Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

With the continuous development of network information technology, people’s dependence on network information is becoming stronger and stronger. The information on the Internet shows a trend of explosion, and information overload has also become a research hotspot. Due to the defects of cold start and sparse data, the traditional personalized recommendation algorithm will show the problem of accuracy degradation in the face of excessive information. Therefore, the traditional methods have been unable to adapt to the current needs of literature and art analysis. The goal of speech enhancement is to remove noise interference from noisy sounds and extract pure sounds as much as possible. Speech enhancement can reduce sound distortion, improve sound quality, and reduce hearing fatigue. At present, voice enhancement technology is widely used in products and fields such as mobile communications, computers, smart phone devices, and smart homes. First, this article will briefly introduce the artistic analysis of film and television works. Starting from the main characteristics of film and television works, according to the characteristics of various data lists based on visualization and visual data mining. Through visual data mining, the experimental data set used in this article is constructed based on various data types such as the main narrative element data set and the character action data set.


Introduction
This article will study the personalized hybrid recommendation algorithm and model based on collaborative filtering. First, a personalized recommendation method based on user context awareness is proposed. The algorithm mines the information in the social information network and builds on the basis of user and item information. In the above, the contextual information of the two is further integrated, and the potential connection between the above three is discovered, which effectively improves the recommendation accuracy, recommendation recall rate and average accuracy rate of the system. Subsequently, the cold start problem of recommendation based on collaborative filtering recommendation was studied, and a matrix decomposition recommendation algorithm based on auxiliary information of users and items was proposed (Lin et al. 2019). Finally, a hybrid recommendation model based on ranking learning is proposed, which effectively solves the problem of weight distribution of the results of different recommendation algorithms (Huang et al. 2019). Speech enhancement technology also has important applications in cochlear implants and hearing aids (Zhang et al. 2022). As a preprocessing module of hearing aids, it greatly reduces the noise interference of hearing aids to obtain a higher target speech signal-to-noise ratio (Plomp 1986). For the hearing impaired, before further processing to restore the spatial hearing of the sound or effectively transmit the speech signal to the brain for processing, they usually need a higher input signal-to-noise ratio to correctly recognize the speech or hear the speech clearly, so in the cochlear implant It is of great significance to apply speech enhancement technology to improve the signal-to-noise ratio of the collected speech signal in the field of hearing aids such as hearing aids (Upadhyay and Pachori 2017).
Voice enhancement technology occupies an important position in the speech signal processing knowledge system (Amehraye et al. 2009). In related application fields, namely, voice communication, human-computer interaction, human-IoT technology product interaction, hearing aids and other fields, voice enhancement technology plays an important role. It plays a pivotal and irreplaceable role (Bando et al. 2017). Based on this, many universities, scientific research institutions, Internet and electronic communication companies at home and abroad have carried out many related researches on speech enhancement technology (Wolfe and Godsill 2003). So far, many speech enhancement algorithms for application in various scenarios have been proposed, and many embedded algorithms have also been produced (Adán et al. 2022). Therefore, the research on speech enhancement algorithms has very important practical significance. ''Shadow'' means movie, and ''vision'' means TV. Film and television literature is a combination of film literature and television literature (Wang and Mandal 2019). As a promising literary style, the history of film and television literature is far less than that of traditional novels, poems, dramas, and prose (Yang 2020). However, in today's social life, film and television have a huge influence, and they have become an important field of modern literature, if you abandon the literature of film and television, the history of modern literature will become incomplete.

