playback sources; user information management includes user nickname modification and user password modification.(2) Administrator Requirements. In addition to the user rights, the system administrator needs to undertake the operation and maintenance tasks of the entire movie recommendation system to ensure the normal and stable operation of the system. These include user information management, website traffic statistics, and website information maintenance. User information management includes banning users and modifying user information and website traffic statistics; website information maintenance includes adding movies, deleting movies, updating movie information, deleting comments, and viewing comments.(3) Performance Requirements. Ensuring system performance, high availability, and scalability.


#### 3.2.2. System Structure

This system adopts the B/S architecture, uses the MVC development model as the system’s microsolution, and uses the microservice architecture as the system's macrosolution to design and develop a large-scale Web system. The development of the whole system takes MVC development model as the development model for designing and creating Web applications and chooses SpringMVC as the framework of MVC development model for development.The interface layer is dominated by html pages, which are implemented through technologies such as JavaScript, SpringMVC, and Json. Among them, it mainly communicates with the server through the Http network communication protocol, and the users of the system interact with the system by browsing the web.The business logic layer is mainly implemented by SpringMVC, based on the controller in the MVC development model, connecting the interface layer and the data access layer. Through the business logic layer, the data access layer can write data to the interface layer, and the data access layer can also send data back to the interface layer.The data access layer mainly uses the Hibernate framework to complete object-relational mapping. First, it encapsulates JDBC lightly and establishes a mapping relationship between the model in the MVC development model and the database table to complete data persistence and other operations.


#### 3.2.3. Database Design

Relational Database. The recommendation system needs a lot of data to support when updating the recommendation model, and there is a certain relationship between data and data, and the relational database is based on the relational model, so the commonly used relational database Mysql is used Store system information.(1) Nonrelational Database. This type of data requires a performance-oriented database system. Redis is a nonrelational database, which is based on memory key-value storage, which is more efficient in storage. For example, after logging in to the movie recommendation system, I regret storing the logged-in user’s Session information in Redis. The storage form of the user’s Session information in Redis is as follows: Key = User ID; Value = Session Information.


#### 3.2.4. Core Module Design

(1) Login Module. This module is mainly responsible for the user’s identity verification when using the system and grants different module access permissions based on the user’s identity and the access control list provided by Spring Security.(2) Movie search Module. This module is responsible for searching out the movie corresponding to the user when the user searches for a movie and providing the playback source of the movie to the user.(3) Offline Calculation Module. By clustering the user’s ratings of all movies and the category of the movie, a part of the movies that users are most likely to be interested in are selected as a list of candidate movies, and then a deep network structure model is used to the candidate movie list to recommend and sort, which is based on deep learning.(4) Online Recommendation Module. Sort and recommend the movie candidate list obtained by the offline module by using an improved movie recommendation algorithm based on deep learning.(5) Information Management Module. The system administrator can add, delete, and update information on the movie resources of the system, check the comments of each movie, and delete false comments.(6) Website Traffic Statistics. It is responsible for statistics of various traffic on the website, which is convenient for administrators to do operation and maintenance management.(7) User Information Feedback Module. Support users to give feedback on recommended movie information in the system, including user ratings and user comments.This experiment part proposes the above steps to be used in the research experiment of personalized movie recommendation method based on deep learning. The specific process is shown in Table1.Table 1
Experimental steps in this article.
Research experiment on personalized movie recommendation method based on deep learning3.1. Based on Seq2Seq user interest model construction1.Model structure2.Data preprocessing vocabulary construction3.Embedding processing layer4.Attention layer construction3.2. Design a personalized movie recommendation system based on deep learning1.Demand analysis2.System structure3.Database design4.Core module design


