# Analysis of TV Film Scoring and Communication Efficiency Based on Artificial Intelligence

Authors: Yue Xian
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The spread of TV movies has brought free participation in film reviews. Movies have brought freedom of time and space for comments. You can publish your own comments at any time without time restrictions. You can publish comments on forums, blogs, vote, and various websites, film companies, and even individuals to hold special online film competitions or film review activities. With the gradual maturity of film types, a series of subtypes such as artificial intelligence, interstellar civilization, and alien species have been derived. These contents have different emphases on type characteristics and narrative characteristics, although the subtypes of films have some common characteristics in essence. Based on artificial intelligence, this paper studies the scoring analysis and communication efficiency of TV and film. By adjusting the recommendation through user feedback, the average absolute error has been improved by 18% and the accuracy has been improved by 24%. Moreover, through four rounds of feedback, the accuracy of online computing recommendation for new users is very close to that for old users. This shows the superiority of artificial intelligence algorithm in solving the problems of cold start and complex interest recommendation. It can be predicted that artificial intelligence will become a unique cultural phenomenon. Its cultural connotation is all inclusive and worthy of continuous research and exploration by future generations. Artificial intelligence appears more and more frequently in science fiction films and presents more rich and detailed diversity and complexity.


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## Body

## 1. Introduction

“Internet plus,” big data, intelligence, mobility, and video, these words have become the service means of today’s movie communication. We can not only watch movies when we pick up our mobile phones but also score movies. The spread of movies has brought about the free participation of movie reviews [1]. On the one hand, movies bring the freedom of time and space for comments. You can express your comments at any time, not limited by time. You can publish comments in forums and blogs, and you can publish votes. There are also various websites, film companies, and even individuals who hold special online movie competitions or film reviews [2, 3]. On the other hand, the film reviews also broke through the limitations of evaluation methods. In the past, the simple way of text comment has been far from satisfying the colorful imagination of netizens, and the comments of movies are mixed with various ways such as text, images, audio, and video editing [4]. Under the influence of the media, the audience has formed an unprecedented “intimate distance” with the film, and the way of film communication has undergone fundamental changes, which has played an important and unpredictable role in rapidly promoting film communication, enhancing film influence and enhancing film value [5]. The spread of Chinese films overseas has promoted the continuous improvement of China’s national image, and the data shows that the respondents know about China through movies the most frequently, which is higher than traditional media such as online media and newspapers. At the same time, the research also shows that movies can deepen the respondents’ understanding of China’s social culture and national conditions, which shows that movies, as a media, have obvious advantages in cross-cultural communication [6].With the gradual maturity of film types, a series of subtypes such as artificial intelligence, interstellar civilization, and alien species have been derived. These contents have different emphases on type characteristics and narrative characteristics. Although the subtypes of films have some common characteristics in essence, placing these contents in the major categories of films is not conducive to deepening the type research and the law of type narration, Therefore, on the basis of diachrony and synchronicity, the study of film types needs to conduct in-depth exploration in the unit of subtypes. Analyzing the laws presented by this subtype in the development process from the perspective of narration can decrypt its unique type characteristics and narrative charm [7, 8]. Artificial intelligence technology has become more and more mature in the 1990s. In the 1990s, the rapid development of modern high technology has brought a series of revolutionary changes. Perfect digital technology and amazing computer-generated stunt “transformation imaging” have replaced the traditional science fiction films based on models and stunt shooting lenses [9, 10]. In the 21st century, artificial intelligence has become a scientific and technological innovation leading the new trend and has increasingly become a hot topic in contemporary society. Today, artificial intelligence appears more and more frequently in science fiction films and presents more rich and detailed diversity and complexity [11, 12].At present, the most widely used place of artificial intelligence in society is big data analysis. For the film industry, big data analysis can distinguish what people like and do not like by analyzing the audience’s preferences and social reactions, including themes, scenes, and people’s settings, so that we can really “prescribe the right medicine” and make movies aimed at the audience and the box office, thus making movies that most audiences like and creating so-called “explosions.” Super artificial intelligence is different from strong artificial intelligence. Strong artificial intelligence has broad psychological ability and can carry out operations such as thinking, planning, problem solving, abstract thinking, and quick learning. If the capabilities of strong artificial intelligence are comparable to those of human beings, then super artificial intelligence is much smarter than the smartest human brain in almost all fields, including scientific innovation, general knowledge, and social skills. It has to be admitted that artificial intelligence can indeed help human beings to better complete film works, but as Ma Zhongjun, chairman of Ciwen Media Group, said: “Good works are not equal to good products, and good products are not equal to good works [13, 14].” The creative thinking of works of art requires considering the influence on the human heart and the whole social consciousness. Everyone can express different views and emotions in works of art, which is the home of human spirit [15]. We can foresee that artificial intelligence will become a unique cultural phenomenon, which contains all-encompassing cultural connotations, and is worthy of continuous research and exploration by future generations.The innovation of this paper is as follows.(1) In this paper, a model of TV movie scoring analysis and communication efficiency based on artificial intelligence is built. At present, there is no effective online computing method to solve the complex interest recommendation problem that users’ interests suddenly change or many people share an account. The newly acquired tag weight score data is used as the user’s temporary tag weight feature, and the online computing recommendation algorithm is called to recommend items. After obtaining the basic information and preference data of users, the article recommendation for each user is generated and the result is stored. Finally, when users use the recommendation system, the recommended items are displayed through the recommendation result processing module, and the recommendation reasons are explained in the form of tag weight score(2) Based on the artificial intelligence, a movie scoring and communication efficiency system is established. According to the analysis of the user’s behavior preference by the artificial intelligence system, it can be seen that the user’s behavior preference is influenced by various characteristic attributes of the user, and the user’s behavior preference and the characteristic attributes of the movie together determine the user’s preference for this movie. Therefore, the prediction of the user’s preference for movies is essentially based on the established modelThe overall structure of this paper is divided into five parts.The first section introduces the background and significance of film scoring and communication efficiency and then introduces the main work of this paper. The second section mainly introduces the related work of film scoring and communication efficiency at home and abroad. The third section introduces the algorithm and model of artificial intelligence. The fourth section introduces the implementation of film scoring and communication efficiency system and the analysis of the experimental part. The fifth section is the summary of the full text.