Related work
The literature proposes to implement a speech enhancement system to overcome the adverse effects of noise. In future, not only humans and computers, but even different computers can communicate smoothly (Mohammed et al. 2022). The literature points out that the advantages of the Internet of Things, big data, cloud computing and other technologies are obvious. In recent years, the mobile Internet industry has shown a trend of prosperity (Kenney and Pon 2011). According to the literature, the so-called voice emphasis refers to the use of a specific method to remove ambient noise mixed with the pure sound of the actual scene, and to improve the signal-to-noise ratio, clarity and comfort of the sound quality (Kulikov 2022). The specific method used in this process is called speech enhancement technology. A coordinated filtering algorithm based on project evaluation and prediction is proposed in the literature. The algorithm uses the item-based coordinated filtering method to fill in the blank values of the user evaluation set, and then uses the fill score set to further calculate the similarity between users to generate recommendations (Zhong 2021). The literature proposes that collaborative filtering is to dig out a small part of users who are similar to the recommender in some aspects from a large number of user groups. In collaborative filtering, these users will be defined as a user neighborhood set, based on other items they like (Duan et al. 2019). The literature proposes that MapReduce is a programmable, parallel distributed computing framework that can be used for data processing, which can be customized based on user-written applications (Dean and Ghemawat 2008).
3 Collaborative filtering recommendation and voice enhancement model design in literature, film, and art analysis

Collaborative filtering recommendation
With the further development of society and information technology, people are gradually entering the era of information explosion. On the consumer side, how to find valuable information from massive amounts of data and information concisely and efficiently; on the server side, how to accurately deliver information to specific service targets has become an urgent problem to be solved. Personalized recommendation system, as an efficient means to deal with information overload, is one of the important ways to solve the above problems. Personalized recommendation system is a tool that can quickly help users find items of interest. They require users to actively input accurate keywords for information retrieval, which is a passive information output method; personalized recommendation systems only need Analyze and process userspecific historical behavior data, supplemented by related recommendation algorithms, output a personalized recommendation list, and actively provide users with items that may have potential preferences. On the one hand, it helps users find the target information they are interested in in the massive and heterogeneous information, and on the other hand, how to push the information that users are interested in efficiently and accurately. As shown in Fig. 1. As shown in Table 1, the personalized recommendation system analyzes and processes a large amount of user historical behavior information to provide users in different scenarios with corresponding personalized services.
Based on the user's coordinated filtering recommendation algorithm, the recommendation idea of this recommendation algorithm can be summarized as follows: Recommend other users who have the same interest as the user, and recommend favorite items to the user. The steps of the algorithm can be summarized as the following two steps: (1) Analyze and process the historical action data of the initial user, and find the user group similar to the target user's interests and preferences, that is, the user's neighbor group.
(2) Analyze the user neighborhood set, find the user's preferred items in the set, and recommend the items that the target user has not explored to the target user. Formula 1 can be used to simply calculate the similarity of preferences between user u and user v.
Or calculated by formula 2 In order to further reduce the computational complexity, an inverted list of users and items can be established to introduce a penalty factor, as shown in formula 3: On the basis of calculating the user similarity, the Userbased recommendation algorithm can be calculated by formula 4 to recommend the k users' favorite items that are closest to their interests and preferences.
The item-based coordinated filtering recommendation algorithm can summarize the idea of using the recommendation algorithm: continue to recommend items similar to the user's previous settings. The steps of the algorithm can be summarized as the following two steps: (1) Analyze and process the initial user historical behavior data, calculate the similarity between items, and find adjacent sets of items.
(2) Analysis of the item's neighboring group, find items with higher preference similarity in the set, and recommend them to users. The similarity of the above items can be obtained by formula 5: But formula 5 does not consider the impact of popular items on the similarity of items, that is, when an item is very popular, the value of simi,j may be close to 1. Therefore, formula 6 adds a penalty factor for popular commodities: In order to further improve the accuracy of the recommended results and increase the scope and diversity of the recommended results, as shown in Eq. 7, the similarity of items is normalized according to the maximum value of the matrix.