## 3.1. Based on Seq2seq User Interest Model Construction

The personalization of personalized search is reflected in the different search results obtained by different users. First, obtain user interests through related algorithms and express and describe the user’s personalized information. Secondly, the personalized information is integrated into the search algorithm or related operations such as expanding the query sentence during the query process and dynamically adjusting the search results [33]. The Seq2Seq model in deep learning is used to characterize the user’s interest, and then it is integrated into the sorting algorithm of the search engine. The coding end of the Seq2Seq model is based on the LSTM recurrent neural network, which has a longer memory capacity than RNN. The decoder also uses LSTM recurrent neural network [34].First, classify the movie titles according to the authors, put together the movie titles of the same director, and construct them into short texts. All authors in the data set are made into short text data sets; secondly, the data are input into the Seq2Seq model, and the input and output are both It is the same data for training until the loss converges and stabilizes. This process is to adjust the weight parameters of the neural network through training, and let the model capture the input semantic information; again input the relevant short text of each director into the trained code In the device, the output result at the last time step is the interest feature vector of the current audience, and it is saved for subsequent use [35]. Finally, each movie title of the search result must be input into the encoder, the generated vector and the interest vector of the audience currently using the system are calculated for similarity, and the calculation result is merged into the Lucene sorting algorithm, and then the search result is reproduced. Sort to achieve personalized search results [36].


### 3.1.1. Model Structure

This article uses the Seq2Seq model to express user interests. The whole model constructed in this paper is mainly divided into four levels. The first level is the data embedding layer, which embeds the text data in a vectorized manner. This paper chooses the word embedding method instead of the one-hot method. The second layer is the coding layer, which is based on the LSTM neural network and encodes text data. The third layer is the attention layer. Through the attention layer, the decoder can use a different intermediate vector at each time step. The fourth layer is the decoding layer, and the decoding layer also uses LSTM recurrent neural network to decode and output according to the intermediate vector [37, 38].


### 3.1.2. Data Preprocessing Vocabulary Construction

The original data need to be processed by removing the stop words, and the stop word list used is the stop word list for English text processing in the natural language processing toolkit (NLTK) related to python. Before the vocabulary is built, punctuation marks in the text are eliminated, which helps to reduce the number of unregistered words. In the process of constructing the vocabulary, the case of words is distinguished, which helps to understand semantics and enhance the expression effect. Construct the vocabulary according to the word frequency and arrange it according to the size of the word frequency. After that, the word is represented by a unique ID, that is, its sorted position in the word frequency database. The word ID is built into a batch and sent to the network, and the network performs word embedding into embedded computing that it is used for subsequent network training [39].


### 3.1.3. Embedding Processing Layer

The Embedding layer uses the training skip-gram model to construct word vectors. First, the movie title corpus data are counted on word frequency and sorted according to word frequency, and a fixed position is determined for each word. Next, perform one-hot encoding. Finally, a word vector matrix is generated through neural network training, where the row vector corresponding to each position is the distributed word vector of the current word [40].


### 3.1.4. Attention Layer Construction

It is the introduction the attention mechanism into the model. The basic idea of the attention mechanism is to break the limitation that the traditional encoder-decoder depends on a fixed vector output at the last time step of the encoding stage when decoding. Attention mechanism is realized by saving the intermediate output results generated by the encoder according to the input sequence and then training a model to selectively learn these inputs and associate the output sequence with it when the model is output [41].


## 3.1.1. Model Structure

This article uses the Seq2Seq model to express user interests. The whole model constructed in this paper is mainly divided into four levels. The first level is the data embedding layer, which embeds the text data in a vectorized manner. This paper chooses the word embedding method instead of the one-hot method. The second layer is the coding layer, which is based on the LSTM neural network and encodes text data. The third layer is the attention layer. Through the attention layer, the decoder can use a different intermediate vector at each time step. The fourth layer is the decoding layer, and the decoding layer also uses LSTM recurrent neural network to decode and output according to the intermediate vector [37, 38].


## 3.1.2. Data Preprocessing Vocabulary Construction

The original data need to be processed by removing the stop words, and the stop word list used is the stop word list for English text processing in the natural language processing toolkit (NLTK) related to python. Before the vocabulary is built, punctuation marks in the text are eliminated, which helps to reduce the number of unregistered words. In the process of constructing the vocabulary, the case of words is distinguished, which helps to understand semantics and enhance the expression effect. Construct the vocabulary according to the word frequency and arrange it according to the size of the word frequency. After that, the word is represented by a unique ID, that is, its sorted position in the word frequency database. The word ID is built into a batch and sent to the network, and the network performs word embedding into embedded computing that it is used for subsequent network training [39].