## 2. Related Work

### 2.1. Research Status at Home and Abroad

Li and Kim proposed Bayesian network to describe the interdependence between advertising keywords and their click through rate in the Internet, build a BN through the user’s advertising click through rate data, and then predict the click through rate for the advertisements that the user may search [16]. Yetginler et al. proposed that when Chinese films participate in the construction of global film cultural pattern, they reflect and express people’s survival from the perspective of globalization and imaginatively meet people’s desire and anxiety in the era of globalization and have begun to construct a cultural symbol system with global consciousness [17]. Jani et al. proposed that films are more classified as the field of art for pancultural research and intensive reading and split the attributes and characteristics of individuals as media and the role and methods as communication tools, the influence of communication mode, and the acceptance and feedback of audiences [18]. Wilson and McGill proposed a Bayesian network to build a model representing the user’s portrait preference. The user’s scores on multiple dimensions of goods are regarded as hidden variables in the model. The BN structure is constructed according to the specific relationship between the scores of each dimension and the final score, based on the expectation maximization algorithm [19]. Bristi et al. put forward that the traditional cinematography research that focuses on the systematic research inside the film at a general level. It includes the study of film language, lens, aesthetics, and even the history of film. Indeed, whether it was Bakhtin, Metz, or Vertov, they made an indelible contribution to the field of cinematic audiovisuality in their initial film studies [20]. Gillick and Bamman differences in cultural background, aesthetic expectation, language, historical tradition, and other factors are one of the important reasons leading to cultural discount. In order to reduce cultural discount, we can choose the target market with strong cultural affinity. The cultural discount can be summarized into two aspects: the relationship between gatekeeper and audience and the power in the field. They believe that the main reason of cultural discount is the difference caused by the cultural power structure [21]. Nurik pointed out that when western theories are widely used for reference to explain the current film and television, there is general indigestion, and there is a lack of independent thinking and creativity in the wave of pancultural studies and postmodern studies. This is especially manifested in the research field of cinematology. Although there is no dispute based on the attribute of film as a media, the theoretical circle often separates film from communication [22]. Ge et al. put forward that film and television products rooted in a certain cultural context are far more attractive in the domestic market than in foreign markets, because the social culture and lifestyle familiar to the audience in the domestic market are closer to the concepts expressed by film and television products; however, the audiences in other places or countries may find it difficult to accept the cultural ideas or values conveyed by the film and television products because of their different lifestyles and historical backgrounds [23]. Feng pointed out that from the reality of cross-cultural communication of Chinese films, although the scale of Chinese film industry and box office revenue have increased tremendously, Chinese films still failed to achieve effective cross-cultural communication in the international market [24]. Zhou proposed a Bayesian nonparametric method. Firstly, it was assumed that several interest points in the user’s interest preference portrait directly drove the user’s behavior. Then, by using the beta process and Dirichlet prior probability, a series of hidden interests shared with the user were found in the behavior data, and finally, the user’s behavior preference was described according to the user’s interest points [25].


### 2.2. Research Status of Film Scoring and Communication Based on Artificial Intelligence

To sum up, previous studies on film scoring analysis and communication efficiency mostly stay at the macronarrative level, and few studies specifically discuss the influencing factors in film scoring analysis and communication efficiency. This paper investigates the rating analysis and distribution efficiency of TV films based on artificial intelligence, proposes independent variables in artificial intelligence that may influence the popularity of TV films, identifies specific factors influencing the popularity of TV films in cross-distribution using quantitative methods of content analysis, and concretizes the mechanism of their influence on the outreach of TV films, proposing a new target for film quality. Although the current film scoring is not satisfactory, it is undeniable that the film scoring under artificial intelligence better represents the evaluation of film quality by most film consumers, and the film scoring is more objective, more transparent, and more valuable. If there is a bad film with low score, the film producer must be careful. Leading with high scores and good films is bound to stimulate the quality of Chinese films to a higher level. Therefore, the existence of film scoring plays a positive role in promoting the quality of Chinese film communication. Television industrialization, professional channels, separation of production and broadcasting, digital television and so on, almost the grand theories and small narratives related to television have been “enclosure movement” in the academic circles. Under the increasingly impetuous academic circle under the whole artificial intelligence and the gradually expanding aura of the whole media circle, panmedia education also appears vigorous. The number of film and television majors in Chinese universities and the overall low academic level have become an important bottleneck restricting the development of Chinese film and television.