Using formula 8, it is possible to calculate the K items recommended by the user-based recommendation algorithm and similar items that the previous user prefers to use. Film and television art innovation in network environment by using collaborative filtering… 7581 Before the model-based hybrid filtering recommendation algorithm, the difference between the two hybrid filtering recommendation algorithms is introduced. Machine learning algorithms are often used to predict user scores for specific items. The first algorithm is based on historical data. The data set is divided into a training set and two parts of a test set. The training set used for training is used to generate a recommendation model. Secondly, the recommended model is suitable for the test group to evaluate the advantages of the model. Table 2 compares the advantages and disadvantages of the model-based coordinated filtering recommendation algorithm and the nearest neighbor-based coordinated filtering recommendation algorithm.
As a typical representative of model-based recommendation, the model uses latent variable analysis technology to automatically cluster user behavior data, mine the hidden features of user preference data, and provide users with personalized recommendations. LFM calculates user u's preference for item i by formula 9: The loss function of the above model is Formula 10, and the optimal p u,k and q i,k model parameter values are calculated through the training set and optimization theory.
Here, a stochastic gradient descent algorithm can be used to optimize the cost(p,q) loss function mentioned above, so that the predicted scoring matrix error is minimized. First, take the partial derivative of the loss function cost(p,q) for the two parameters u, kp and i, kq of the model, and then step the parameters in the direction of the steepest descent gradient to obtain the iterative formulas of formulas 14 and 15, the parameter a is The learning rate (LearningRate) needs to be determined in multiple offline experiments. The specific calculation steps of the above process are as follows: In the current era of big data, the single database storage technology of the traditional personalized basic system itself can no longer be applied to the calculation of big data sets. In order to satisfy the storage of various data files in big data sets, it is necessary to provide an integrated interface externally and use a storage architecture of various mixed-mode storage internally. At the same time, the previous personalized basic system used the calculation method of a single machine node in the recommendation algorithm. This cannot meet the computing requirements of large data sets generated by large users under large data sets. YARN (decentralized resource management), HDFS (decentralized file system), and MapReduce (decentralized computing framework) are the centers of the above architecture. Figure 2 shows the main ecosystem of the Hadoop architecture.
Generally, one or several evaluation indicators shown in Table 3 are used in the industry to measure the performance of the recommendation algorithm. The specific ways to obtain the above indicators are shown in the table below, where O means OPTIONAL, N means NO, and Y means YES. The main measurement indicators will be briefly introduced below.
Prediction accuracy is an important indicator for measuring and predicting user actions through personalized recommendation systems and recommendation algorithms. Neighborhood-based recommendation algorithm The principle of the algorithm is simple, easy to apply, and can provide an explanation of recommended items The scalability is not strong, the recommendation accuracy is not high, and there are problems such as cold start and data update Model-based recommendation algorithm It has the advantages of fast copy speed, good theoretical foundation and high copy precision Model building, training takes a long time, and model parameter adjustments are more complicated The index is based on various investigation directions of offline experiments, as shown in formulas 16 and 17. The prediction accuracy of the Top-N list is generally measured according to the coincidence rate and the reproduction rate, as shown in Eqs. 18 and 19.
Here, T represents the test set, and the predictive evaluation of the user U of the item I is represented by the recommendation algorithm.
In real life, users' interests are very broad. For example, in a music website, users can like to listen to classical music or popular music. Therefore, the result of recommendation needs to include multiple areas of interest, that is, recommendation needs diversity, and its definition is shown in Eq. 19.
Among them, sim(i,j) [ [0,1] represents the similarity between item i and item j, and R(u) represents the recommendation list of user u.

Speech enhancement
The SDnCNN model uses an ideal residual spectrogram, and the cost function of the neural network is shown in the following formula: Among them, Y i ; X i ð Þ ½ N i¼1 represents a pair of noisy spectrogram blocks and corresponding pure noisy spectrogram blocks. Figure 3 shows the SDnCNN model framework proposed in this paper and the post-processing schematic for obtaining the target.