## 3.1.3. Embedding Processing Layer

The Embedding layer uses the training skip-gram model to construct word vectors. First, the movie title corpus data are counted on word frequency and sorted according to word frequency, and a fixed position is determined for each word. Next, perform one-hot encoding. Finally, a word vector matrix is generated through neural network training, where the row vector corresponding to each position is the distributed word vector of the current word [40].


## 3.1.4. Attention Layer Construction

It is the introduction the attention mechanism into the model. The basic idea of the attention mechanism is to break the limitation that the traditional encoder-decoder depends on a fixed vector output at the last time step of the encoding stage when decoding. Attention mechanism is realized by saving the intermediate output results generated by the encoder according to the input sequence and then training a model to selectively learn these inputs and associate the output sequence with it when the model is output [41].


## 3.2. Design a Personalized Movie Recommendation System Based on Deep Learning

### 3.2.1. Demand Analysis

(1) User Needs. The functions that users can use generally include account management, movie search, user ratings, user reviews, personalized movie recommendations, movie nonpersonalized recommendations, and user information management. Account management includes account login, account logout, and account registration; movie search includes movie search and movie playback sources; user information management includes user nickname modification and user password modification.(2) Administrator Requirements. In addition to the user rights, the system administrator needs to undertake the operation and maintenance tasks of the entire movie recommendation system to ensure the normal and stable operation of the system. These include user information management, website traffic statistics, and website information maintenance. User information management includes banning users and modifying user information and website traffic statistics; website information maintenance includes adding movies, deleting movies, updating movie information, deleting comments, and viewing comments.(3) Performance Requirements. Ensuring system performance, high availability, and scalability.


### 3.2.2. System Structure

This system adopts the B/S architecture, uses the MVC development model as the system’s microsolution, and uses the microservice architecture as the system's macrosolution to design and develop a large-scale Web system. The development of the whole system takes MVC development model as the development model for designing and creating Web applications and chooses SpringMVC as the framework of MVC development model for development.The interface layer is dominated by html pages, which are implemented through technologies such as JavaScript, SpringMVC, and Json. Among them, it mainly communicates with the server through the Http network communication protocol, and the users of the system interact with the system by browsing the web.The business logic layer is mainly implemented by SpringMVC, based on the controller in the MVC development model, connecting the interface layer and the data access layer. Through the business logic layer, the data access layer can write data to the interface layer, and the data access layer can also send data back to the interface layer.The data access layer mainly uses the Hibernate framework to complete object-relational mapping. First, it encapsulates JDBC lightly and establishes a mapping relationship between the model in the MVC development model and the database table to complete data persistence and other operations.


### 3.2.3. Database Design

Relational Database. The recommendation system needs a lot of data to support when updating the recommendation model, and there is a certain relationship between data and data, and the relational database is based on the relational model, so the commonly used relational database Mysql is used Store system information.(1) Nonrelational Database. This type of data requires a performance-oriented database system. Redis is a nonrelational database, which is based on memory key-value storage, which is more efficient in storage. For example, after logging in to the movie recommendation system, I regret storing the logged-in user’s Session information in Redis. The storage form of the user’s Session information in Redis is as follows: Key = User ID; Value = Session Information.


### 3.2.4. Core Module Design

(1) Login Module. This module is mainly responsible for the user’s identity verification when using the system and grants different module access permissions based on the user’s identity and the access control list provided by Spring Security.(2) Movie search Module. This module is responsible for searching out the movie corresponding to the user when the user searches for a movie and providing the playback source of the movie to the user.(3) Offline Calculation Module. By clustering the user’s ratings of all movies and the category of the movie, a part of the movies that users are most likely to be interested in are selected as a list of candidate movies, and then a deep network structure model is used to the candidate movie list to recommend and sort, which is based on deep learning.(4) Online Recommendation Module. Sort and recommend the movie candidate list obtained by the offline module by using an improved movie recommendation algorithm based on deep learning.(5) Information Management Module. The system administrator can add, delete, and update information on the movie resources of the system, check the comments of each movie, and delete false comments.(6) Website Traffic Statistics. It is responsible for statistics of various traffic on the website, which is convenient for administrators to do operation and maintenance management.(7) User Information Feedback Module. Support users to give feedback on recommended movie information in the system, including user ratings and user comments.This experiment part proposes the above steps to be used in the research experiment of personalized movie recommendation method based on deep learning. The specific process is shown in Table1.Table 1
Experimental steps in this article.
Research experiment on personalized movie recommendation method based on deep learning3.1. Based on Seq2Seq user interest model construction1.Model structure2.Data preprocessing vocabulary construction3.Embedding processing layer4.Attention layer construction3.2. Design a personalized movie recommendation system based on deep learning1.Demand analysis2.System structure3.Database design4.Core module design