## 2.1. Research Status at Home and Abroad

Li and Kim proposed Bayesian network to describe the interdependence between advertising keywords and their click through rate in the Internet, build a BN through the user’s advertising click through rate data, and then predict the click through rate for the advertisements that the user may search [16]. Yetginler et al. proposed that when Chinese films participate in the construction of global film cultural pattern, they reflect and express people’s survival from the perspective of globalization and imaginatively meet people’s desire and anxiety in the era of globalization and have begun to construct a cultural symbol system with global consciousness [17]. Jani et al. proposed that films are more classified as the field of art for pancultural research and intensive reading and split the attributes and characteristics of individuals as media and the role and methods as communication tools, the influence of communication mode, and the acceptance and feedback of audiences [18]. Wilson and McGill proposed a Bayesian network to build a model representing the user’s portrait preference. The user’s scores on multiple dimensions of goods are regarded as hidden variables in the model. The BN structure is constructed according to the specific relationship between the scores of each dimension and the final score, based on the expectation maximization algorithm [19]. Bristi et al. put forward that the traditional cinematography research that focuses on the systematic research inside the film at a general level. It includes the study of film language, lens, aesthetics, and even the history of film. Indeed, whether it was Bakhtin, Metz, or Vertov, they made an indelible contribution to the field of cinematic audiovisuality in their initial film studies [20]. Gillick and Bamman differences in cultural background, aesthetic expectation, language, historical tradition, and other factors are one of the important reasons leading to cultural discount. In order to reduce cultural discount, we can choose the target market with strong cultural affinity. The cultural discount can be summarized into two aspects: the relationship between gatekeeper and audience and the power in the field. They believe that the main reason of cultural discount is the difference caused by the cultural power structure [21]. Nurik pointed out that when western theories are widely used for reference to explain the current film and television, there is general indigestion, and there is a lack of independent thinking and creativity in the wave of pancultural studies and postmodern studies. This is especially manifested in the research field of cinematology. Although there is no dispute based on the attribute of film as a media, the theoretical circle often separates film from communication [22]. Ge et al. put forward that film and television products rooted in a certain cultural context are far more attractive in the domestic market than in foreign markets, because the social culture and lifestyle familiar to the audience in the domestic market are closer to the concepts expressed by film and television products; however, the audiences in other places or countries may find it difficult to accept the cultural ideas or values conveyed by the film and television products because of their different lifestyles and historical backgrounds [23]. Feng pointed out that from the reality of cross-cultural communication of Chinese films, although the scale of Chinese film industry and box office revenue have increased tremendously, Chinese films still failed to achieve effective cross-cultural communication in the international market [24]. Zhou proposed a Bayesian nonparametric method. Firstly, it was assumed that several interest points in the user’s interest preference portrait directly drove the user’s behavior. Then, by using the beta process and Dirichlet prior probability, a series of hidden interests shared with the user were found in the behavior data, and finally, the user’s behavior preference was described according to the user’s interest points [25].


## 2.2. Research Status of Film Scoring and Communication Based on Artificial Intelligence

To sum up, previous studies on film scoring analysis and communication efficiency mostly stay at the macronarrative level, and few studies specifically discuss the influencing factors in film scoring analysis and communication efficiency. This paper investigates the rating analysis and distribution efficiency of TV films based on artificial intelligence, proposes independent variables in artificial intelligence that may influence the popularity of TV films, identifies specific factors influencing the popularity of TV films in cross-distribution using quantitative methods of content analysis, and concretizes the mechanism of their influence on the outreach of TV films, proposing a new target for film quality. Although the current film scoring is not satisfactory, it is undeniable that the film scoring under artificial intelligence better represents the evaluation of film quality by most film consumers, and the film scoring is more objective, more transparent, and more valuable. If there is a bad film with low score, the film producer must be careful. Leading with high scores and good films is bound to stimulate the quality of Chinese films to a higher level. Therefore, the existence of film scoring plays a positive role in promoting the quality of Chinese film communication. Television industrialization, professional channels, separation of production and broadcasting, digital television and so on, almost the grand theories and small narratives related to television have been “enclosure movement” in the academic circles. Under the increasingly impetuous academic circle under the whole artificial intelligence and the gradually expanding aura of the whole media circle, panmedia education also appears vigorous. The number of film and television majors in Chinese universities and the overall low academic level have become an important bottleneck restricting the development of Chinese film and television.


## 3. Algorithms and Models of Artificial Intelligence

With the development of artificial intelligence, human’s attitude towards artificial intelligence has changed, and the relationship between artificial intelligence and human is worth reexamining. When the interaction between artificial intelligence and human becomes more harmonious, artificial intelligence is no longer just a threat to human beings but also a friend or partner of human beings. In this way, the structural complexity of the model can be simplified according to the hidden variables, and it is easier to complete and get the optimal solution when learning and reasoning the model. At the same time, through the introduction of grouping information, the domain knowledge is combined with attribute grouping, and the probability dependence between attributes is limited when learning the model, thus avoiding the problem of randomness in the BN learning process and greatly improving the efficiency of model learning. Artificial intelligence TV movie scoring system is mainly divided into data source module, recommendation engine module, recommendation result processing module, and user feedback module. The specific explanation is as follows.


### 3.1. Data Source Module

Data sourceD is the basis source for recommendation by the recommendation system, mainly including user information set u, item information set I, user preference score information r for items, and user label weight score s for items. The main task of the data source module is to obtain and preprocess the data source D.


### 3.2. Recommendation Engine Module

Recommendation engineE is the core of recommendation system. Its main function is to use recommendation algorithm to process and analyze the information from data source D and recommend the most needed item set I (U) for users according to certain recommendation standards.


### 3.3. Recommended Result Processing Module

After the recommendation engine calculates the initial list of items recommended to users, we should filter and rank the recommended items and add the corresponding tag weight to explain the reasons for recommending these items to users.