Film and television art innovation in network environment by using collaborative filtering… 7583 In general, the characteristics of the SDnCNN model in this article are based on the feature extraction method of the image, using the spectrogram as the training set, and applying the denoising convolutional neural network with outstanding performance for the denoising processing of the spectrogram, avoiding traditional speech. Recurrent neural networks commonly used in data have limited development depth and excessive complexity. It is easier to obtain a large amount of training data by relying on the spectrogram clipping strategy, and the space storage cost is much lower. Figures 4 and 5, respectively, show the experimental test comparison results of SegSNRI and LSD scores of the six algorithms involved in the comparison. The BSF algorithm in the figure is an algorithm that uses the bilateral filtering technology of image processing to process the spectrogram. It belongs to the comparison work of the simulation test in this article. Like the guiding spectrogram filtering algorithm proposed in this article, it uses image denoising technology to process the spectrogram. However, He et al. verified that the processing effect and algorithm running speed of the guided filtering technology are better than bilateral filtering.
From the results in Fig. 4, it can be seen that under four different noise environments, the SegSNRI score of the enhanced speech processed by the AM-GSF algorithm in this paper is the highest, which is better than the traditional single-channel speech enhancement algorithm, and the amount of noise suppression and The overall level of voice quality improvement is relatively good, and for stationary white Gaussian noise or Babble noise, the leading margin of the AM-GSF algorithm is relatively large, which also shows that for the non-stationary noise environment The AM-GSF algorithm has better suppression performance.
Observing the score comparison in Fig. 5, it can be found that the LSD score curve of the AM-GSF algorithm in this article is basically the smallest, and the PSNRE-USPP algorithm has also achieved good results when dealing with Gaussian white noise and Babble noise. Based on the four noise environments in Fig. 5, the LSD score of the guided spectrogram filtering algorithm based on the auditory masking effect is the best, indicating that the enhanced speech distortion measure of the algorithm in this paper is at the smallest level and the overall quality is better.  innovations in the form of works and communication methods. In order to promote their works more quickly, many creators change the form of their works from the beginning of production. They are produced through digital networks, and the delivery method has changed from the previous printing mode to the digital communication mode. The network environment mentioned in this article has gradually formed with the development of information network technology. Information network environment is also an abstract space category that exists objectively after the emergence of network systems. Regarding the definition of the network environment, the Supreme People's Court issued a relevant judicial interpretation in 2012, namely, the provisions on the application of certain laws in the trial of civil disputes involving infringement of the right to freedom of speech. Article 2 of the judicial interpretation broadly defines the network environment as an information network environment, with computers, televisions, fixed phones, mobile phones and other electronic devices as terminals, computer Internet, radio and television networks, fixed and mobile communication networks, and other information The environment created by the network.
The boundary of the network environment is different from the previous communication environment and has its own unique characteristics. First, the network environment has the characteristics of sharing. In the past non-network environment, the formation and popularization of works mainly relied on the development of printing technology and digital technology. The reproduction and popularization of works were restricted by previous technical means, and the transmission channel was relatively single. However, in the network environment, the work is easy to get started. With the innovation of network digital technology, works can be copied in a few minutes to a few seconds. This kind of copy can be extended to an unlimited network range, which is faster and more convenient. Second, the network environment is spatial. In the method of network communication, distance will not become an obstacle to communication. In the network environment, as long as it is a person who uses the information network, anyone can find the work they need from anywhere in the world through the network. Regardless of the rights and interests of copyright owners, anyone can use and redistribute works in the most convenient way. However, in the past nonnetwork environment, transportation costs may hinder long-distance use and immediate release of works. Therefore, in the network environment, the use of works that can access the space will also cause great damage to the rights and interests of the copyright owner. Secondly, in addition to the damage to the network environment caused by the exclusive rights of copyright owners, due to the rapid and rapid distribution of information, copyright owners can also achieve the initial goal of rapid distribution and widespread distribution of copyright. With the development of the information network environment, a series of complex relationships have arisen between the rights holders and users of network information, and new contradictions and oppositions have arisen. These characteristics have a great impact on the fair use of film and television works. Therefore, the research in the network environment and the proper use of film and television works must understand the meaning of the network environment, understand the boundaries and differences of the network environment, and correctly understand the scope of the network environment. In order to correctly understand the popularization of legal issues in the use of film and television works in the network environment, better study the environment of this fair use system.