## 3.2.1. Demand Analysis

(1) User Needs. The functions that users can use generally include account management, movie search, user ratings, user reviews, personalized movie recommendations, movie nonpersonalized recommendations, and user information management. Account management includes account login, account logout, and account registration; movie search includes movie search and movie playback sources; user information management includes user nickname modification and user password modification.(2) Administrator Requirements. In addition to the user rights, the system administrator needs to undertake the operation and maintenance tasks of the entire movie recommendation system to ensure the normal and stable operation of the system. These include user information management, website traffic statistics, and website information maintenance. User information management includes banning users and modifying user information and website traffic statistics; website information maintenance includes adding movies, deleting movies, updating movie information, deleting comments, and viewing comments.(3) Performance Requirements. Ensuring system performance, high availability, and scalability.


## 3.2.2. System Structure

This system adopts the B/S architecture, uses the MVC development model as the system’s microsolution, and uses the microservice architecture as the system's macrosolution to design and develop a large-scale Web system. The development of the whole system takes MVC development model as the development model for designing and creating Web applications and chooses SpringMVC as the framework of MVC development model for development.The interface layer is dominated by html pages, which are implemented through technologies such as JavaScript, SpringMVC, and Json. Among them, it mainly communicates with the server through the Http network communication protocol, and the users of the system interact with the system by browsing the web.The business logic layer is mainly implemented by SpringMVC, based on the controller in the MVC development model, connecting the interface layer and the data access layer. Through the business logic layer, the data access layer can write data to the interface layer, and the data access layer can also send data back to the interface layer.The data access layer mainly uses the Hibernate framework to complete object-relational mapping. First, it encapsulates JDBC lightly and establishes a mapping relationship between the model in the MVC development model and the database table to complete data persistence and other operations.


## 3.2.3. Database Design

Relational Database. The recommendation system needs a lot of data to support when updating the recommendation model, and there is a certain relationship between data and data, and the relational database is based on the relational model, so the commonly used relational database Mysql is used Store system information.(1) Nonrelational Database. This type of data requires a performance-oriented database system. Redis is a nonrelational database, which is based on memory key-value storage, which is more efficient in storage. For example, after logging in to the movie recommendation system, I regret storing the logged-in user’s Session information in Redis. The storage form of the user’s Session information in Redis is as follows: Key = User ID; Value = Session Information.


## 3.2.4. Core Module Design

(1) Login Module. This module is mainly responsible for the user’s identity verification when using the system and grants different module access permissions based on the user’s identity and the access control list provided by Spring Security.(2) Movie search Module. This module is responsible for searching out the movie corresponding to the user when the user searches for a movie and providing the playback source of the movie to the user.(3) Offline Calculation Module. By clustering the user’s ratings of all movies and the category of the movie, a part of the movies that users are most likely to be interested in are selected as a list of candidate movies, and then a deep network structure model is used to the candidate movie list to recommend and sort, which is based on deep learning.(4) Online Recommendation Module. Sort and recommend the movie candidate list obtained by the offline module by using an improved movie recommendation algorithm based on deep learning.(5) Information Management Module. The system administrator can add, delete, and update information on the movie resources of the system, check the comments of each movie, and delete false comments.(6) Website Traffic Statistics. It is responsible for statistics of various traffic on the website, which is convenient for administrators to do operation and maintenance management.(7) User Information Feedback Module. Support users to give feedback on recommended movie information in the system, including user ratings and user comments.This experiment part proposes the above steps to be used in the research experiment of personalized movie recommendation method based on deep learning. The specific process is shown in Table1.Table 1
Experimental steps in this article.
Research experiment on personalized movie recommendation method based on deep learning3.1. Based on Seq2Seq user interest model construction1.Model structure2.Data preprocessing vocabulary construction3.Embedding processing layer4.Attention layer construction3.2. Design a personalized movie recommendation system based on deep learning1.Demand analysis2.System structure3.Database design4.Core module design