### 3.4. User Feedback Module

After seeing the recommended items and recommendation explanations provided by the recommendation system, users can give corresponding user information feedback. The user feedback module collects and processes the feedback information and transmits it to the recommendation engine module to complete a new round of recommendation.The architecture of TV movie scoring system based on artificial intelligence is shown in Figure1.Figure 1
Architecture diagram of movie scoring system of artificial intelligence.Artificial intelligence is the product of human worshipping the industrial revolution, trying to break through its own limitations and enjoying scientific and technological achievements. The reason why human beings create artificial intelligence machines is based on real needs. At first, the artificial intelligence in reality was created to meet the real needs of human beings, and it mainly existed as a tool of labor service. For example, disabled people can take care of themselves, scientific and technological calculations can be faster and more accurate, and various industries can generate income and save energy. In short, artificial intelligence exists to make human production and life more convenient. At present, there is no effective online computing method to solve the complex interest recommendation problem that users’ interests suddenly change or many people share an account. However, similar to solving the cold start problem, we allow users to accurately express their current interest preferences through the tag weight scoring mechanism. The system takes the newly acquired tag weight scoring data as the user’s temporary tag weight feature and calls online computing recommendation algorithm to recommend items. We should add restrictions to this situation, remove this kind of pseudoproposition structure in the process of model search, and avoid judging this kind of model as the optimal model structure in the process of search. Therefore, we combine the domain knowledgeW with the grouping information in the model, organize it into several rules, and add the set of dependency restriction rules to the search space constraints of heuristic model learning, so as to optimize the structural space of the candidate model and improve the efficiency of the algorithm. After obtaining the user’s basic information and preference data, the data source module first preprocesses, then calls the corresponding recommendation algorithm to calculate, generates the item recommendation for each user, and stores the result. Aiming at the prediction of user’s behavior preference by the model, according to the model’s strengths, a model reasoning algorithm based on mission tree propagation is proposed, and the user’s score is calculated by using the principle of diminishing marginal utility, and finally, the prediction of user’s preference is completed. Experiments show that in the proposed model framework, the corresponding construction method and reasoning method are efficient and feasible. Finally, when users use the recommendation system, the recommended items are displayed through the recommendation result processing module, and the recommendation reasons are explained in the form of tag weight score. The model of the algorithm is shown in Figure 2.Figure 2
Artificial intelligence algorithm model.In order to predict the user’s preference for movies according to the user behavior preference model based on movie scoring data, first, the user’s score about movies needs to be obtained through reasoning according to the user and movie feature attributes. In this paper, the value with the maximum posterior probability of evaluation differentiation is taken as the reasoning result of scoring, that is, given user attributes and movie attributes. The recommendation algorithm based on item score is also called item-based artificial intelligence algorithm. The core idea of the algorithm is to recommend items similar to the items they once liked. The algorithm is divided into two steps: one is to calculate the similarity between items; the second is to generate a recommendation list for users through the similarity of items and the user item scoring matrix. The idea of similarity measurement between two items is to calculate the “distance” between samples. The similarity between projecti and project j is generally recorded as simi,j. The smaller the value of similarity, the higher the similarity between items.In the user-item scoring matrix, let useri and user j score items i⟶ and j⟶, respectively, in n dimension space, and then, the similarity simi,j between item i and item j is
(1)simi,j=cosi⟶,j⟶=i⟶•j⟶i⟶∗j⟶.Pearson correlation coefficient similarity: let Ui,j be the set of users who have scored project i and project j together, and then, the formula simi,j for calculating the similarity between project i and project j is expressed as follows
(2)simi,j=∑c∈UijRc,i−R−Rc,j−R−∑c∈UijRc,i−Ri−2,where Rc,i represents the rating of user c on item i, and Ri− and Rj− represent the average rating of item i and item jLetIij be the set of all users who have scored i and j together, and Ii and Ij represent all users who have scored i and j, respectively, and then, the formula simi,j for calculating the similarity between item i and item j is as follows
(3)simi,j=∑c∈IijRc,i−Rc−Rc,j−Rc−∑c∈IiRc,i−Rc−2∑c∈IjRc,j−Rc−2,whereRc,i represents the scoring data of the user c on the project i, Rc,j represents the scoring data of the user on the project j, and Rc− represents the average score of the user on the project.After calculating the similarity between items, the formula is usually used to calculate the user’su preference for item j(4)ρuj=∑i∈Nu∩Sj,Kωjirui,whereNu represents the collection of items that users like, Sj,K is the collection of the first K projects most similar to project j, ωij represents the similarity between project i and project j, and rui indicates the user’s u preference for the item i.Average prediction algorithm is to use the average score to predict the user’s score of the project, and it can be divided into the following two algorithms.Global average: it is defined as the average score of all score records in the training set.(5)r−ui=∑u,i∈Trainrui∑u,i∈Train1,where U represents the user, i represents the project, rui represents the score of user u on the project i, and Train represents the training set.verage value of user classification to item classification: Based on the global average value, this algorithm defines two classification functions, one is user classification functionφ and the other is item classification function ϕ. φu defines the class to which user u belongs, and ϕi defines the class to which project i belongs. The idea of the algorithm is to predict the user’s score on the item by using the average of the user’s scores on the item of the same set in the training set
(6)r∧ui=∑υ,j∈Train,φu=φυrij∑υ,j∈Train,φυ=φυ1.The idea of user-based algorithm is that to predict a user’s rating on a project, it is necessary to add users with similar interests to the user’s rating on the project.(7)rui=r−u+∑V∈Su,k∩NiWuυrυi−r−υ∑V∈Su,k∩NiWuυ,whereu,k is the set of k users who are the most similar to user’s u interests, Ni is the set of users who overestimate the project, Ni is the set of users who overestimate the project, rυi is the score of user υ on project i, and r−υ is the average of all the scores of user υ who overestimate him.The idea of the item-based algorithm is that when predicting the score of useru on item i, it is necessary to add the score of user u on other items similar to item i(8)r∧ui=r−i+∑j∈Si,k∩NuWijruj−rj−∑j∈Si,k∩NuWij,whereSi,k is the set of all items most similar to i, Nu is the set of all items over rated by u, Wij is the similarity between items, and r−i is the average value of item score.The cooccurrence matrix of keywords isc=AT•A=cijm×m. When i=j, cij is the total frequency of i keyword and j keyword. According to the cooccurrence matrix, the cooccurrence relative intensity matrix between keywords is calculated
(9)B=bijm×m=diag1c11,1c22,⋯,1cmmAT,wherebij=cij/ciicjj,i,j=1,2,⋯,m, when i≠j, and bij is the cooccurrence intensity of the i keyword and the j keyword.In this paper,IBCF recommended based on RQ matrix is called CLSVSM_IBCF algorithm.
(10)RQij=Rij,Rij≠0,∑k∈T,T∈UBjk×Rik∑k∈T,T∈UBjk,Rij=0,0,Bjk=0,Rij=0,whereU is the film collection, T is the film collection seen by user i, k is one of the films seen by user i, and Bjk is the cooccurrence intensity between film j and film k.


## 3.1. Data Source Module

Data sourceD is the basis source for recommendation by the recommendation system, mainly including user information set u, item information set I, user preference score information r for items, and user label weight score s for items. The main task of the data source module is to obtain and preprocess the data source D.


## 3.2. Recommendation Engine Module

Recommendation engineE is the core of recommendation system. Its main function is to use recommendation algorithm to process and analyze the information from data source D and recommend the most needed item set I (U) for users according to certain recommendation standards.