Reasonable analysis of literature, film and art
Only when the exclusive rights of the copyright owner are protected, other people's use of the work will be completely blocked. Therefore, users must obtain the consent or permission of the copyright owner under any circumstances to maximize the protection of the copyright owner's exclusive rights and interests. However, from the point of view of the balance of legal interests, this method is unfair to the users of the work. The cost of using the works has increased significantly, and the efficiency has decreased significantly. Users will give up using other people's work. This is not conducive to the popularization and inheritance of excellent works, and ultimately is not conducive to the benefit of copyright owners based on their own works.
In the network environment, the sending method of film and television works is more convenient, and users can obtain works simply and quickly through digital technology. In the network environment, through the actions of the public Internet server, the user's ''upload'' or the ''configuration'' of server actions can be supported. P2P software provides users with seeds, etc., and there is the ''interactive'' diffusion action of the computer itself. To a certain extent, download and upload services exist for the purpose of copying and distributing film and television works. In these behaviors, there may be users who use noninfringement and situational work for the purpose of fair use. For example, it is unfair for a reasonable user of a work to regard all copying or proliferation as illegal. In the modern information network environment, the balance of interests between the exclusive rights of copyright owners and, under certain circumstances, exempting users from liability for illegal acts also needs to be resolved.
The basic purpose of protecting copyright is to continuously create excellent works and protect the author's creative enthusiasm. All kinds of cultural development are the result of inheritance. Later works, learn from the excellent works of others, and combine their own wisdom to innovate. The birth of excellent works cannot be separated from the foundation built by many excellent works in the initial stage. When the copyright owner is protected by absolute rights and hinders the reasonable use or use of the work by others, the creative inspiration of the creator right owner will be hindered to a certain extent. It will be difficult for the future generation to independently produce innovative works, which is not conducive to the development and prosperity of culture. Therefore, the fair use system is considered to be a restriction on copyright owners, but in essence, it encourages more people to participate in the innovation of works and expand the influence in the cultural field. The new works can achieve common development, enjoy the common results of cultural prosperity, and jointly obtain more benefits.

Literature, film and art are facing impact
The right of reproduction is the exclusive right of the copyright owner. In the copyright law, the copyright owner can use the right as the basis of the claim to prevent others from illegally copying or infringing the copyright. This kind of copying is a conscious operation performed by an individual for the purpose of copying. In other words, the right to copy will adjust conscious copying behavior. Under the traditional copy right, the copyer clearly knows that he is engaged in copying and hopes to achieve the purpose of copying the work through copying. Regarding the past film and television works, no matter what methods are adopted, such as the use of multimedia machines in copying, writing, and other copying methods, the operators clearly realize that they are copying. The purpose is to copy movies or TV works, or personal use, or popularize to other people. Because in the traditional way of copying, the actor's incubator behavior will infringe the copyright of the copyright owner, if the law protects this copy right, the violator is legally responsible for the copy.
Currently, the copyright laws of various countries in the world do not have clear regulations on private copying, and theoretical opinions are still inconsistent. Generally speaking, copyright law scholars believe that users of personal copying are not based on commercial purposes, but for personal appreciation and use, and a small amount of copies of other people's works. Our country's ''Private Copying Copyright Law'' has no other clear regulations. Article 15 of our country's Copyright Law is only from the viewpoint of reasonable use by users for personal learning, research or educational purposes, and copying a small amount of others. It does not require permission from the copyright owner. No remuneration is required. As literally stated, private copying, which is different from private use, is essentially a special kind of copy, but it is allowed.