## 4. Personalized Movie Recommendation Method Based on Deep Learning

### 4.1. Development Status of Intelligent Recommendation System

Nowadays, the recommendation system as a kind of intelligent information service has been applied in all walks of life, for example, the well-known e-commerce product recommendation, hot topic recommendation of news websites, high-quality content recommendation of video and music, keyword recommendation of search engines, and recommendation of similar documents in academic websites. This article counts the well-known apps on the market that contain smart recommendations, as shown in Table2.Table 2
App with smart recommendations.
Application fieldAppUsesE-commerceTaobao, Jingdong, Buy together, AmazonOnline shoppingFilm and televisionYoutube, Youku, Tencent video, Bilibili, Aiqiyi, DoubanWatching TV and moviesMusic softwareNetEase Cloud Music, Kugou music, Kuwo music, QQ music, Xiami MusicListen to the musicRadio stationLychee fm, Radio HimalayanListen to the radioNews and informationToutiao, ZiteNews browsingLife serviceMeituan, ElemeEating, lodging, travelingSocial contactQQ, Weibo, WechatExchange, make friendsSearch enginesBaidu, GoogleSearch for things you want to knowThe recommendation system uses the user’s historical behavior information to infer the user’s interests and preferences, so as to provide recommendations for them. The personalized recommendation system can not only make recommendations based on users’ preferences but can also recommend new items that users might like when the users are not clear about their preferences. This is called the novelty of recommendation.


### 4.2. Personalized Movie Recommendation User Analysis

(1)
Count the average usage time of the application platform with movie personalized recommendation function in the daily life of each user layer, and draw it into a chart, as shown in Table3 and Figure 2.It can be seen from the chart that Saturday and Sunday are the two days when users spend the most time online. The platform can set the number of personalized recommendations on Saturdays and Sundays to be more frequent, and there are more types of pushes; users online use time on weekdays after Monday. Relatively short, the platform can be set to accurately push according to users’ likes.(2)
Calculate the time period that users use the application platform with the personalized recommendation function of movies and draw them into charts, as shown in Table4 and Figure 3.It can be seen from the graph that users often use application platforms with personalized movie recommendation functions during commuting hours, lunch time, dinner time, etc. It should be adapted to the current conditions, and more personalized movie recommendations for users during these time periods will surely achieve a better recommendation effect.Figure 2
Average user usage time (unit: minute).Figure 3
User viewing time period.Table 3
Average user usage time (unit: minute).
UserMondayTuesdayWednesdayThursdayFridaySaturdaySundayPrimary and secondary school students112.3106.4101.2110.1116.4134.8141.2College students164.1171.3164.6175.3154.9212.3230.6Office worker108.692.795.6100.5124.6176.7200.4Table 4
User viewing time period.
PeriodPercentage (%)0 : 00–6 : 003.626 : 00–9:006.239 : 00–12 : 0013.9112 : 00–15 : 0020.3415 : 00–18 : 0019.1218 : 00–21 : 0023.6721 : 00–24 : 0013.11