## 3.3. Recommended Result Processing Module

After the recommendation engine calculates the initial list of items recommended to users, we should filter and rank the recommended items and add the corresponding tag weight to explain the reasons for recommending these items to users.


## 3.4. User Feedback Module

After seeing the recommended items and recommendation explanations provided by the recommendation system, users can give corresponding user information feedback. The user feedback module collects and processes the feedback information and transmits it to the recommendation engine module to complete a new round of recommendation.The architecture of TV movie scoring system based on artificial intelligence is shown in Figure1.Figure 1
Architecture diagram of movie scoring system of artificial intelligence.Artificial intelligence is the product of human worshipping the industrial revolution, trying to break through its own limitations and enjoying scientific and technological achievements. The reason why human beings create artificial intelligence machines is based on real needs. At first, the artificial intelligence in reality was created to meet the real needs of human beings, and it mainly existed as a tool of labor service. For example, disabled people can take care of themselves, scientific and technological calculations can be faster and more accurate, and various industries can generate income and save energy. In short, artificial intelligence exists to make human production and life more convenient. At present, there is no effective online computing method to solve the complex interest recommendation problem that users’ interests suddenly change or many people share an account. However, similar to solving the cold start problem, we allow users to accurately express their current interest preferences through the tag weight scoring mechanism. The system takes the newly acquired tag weight scoring data as the user’s temporary tag weight feature and calls online computing recommendation algorithm to recommend items. We should add restrictions to this situation, remove this kind of pseudoproposition structure in the process of model search, and avoid judging this kind of model as the optimal model structure in the process of search. Therefore, we combine the domain knowledgeW with the grouping information in the model, organize it into several rules, and add the set of dependency restriction rules to the search space constraints of heuristic model learning, so as to optimize the structural space of the candidate model and improve the efficiency of the algorithm. After obtaining the user’s basic information and preference data, the data source module first preprocesses, then calls the corresponding recommendation algorithm to calculate, generates the item recommendation for each user, and stores the result. Aiming at the prediction of user’s behavior preference by the model, according to the model’s strengths, a model reasoning algorithm based on mission tree propagation is proposed, and the user’s score is calculated by using the principle of diminishing marginal utility, and finally, the prediction of user’s preference is completed. Experiments show that in the proposed model framework, the corresponding construction method and reasoning method are efficient and feasible. Finally, when users use the recommendation system, the recommended items are displayed through the recommendation result processing module, and the recommendation reasons are explained in the form of tag weight score. The model of the algorithm is shown in Figure 2.Figure 2
Artificial intelligence algorithm model.In order to predict the user’s preference for movies according to the user behavior preference model based on movie scoring data, first, the user’s score about movies needs to be obtained through reasoning according to the user and movie feature attributes. In this paper, the value with the maximum posterior probability of evaluation differentiation is taken as the reasoning result of scoring, that is, given user attributes and movie attributes. The recommendation algorithm based on item score is also called item-based artificial intelligence algorithm. The core idea of the algorithm is to recommend items similar to the items they once liked. The algorithm is divided into two steps: one is to calculate the similarity between items; the second is to generate a recommendation list for users through the similarity of items and the user item scoring matrix. The idea of similarity measurement between two items is to calculate the “distance” between samples. The similarity between projecti and project j is generally recorded as simi,j. The smaller the value of similarity, the higher the similarity between items.In the user-item scoring matrix, let useri and user j score items i⟶ and j⟶, respectively, in n dimension space, and then, the similarity simi,j between item i and item j is
(1)simi,j=cosi⟶,j⟶=i⟶•j⟶i⟶∗j⟶.Pearson correlation coefficient similarity: let Ui,j be the set of users who have scored project i and project j together, and then, the formula simi,j for calculating the similarity between project i and project j is expressed as follows
(2)simi,j=∑c∈UijRc,i−R−Rc,j−R−∑c∈UijRc,i−Ri−2,where Rc,i represents the rating of user c on item i, and Ri− and Rj− represent the average rating of item i and item jLetIij be the set of all users who have scored i and j together, and Ii and Ij represent all users who have scored i and j, respectively, and then, the formula simi,j for calculating the similarity between item i and item j is as follows
(3)simi,j=∑c∈IijRc,i−Rc−Rc,j−Rc−∑c∈IiRc,i−Rc−2∑c∈IjRc,j−Rc−2,whereRc,i represents the scoring data of the user c on the project i, Rc,j represents the scoring data of the user on the project j, and Rc− represents the average score of the user on the project.After calculating the similarity between items, the formula is usually used to calculate the user’su preference for item j(4)ρuj=∑i∈Nu∩Sj,Kωjirui,whereNu represents the collection of items that users like, Sj,K is the collection of the first K projects most similar to project j, ωij represents the similarity between project i and project j, and rui indicates the user’s u preference for the item i.Average prediction algorithm is to use the average score to predict the user’s score of the project, and it can be divided into the following two algorithms.Global average: it is defined as the average score of all score records in the training set.(5)r−ui=∑u,i∈Trainrui∑u,i∈Train1,where U represents the user, i represents the project, rui represents the score of user u on the project i, and Train represents the training set.verage value of user classification to item classification: Based on the global average value, this algorithm defines two classification functions, one is user classification functionφ and the other is item classification function ϕ. φu defines the class to which user u belongs, and ϕi defines the class to which project i belongs. The idea of the algorithm is to predict the user’s score on the item by using the average of the user’s scores on the item of the same set in the training set
(6)r∧ui=∑υ,j∈Train,φu=φυrij∑υ,j∈Train,φυ=φυ1.The idea of user-based algorithm is that to predict a user’s rating on a project, it is necessary to add users with similar interests to the user’s rating on the project.(7)rui=r−u+∑V∈Su,k∩NiWuυrυi−r−υ∑V∈Su,k∩NiWuυ,whereu,k is the set of k users who are the most similar to user’s u interests, Ni is the set of users who overestimate the project, Ni is the set of users who overestimate the project, rυi is the score of user υ on project i, and r−υ is the average of all the scores of user υ who overestimate him.The idea of the item-based algorithm is that when predicting the score of useru on item i, it is necessary to add the score of user u on other items similar to item i(8)r∧ui=r−i+∑j∈Si,k∩NuWijruj−rj−∑j∈Si,k∩NuWij,whereSi,k is the set of all items most similar to i, Nu is the set of all items over rated by u, Wij is the similarity between items, and r−i is the average value of item score.The cooccurrence matrix of keywords isc=AT•A=cijm×m. When i=j, cij is the total frequency of i keyword and j keyword. According to the cooccurrence matrix, the cooccurrence relative intensity matrix between keywords is calculated
(9)B=bijm×m=diag1c11,1c22,⋯,1cmmAT,wherebij=cij/ciicjj,i,j=1,2,⋯,m, when i≠j, and bij is the cooccurrence intensity of the i keyword and the j keyword.In this paper,IBCF recommended based on RQ matrix is called CLSVSM_IBCF algorithm.
(10)RQij=Rij,Rij≠0,∑k∈T,T∈UBjk×Rik∑k∈T,T∈UBjk,Rij=0,0,Bjk=0,Rij=0,whereU is the film collection, T is the film collection seen by user i, k is one of the films seen by user i, and Bjk is the cooccurrence intensity between film j and film k.