### 4.3. Experimental Results

(1)
This paper calculates the similarity algorithm for the collaborative filtering algorithm based on personalized recommendation and the Seq2Seq model, fuses the similarity through the fusion algorithm, and finally completes the movie recommendation. The recommendation is the Top-N method. Analyze and compare relevant experimental results. Through the fusion algorithm, the similarity between the movie similarity obtained by the item-based collaborative filtering recommendation algorithm (Item-CF) and the movie based on the Seq2Seq model is fused and statistically sorted out. Draw a chart, as shown in Table5 and Figure 4.It can be seen from the chart that the similar fusion ratio of Item-CF and Seq2Seq is 1 : 0, which means the index result is obtained by the Item-CF algorithm. It can be seen from various evaluation indicators that when the Seq2Seq fusion ratio is relatively high, the coverage rate is significantly higher than that of the traditional collaborative filtering algorithm. As the proportion of Seq2Seq fusion decreases, the accuracy, recall, and novelty are all improved, while the coverage rate gradually decreases.(2)
The data set used in the experiment is the MovieLens data set collected by the GroupLens team of the University of Minnesota in the United States. It records users’ ratings of movies and is a standard data set for personalized recommendation algorithm evaluation. The MovieLens dataset used in this article includes MovieLens-100k dataset and MovieLens-1M dataset. Based on the MovieLens-100k data set and MovieLens-1M data set, starting from three aspects, the output size of the hidden layer is 8. Regarding the models GMF, MLP, NeuMF, and Seq2Seq, the accuracy and diversity of the two data sets are compared Experiment to verify the superiority of the Seq2Seq model is proposed in this chapter. The specific results are shown in Table6 and Figure 5.It can be seen from the graph that, as the user activity increases, the accuracy fluctuates slightly and the diversity rises slightly; the increase in activity indicates that the proportion of user activity obtained by clustering gradually increases, compared to pure. The diversity characteristics of the activity derived from the number of times the project is evaluated are more obvious and bring more gains.(3)
A three-layer LSTM network model is built, the number of LSTM neurons is set to 131, the learning is 0.002, the dropout value is 0.2, the number of training rounds Epoch is 27, the batch size Batch_size is 35, Adam is the model optimizer, and Sigmoid function is used as the activation function. When the model is stable, the setting is that the LSTM layer contains 131 neurons and the batch_size of each batch of data is 70. After 27 iterations, the model achieves better results. The dropout value is 0.23, and Adam as a model optimizer, the sigmoid function is used as the final activation function of the model. The specific conditions of the model’s loss function and accuracy rate change trend on the test set and training set are shown in Table7 and Figure 6.It can be seen from the graph that the loss function and accuracy of the model gradually stabilized after the epoch reached 15, and the loss function remained around 0.0080. The accuracy of the final model training set reached 96.27%, and the accuracy rate of the test set reached 95.89%. Convergence speed and model accuracy are better than traditional RNN models. Compared with traditional RNN models, processing performance has been greatly improved.Figure 4
Item-based collaborative filtering recommendation algorithm and Seq2Seq fusion results in different proportions.Figure 5
Comparison of experimental results.Figure 6
Loss function and accuracy rate changes.Table 5
Item-CF and Seq2Seq fusion results in different proportions.
NumberSimilar fusion ratio of Item-CF and Seq2seqAccuracy (%)Recall rate (%)Coverage (%)Novelty11 : 039.814.2129.614.3121 : 142.314.6732.334.2631 : 237.423.9436.074.5447 : 135.674.5641.204.62514 : 142.335.0839.264.70619 : 146.244.7836.443.96720 : 648.095.1232.524.33848 : 139.214.8329.074.27950 : 436.523.6426.434.391067 : 237.433.2723.674.511192 : 141.662.9829.114.6312100 : 149.572.5130.274.71Table 6
Comparative experimental results.
User activityMovieLens-100kMovieLens-1MHRNDCGILSHRNDCGILS0.20.64810.34210.45680.71260.50270.46130.30.65270.35190.46310.72170.51120.47170.40.65820.36070.47240.73050.52040.48060.50.66430.36870.48110.73960.52930.49120.60.67090.37240.49010.74210.53480.5071Table 7
Loss function and accuracy rate changes.
EpochLoss trainingLoss testAccuracy training (%)Accuracy test (%)0∼30.01520.014671.2464.144∼60.01310.013779.5675.637∼90.01270.012182.3180.4110∼120.01140.011785.4782.6713∼150.00970.010690.2689.1716∼180.00860.009592.5193.6719∼210.00820.008993.3894.5922∼240.00780.007495.4695.2125∼270.00730.006996.2795.89


## 4.1. Development Status of Intelligent Recommendation System

Nowadays, the recommendation system as a kind of intelligent information service has been applied in all walks of life, for example, the well-known e-commerce product recommendation, hot topic recommendation of news websites, high-quality content recommendation of video and music, keyword recommendation of search engines, and recommendation of similar documents in academic websites. This article counts the well-known apps on the market that contain smart recommendations, as shown in Table2.Table 2
App with smart recommendations.
Application fieldAppUsesE-commerceTaobao, Jingdong, Buy together, AmazonOnline shoppingFilm and televisionYoutube, Youku, Tencent video, Bilibili, Aiqiyi, DoubanWatching TV and moviesMusic softwareNetEase Cloud Music, Kugou music, Kuwo music, QQ music, Xiami MusicListen to the musicRadio stationLychee fm, Radio HimalayanListen to the radioNews and informationToutiao, ZiteNews browsingLife serviceMeituan, ElemeEating, lodging, travelingSocial contactQQ, Weibo, WechatExchange, make friendsSearch enginesBaidu, GoogleSearch for things you want to knowThe recommendation system uses the user’s historical behavior information to infer the user’s interests and preferences, so as to provide recommendations for them. The personalized recommendation system can not only make recommendations based on users’ preferences but can also recommend new items that users might like when the users are not clear about their preferences. This is called the novelty of recommendation.


## 4.2. Personalized Movie Recommendation User Analysis

(1)
Count the average usage time of the application platform with movie personalized recommendation function in the daily life of each user layer, and draw it into a chart, as shown in Table3 and Figure 2.It can be seen from the chart that Saturday and Sunday are the two days when users spend the most time online. The platform can set the number of personalized recommendations on Saturdays and Sundays to be more frequent, and there are more types of pushes; users online use time on weekdays after Monday. Relatively short, the platform can be set to accurately push according to users’ likes.(2)
Calculate the time period that users use the application platform with the personalized recommendation function of movies and draw them into charts, as shown in Table4 and Figure 3.It can be seen from the graph that users often use application platforms with personalized movie recommendation functions during commuting hours, lunch time, dinner time, etc. It should be adapted to the current conditions, and more personalized movie recommendations for users during these time periods will surely achieve a better recommendation effect.Figure 2
Average user usage time (unit: minute).Figure 3
User viewing time period.Table 3
Average user usage time (unit: minute).
UserMondayTuesdayWednesdayThursdayFridaySaturdaySundayPrimary and secondary school students112.3106.4101.2110.1116.4134.8141.2College students164.1171.3164.6175.3154.9212.3230.6Office worker108.692.795.6100.5124.6176.7200.4Table 4
User viewing time period.
PeriodPercentage (%)0 : 00–6 : 003.626 : 00–9:006.239 : 00–12 : 0013.9112 : 00–15 : 0020.3415 : 00–18 : 0019.1218 : 00–21 : 0023.6721 : 00–24 : 0013.11