## 4. Implementation of Film Scoring and Communication Efficiency System

### 4.1. Film Scoring and Communication Efficiency of Artificial Intelligence

The rapid development of China’s film industry in the past decade has made the film announcement more and more exquisite, but it is difficult to judge the quality of a film only by a short promo. With the increase of the number of movies released every year, “which movie to choose” has become a headache for consumers. In the face of wonderful promotional videos, consumers can only passively choose “buy first and review later,” and the interests of consumers cannot be guaranteed. The appearance of movie ratings gives consumers a relatively reliable choice of “watching reviews and buying again.” The prediction of user’s behavior preference based on artificial intelligence system is actually the prediction of user’s rating about a certain movie, which is determined by the causal relationship between preference and rating. According to the analysis of the user’s behavior preference by the artificial intelligence system, it can be seen that the user’s behavior preference is influenced by various characteristic attributes of the user. The user’s behavior preference and the characteristic attributes of the movie together determine the user’s preference for this movie. Therefore, the prediction of the user’s preference for the movie is essentially based on the established model. The spread of movies has broken through the limitations of countries and regions, and people everywhere can click, watch, spread, and evaluate. It can be said that on the Internet, if there is no language barrier, there is no obvious boundary between national boundaries and regions. In China, we can click on the webpage on the other side of the ocean, watch online movies made in the United States or South Korea, post its links on our own webpage, or pass on the dialogue to friends in Africa in the instant dialog box, and leave our own comments on the webpage at the same time. This freedom to break through the national geographical restrictions makes online movies more widely spread. The traditional communication in artificial intelligence system is relatively concentrated and complete, and the communication path of information in the era of media integration has been broken. With the continuous development of new media, the emergence of Internet, intelligent mobile terminals, and various apps has provided more diverse ways for information communication.With the fragmented dissemination of information, the fragmented consumption mode also comes into being, and the dissemination mode of movies is also fragmented. Today, we can watch movies anytime and anywhere through mobile phones. “Movie fragmentation” makes the dissemination of movies everywhere all the time. Movie reviews are also anonymous. This anonymity enables netizens to express what they are willing to express under the artificial intelligence system when publishing their own comments on some online movies. They do not have to worry about being considered flattering or incompetent because they express the same views as others, or because they express different views from others, as in daily life, and be hostile to people who hold different views from themselves. In this way, the commentators of online films can get the complete catharsis of their emotions and the release of themselves, which also promotes the increasing popularity of online films and the continuous dissemination and comment of online films. Film has always been the representative of high-quality communication content. No matter what new media appear, it is necessary to rely on the original content of the film and produce and play the online version of the film through different new media platforms. The total box office of Chinese films in 2017 was 559 1.1 billion yuan, and the overall scale of China’s film industry can be as high as trillion yuan. Under the artificial intelligence system, high-quality film content will be the object of many multimedia, which is bound to dominate the film communication. At present, the number of films is huge, the communication frequency is high, and the communication speed is fast, which is inevitably filled with some vulgar and profiteering films. The state’s supervision on this aspect is not enough, which has produced some negative social effects. Therefore, major video websites should take the initiative to assume the regulatory responsibility, avoid the launch of microfilms that convey bad values, and promote the healthy and orderly development of the film industry. No matter what kind of film, it always tells a story and tells a kind of idea and emotion and then completes its communication behavior and causes its communication effect with the help of media planted screen. Classical communication in artificial intelligence system also needs feedback mechanism. Films without feedback mechanism are equivalent to model operas in China during the cultural revolution. Without targeted screening schedule, it is difficult to cause huge box office revenue. Without effective planning mechanism and promotion means, it is difficult to achieve effective communication effect. Finally, the model proposed in this paper uses the probability dependence relationship between feature attributes to represent user behavior preferences, which can find the group information contained in the data, but it cannot provide reliable suggestions for more personalized recommendation services. We can improve the model by introducing more personalized tags. These are the work we need to continue in the future.


### 4.2. Experimental Results and Analysis

In this paper, the fantastic TV movies listed in IMDB rating ranking list are taken as samples, which are sampled according to the rated stars, and 25% movies of each star are selected from 1 star to 5 stars in turn. As of 14: 56 on March 12, 2022, there are 143 fantasy movies listed on this list, among which 105 movies such as Firefly, Death likes me, Star Wars V: Empire Strikes Back, and The Lord of the Rings are selected as specific analysis objects. As shown in Table1.Table 1
Sampling table.
StarNumber of slicesSampling number1 star412 star523 star1434 star3775 star8318Amount to14331This study designed the two dimensions of “background analysis” and “theme analysis” of fantasy TV movies and divided several items under these two dimensions. The background analysis is divided into four categories: year, star, region, and type, and the theme analysis is divided into two categories: theme type and theme spirit. As shown in Table2.Table 2
Distribution of release years of fantasy TV movies.
YearsNumber of slicesYearsNumber of slicesYearsNumber of slicesYearsNumber of slices2011120142201732020620122201532018420211020131201642019520229SummaryBefore 20144Before 20179Before 2020122020-202225As can be seen from Table2, from the chronological distribution table, we can see that the number of films of this type is increasing year by year. It has flourished since 2017 and has a growing development trend.The design of this experiment is as follows: randomly selectn, n=1000 users, and first conduct the online calculation recommendation experiment. The difference is to recommend L, l=10 films for users each time, and provide the recommendation explanation in the form of label weight score, that is, each film is equipped with L, l=10 labels, and each label will have corresponding weight to indicate the relevance of the film and the label. Then, the user can feedback the recommended movies through the tag weight score. The system receives the feedback and calls the feedback calculation recommendation algorithm for the next round of recommendation. In each round of experiment, we take the score information of N users in the data set as the test set, and the list of movies with user u score of more than 3 in the test set is d (U), and the item recommendation obtained after calling the online feedback mechanism is I (U). The calculation method of recommended accuracy is the same as that of offline calculation and recommendation experiment. Every time we call, we will calculate the average absolute error MAE and the accuracy of recommendation as indicators to measure the performance of user feedback. The experiments were compared three times, and the experimental results are shown in Figures 3–5.Figure 3
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.Figure 4
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.Figure 5
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.From Figures3–5, from the first round to the fourth round, we adjusted the recommendation through user feedback, which improved the average absolute error by 18% and the accuracy by 24%. Moreover, through four rounds of feedback, the accuracy of online computing recommendation for new users has been very close to that for old users, which once again reflects the superiority of this algorithm in solving the problems of cold start and complex interest recommendation.In this experiment, the average accuracy ofN users is compared with offline recommendation, radom recommendation, and popular recommendation. Among them, radom recommendation refers to randomly recommending L for each user. In this paper, L=10 movies; popular recommendation refers to recommending L with the highest overall rating to users every time. In this paper, L=10movies. This experiment was compared twice, and the experimental results are shown in Figures 6 and 7.Figure 6
Comparison of accuracy of online recommendation algorithm.Figure 7
Comparison of accuracy of online recommendation algorithm.From Figures6 and 7, it can be found that from multiple perspectives, random recommendation algorithm and popular recommendation algorithm have a certain gap in performance with artificial intelligence algorithm. This is because the input data does not use the preference scoring information of the recommended user but only the user’s signature weight feature. The accuracy of artificial intelligence algorithm is much higher than random recommendation algorithm and popular recommendation algorithm, so it is very helpful to solve the cold start and complex interest recommendation problems.


## 4.1. Film Scoring and Communication Efficiency of Artificial Intelligence

The rapid development of China’s film industry in the past decade has made the film announcement more and more exquisite, but it is difficult to judge the quality of a film only by a short promo. With the increase of the number of movies released every year, “which movie to choose” has become a headache for consumers. In the face of wonderful promotional videos, consumers can only passively choose “buy first and review later,” and the interests of consumers cannot be guaranteed. The appearance of movie ratings gives consumers a relatively reliable choice of “watching reviews and buying again.” The prediction of user’s behavior preference based on artificial intelligence system is actually the prediction of user’s rating about a certain movie, which is determined by the causal relationship between preference and rating. According to the analysis of the user’s behavior preference by the artificial intelligence system, it can be seen that the user’s behavior preference is influenced by various characteristic attributes of the user. The user’s behavior preference and the characteristic attributes of the movie together determine the user’s preference for this movie. Therefore, the prediction of the user’s preference for the movie is essentially based on the established model. The spread of movies has broken through the limitations of countries and regions, and people everywhere can click, watch, spread, and evaluate. It can be said that on the Internet, if there is no language barrier, there is no obvious boundary between national boundaries and regions. In China, we can click on the webpage on the other side of the ocean, watch online movies made in the United States or South Korea, post its links on our own webpage, or pass on the dialogue to friends in Africa in the instant dialog box, and leave our own comments on the webpage at the same time. This freedom to break through the national geographical restrictions makes online movies more widely spread. The traditional communication in artificial intelligence system is relatively concentrated and complete, and the communication path of information in the era of media integration has been broken. With the continuous development of new media, the emergence of Internet, intelligent mobile terminals, and various apps has provided more diverse ways for information communication.With the fragmented dissemination of information, the fragmented consumption mode also comes into being, and the dissemination mode of movies is also fragmented. Today, we can watch movies anytime and anywhere through mobile phones. “Movie fragmentation” makes the dissemination of movies everywhere all the time. Movie reviews are also anonymous. This anonymity enables netizens to express what they are willing to express under the artificial intelligence system when publishing their own comments on some online movies. They do not have to worry about being considered flattering or incompetent because they express the same views as others, or because they express different views from others, as in daily life, and be hostile to people who hold different views from themselves. In this way, the commentators of online films can get the complete catharsis of their emotions and the release of themselves, which also promotes the increasing popularity of online films and the continuous dissemination and comment of online films. Film has always been the representative of high-quality communication content. No matter what new media appear, it is necessary to rely on the original content of the film and produce and play the online version of the film through different new media platforms. The total box office of Chinese films in 2017 was 559 1.1 billion yuan, and the overall scale of China’s film industry can be as high as trillion yuan. Under the artificial intelligence system, high-quality film content will be the object of many multimedia, which is bound to dominate the film communication. At present, the number of films is huge, the communication frequency is high, and the communication speed is fast, which is inevitably filled with some vulgar and profiteering films. The state’s supervision on this aspect is not enough, which has produced some negative social effects. Therefore, major video websites should take the initiative to assume the regulatory responsibility, avoid the launch of microfilms that convey bad values, and promote the healthy and orderly development of the film industry. No matter what kind of film, it always tells a story and tells a kind of idea and emotion and then completes its communication behavior and causes its communication effect with the help of media planted screen. Classical communication in artificial intelligence system also needs feedback mechanism. Films without feedback mechanism are equivalent to model operas in China during the cultural revolution. Without targeted screening schedule, it is difficult to cause huge box office revenue. Without effective planning mechanism and promotion means, it is difficult to achieve effective communication effect. Finally, the model proposed in this paper uses the probability dependence relationship between feature attributes to represent user behavior preferences, which can find the group information contained in the data, but it cannot provide reliable suggestions for more personalized recommendation services. We can improve the model by introducing more personalized tags. These are the work we need to continue in the future.