## 4.3. Experimental Results

(1)
This paper calculates the similarity algorithm for the collaborative filtering algorithm based on personalized recommendation and the Seq2Seq model, fuses the similarity through the fusion algorithm, and finally completes the movie recommendation. The recommendation is the Top-N method. Analyze and compare relevant experimental results. Through the fusion algorithm, the similarity between the movie similarity obtained by the item-based collaborative filtering recommendation algorithm (Item-CF) and the movie based on the Seq2Seq model is fused and statistically sorted out. Draw a chart, as shown in Table5 and Figure 4.It can be seen from the chart that the similar fusion ratio of Item-CF and Seq2Seq is 1 : 0, which means the index result is obtained by the Item-CF algorithm. It can be seen from various evaluation indicators that when the Seq2Seq fusion ratio is relatively high, the coverage rate is significantly higher than that of the traditional collaborative filtering algorithm. As the proportion of Seq2Seq fusion decreases, the accuracy, recall, and novelty are all improved, while the coverage rate gradually decreases.(2)
The data set used in the experiment is the MovieLens data set collected by the GroupLens team of the University of Minnesota in the United States. It records users’ ratings of movies and is a standard data set for personalized recommendation algorithm evaluation. The MovieLens dataset used in this article includes MovieLens-100k dataset and MovieLens-1M dataset. Based on the MovieLens-100k data set and MovieLens-1M data set, starting from three aspects, the output size of the hidden layer is 8. Regarding the models GMF, MLP, NeuMF, and Seq2Seq, the accuracy and diversity of the two data sets are compared Experiment to verify the superiority of the Seq2Seq model is proposed in this chapter. The specific results are shown in Table6 and Figure 5.It can be seen from the graph that, as the user activity increases, the accuracy fluctuates slightly and the diversity rises slightly; the increase in activity indicates that the proportion of user activity obtained by clustering gradually increases, compared to pure. The diversity characteristics of the activity derived from the number of times the project is evaluated are more obvious and bring more gains.(3)
A three-layer LSTM network model is built, the number of LSTM neurons is set to 131, the learning is 0.002, the dropout value is 0.2, the number of training rounds Epoch is 27, the batch size Batch_size is 35, Adam is the model optimizer, and Sigmoid function is used as the activation function. When the model is stable, the setting is that the LSTM layer contains 131 neurons and the batch_size of each batch of data is 70. After 27 iterations, the model achieves better results. The dropout value is 0.23, and Adam as a model optimizer, the sigmoid function is used as the final activation function of the model. The specific conditions of the model’s loss function and accuracy rate change trend on the test set and training set are shown in Table7 and Figure 6.It can be seen from the graph that the loss function and accuracy of the model gradually stabilized after the epoch reached 15, and the loss function remained around 0.0080. The accuracy of the final model training set reached 96.27%, and the accuracy rate of the test set reached 95.89%. Convergence speed and model accuracy are better than traditional RNN models. Compared with traditional RNN models, processing performance has been greatly improved.Figure 4
Item-based collaborative filtering recommendation algorithm and Seq2Seq fusion results in different proportions.Figure 5
Comparison of experimental results.Figure 6
Loss function and accuracy rate changes.Table 5
Item-CF and Seq2Seq fusion results in different proportions.
NumberSimilar fusion ratio of Item-CF and Seq2seqAccuracy (%)Recall rate (%)Coverage (%)Novelty11 : 039.814.2129.614.3121 : 142.314.6732.334.2631 : 237.423.9436.074.5447 : 135.674.5641.204.62514 : 142.335.0839.264.70619 : 146.244.7836.443.96720 : 648.095.1232.524.33848 : 139.214.8329.074.27950 : 436.523.6426.434.391067 : 237.433.2723.674.511192 : 141.662.9829.114.6312100 : 149.572.5130.274.71Table 6
Comparative experimental results.
User activityMovieLens-100kMovieLens-1MHRNDCGILSHRNDCGILS0.20.64810.34210.45680.71260.50270.46130.30.65270.35190.46310.72170.51120.47170.40.65820.36070.47240.73050.52040.48060.50.66430.36870.48110.73960.52930.49120.60.67090.37240.49010.74210.53480.5071Table 7
Loss function and accuracy rate changes.
EpochLoss trainingLoss testAccuracy training (%)Accuracy test (%)0∼30.01520.014671.2464.144∼60.01310.013779.5675.637∼90.01270.012182.3180.4110∼120.01140.011785.4782.6713∼150.00970.010690.2689.1716∼180.00860.009592.5193.6719∼210.00820.008993.3894.5922∼240.00780.007495.4695.2125∼270.00730.006996.2795.89


## 5. Conclusions

In recent years, with the rapid development of information technology and the Internet, watching movies through the Internet has become a habit of many people. However, because people cannot quickly obtain their favorite movie content from the massive movie resources, this makes the overload of movie information more and more serious. As one of the important means to alleviate the problem of information overload, the recommendation system can help users quickly find favorite movie content and bring users a good experience. Therefore, it is widely used in well-known movie and video websites at home and abroad and has great commercial value.The advent of the 5 G era has put forward higher requirements for Internet technology. As one of the important areas, the recommendation system has been deeply applied to all aspects of the Internet. It helps people provide solutions from massive information retrieval and changes the original passive search method. To take the initiative and bring certain economic benefits to related enterprises, it has been widely recognized by the industry. At the same time, deep learning technology has shined in the field of classification and prediction in recent years, but in the field of recommendation, the application of this technology is still in a period of rapid growth. How to combine the two more effectively is an important direction of current research.This article begins by focusing on the research background and significance of the thesis. Through the research and judgment of the current research status in this field, combined with the development and application of algorithm technology, it analyzes and summarizes the common problems in the algorithm. Then, the related theories and technologies needed in this article are researched and organized, several basic algorithms of recommendation systems and the current popular social network-based recommendation algorithms are theoretically studied, and then the foundation of deep learning algorithms is explained. This article has conducted certain research on the movie recommendation system and its combination with deep learning, but there are still many shortcomings, which require continuous learning and hard work. The data set used in the model test in this paper is very small compared with the calculation required for actual application, and there is still a certain gap in the amount of data.



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*Source: 6694237-2021-02-19.xml*