## 4.2. Experimental Results and Analysis

In this paper, the fantastic TV movies listed in IMDB rating ranking list are taken as samples, which are sampled according to the rated stars, and 25% movies of each star are selected from 1 star to 5 stars in turn. As of 14: 56 on March 12, 2022, there are 143 fantasy movies listed on this list, among which 105 movies such as Firefly, Death likes me, Star Wars V: Empire Strikes Back, and The Lord of the Rings are selected as specific analysis objects. As shown in Table1.Table 1
Sampling table.
StarNumber of slicesSampling number1 star412 star523 star1434 star3775 star8318Amount to14331This study designed the two dimensions of “background analysis” and “theme analysis” of fantasy TV movies and divided several items under these two dimensions. The background analysis is divided into four categories: year, star, region, and type, and the theme analysis is divided into two categories: theme type and theme spirit. As shown in Table2.Table 2
Distribution of release years of fantasy TV movies.
YearsNumber of slicesYearsNumber of slicesYearsNumber of slicesYearsNumber of slices2011120142201732020620122201532018420211020131201642019520229SummaryBefore 20144Before 20179Before 2020122020-202225As can be seen from Table2, from the chronological distribution table, we can see that the number of films of this type is increasing year by year. It has flourished since 2017 and has a growing development trend.The design of this experiment is as follows: randomly selectn, n=1000 users, and first conduct the online calculation recommendation experiment. The difference is to recommend L, l=10 films for users each time, and provide the recommendation explanation in the form of label weight score, that is, each film is equipped with L, l=10 labels, and each label will have corresponding weight to indicate the relevance of the film and the label. Then, the user can feedback the recommended movies through the tag weight score. The system receives the feedback and calls the feedback calculation recommendation algorithm for the next round of recommendation. In each round of experiment, we take the score information of N users in the data set as the test set, and the list of movies with user u score of more than 3 in the test set is d (U), and the item recommendation obtained after calling the online feedback mechanism is I (U). The calculation method of recommended accuracy is the same as that of offline calculation and recommendation experiment. Every time we call, we will calculate the average absolute error MAE and the accuracy of recommendation as indicators to measure the performance of user feedback. The experiments were compared three times, and the experimental results are shown in Figures 3–5.Figure 3
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.Figure 4
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.Figure 5
Performance comparison of artificial intelligence algorithm in multiple rounds of feedback.From Figures3–5, from the first round to the fourth round, we adjusted the recommendation through user feedback, which improved the average absolute error by 18% and the accuracy by 24%. Moreover, through four rounds of feedback, the accuracy of online computing recommendation for new users has been very close to that for old users, which once again reflects the superiority of this algorithm in solving the problems of cold start and complex interest recommendation.In this experiment, the average accuracy ofN users is compared with offline recommendation, radom recommendation, and popular recommendation. Among them, radom recommendation refers to randomly recommending L for each user. In this paper, L=10 movies; popular recommendation refers to recommending L with the highest overall rating to users every time. In this paper, L=10movies. This experiment was compared twice, and the experimental results are shown in Figures 6 and 7.Figure 6
Comparison of accuracy of online recommendation algorithm.Figure 7
Comparison of accuracy of online recommendation algorithm.From Figures6 and 7, it can be found that from multiple perspectives, random recommendation algorithm and popular recommendation algorithm have a certain gap in performance with artificial intelligence algorithm. This is because the input data does not use the preference scoring information of the recommended user but only the user’s signature weight feature. The accuracy of artificial intelligence algorithm is much higher than random recommendation algorithm and popular recommendation algorithm, so it is very helpful to solve the cold start and complex interest recommendation problems.


## 5. Conclusions

This paper analyzes the advantages of three recommendation methods under the artificial intelligence system model in solving this problem. In order to realize this recommendation method, we have conducted in-depth research on relevant algorithms. Finally, experiments have verified the effectiveness of this technology. In terms of the film communication mode discussed in this paper, the feedback mechanism in film communication is constantly strengthened, including film award consciousness, film planning consciousness, and film marketing consciousness. Film packaging awareness is constantly entering the psychology of film makers. Once the feedback is strengthened, the communication effect of films can be organically linked. Aiming at the application scenario of film scoring data analysis, the implicit variable model is used as the basic representation framework. By analyzing the probability dependence between the attributes in the film scoring data, a user behavior preference model based on the film scoring data is proposed. According to the model definition and related characteristics, by introducing domain knowledge to restrict the model learning process, a heuristic model learning algorithm based on domain knowledge artificial intelligence is proposed, which can learn the implicit variable model efficiently and quickly and converge to the optimal model solution. By adjusting the recommendation through user feedback, the average absolute error has been improved by 18% and the accuracy has been improved by 24%. Moreover, through four rounds of feedback, the accuracy of online computing recommendation for new users has been very close to that for old users, which once again reflects the superiority of artificial intelligence algorithm in solving the problems of cold start and complex interest recommendation. In the future, it is planned to further optimize the efficiency and effectiveness of the artificial intelligence system based on tag weight to adapt it to large-scale data. Another future research work is how to improve the collection quality of offline training data and further improve the accuracy of the model. For more personalized recommendation services, we cannot provide reliable suggestions. We can improve the model by introducing more personalized tags, These are the work we need to continue in the future.



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*Source: 5289754-2022-05-14.xml*