Secondary editing is a very popular video work in the network environment in recent years. Video material usually comes from other film and television works and is reedited in order to form new video works. The production methods are mainly video synthesis and editing. The final publication form is similar to that of film and television works, but it is different from film and television works in the legal sense. Usually, in the second editing work, some clips of film and television works will be used. The final animator is usually an individual user of Wemedia, without the permission of the copyright owner of the relevant film and television works. Therefore, the author of such a video edits a certain number of film and television works from his personal preferences.
The rights of copyright owners of film and television works mainly include the right to maintain copyright, publishing copyright, and the integrity of the work. As far as the editing format is concerned, the general video editor will add a complete label to the author of the second clip at the beginning of the video, and the clip marked by the author on the second editing video work website is not the author's real name in most cases. In order to distinguish the clips uploaded by other users, the author usually uses the user login name of the video website. Moreover, in the final video, most of the editing works listed the reference sources and names of the film and television works. Because of the source of the film and television works, it can be considered that the work is completed for the first time, and the second editing only performs the second editing on the published content.

Development strategy
As mentioned above, there is no legal meaning to adjust the temporary copying behavior, but many users of film and television obtain works for free through illegal websites. That is to say, mainly from the point of view of the website operator, there may be a possibility of temporary copying that infringes the rights of others. Secondly, regarding the legal restrictions on temporary applications, the restrictions on temporary applications by network service providers can be taken as the starting point.
Uploading, downloading, and copying film and television works are very convenient in the network environment. Many popular film and television programs are also widely popularized by website operators and computer users. It is difficult for copyright owners to manage the source of film and television works. However, if it is found that the copyright owner has infringed the rights of a part of the website or users, it is necessary to negotiate with the website operator or the infringing users. If the negotiation Film and television art innovation in network environment by using collaborative filtering… 7587 fails, he or she must bring the case to court. The process may not be smooth and may require a lot of time and energy. In addition, due to the development of the network environment, it is difficult to detect infringement and copying, and it is difficult to identify the offender. Even if they are sued, copyright owners face difficulties in obtaining evidence. Infringements always exist, and copyright owners are tired of protecting their rights. This is obviously unfair to copyright owners. If a unified rights protection management organization is established, copyright owners can apply to join the organization and use unified management methods to mitigate some illegal acts. Therefore, copyright owners do not need to spend a lot of human and physical resources to protect their rights. In addition, the cost of rights protection can be reduced. The fair utilization standards in the field of private copying are very complicated. From the perspective of the world's legislative standards, it is impossible to find a uniform standard for fair utilization. In fact, the judge must make a fairer decision based on the court's experience analysis and the facts of a particular case. In order to avoid confusion in judicial practice, many countries have developed legal preventive measures as compensation systems. Due to the different legal status, the country has different limits on the scope of compensation. The purpose of this law is to make compensation for the potential loss of potential infringement when it is difficult for the right holder to manage the potential infringement caused by private copying. Private copying of film and television works is especially popular in the network environment. During the download and upload process, users may face the infringement of private copying. If the copyright owner pursues the infringements one by one, the cost of the protection right may be higher than the compensation profit of the protection right. Therefore, the copyright owners of the film and television works are all allocated to the users to use their rights and interests reasonably, and at the same time, they demand a fixed fee from the users, thereby increasing the cost of protecting their rights. This is also the balance of interests between copyright owners and users.
The author of the second editing usually publishes the video work for free, thus gaining a large audience's popularity. The author of the second editing is completely given free and reasonable use. The original creator's interests will be at a specific risk. Therefore, the fair use defense is inevitable. Lead to an imbalance in future system. Therefore, original creators can safeguard their rights through legal channels, thereby managing secondary creators.

Conclusion
With the rapid development of science and technology, people have gradually entered the era of big data information. In the era of information explosion, to provide users with effective and personalized copy services, so that users can efficiently and accurately find valuable information, which is an urgent problem to solve the personalized maintenance system? Among them, personalized maintenance based on coordinated filtering is one of the most widely used techniques in recommendation systems. This technology uses a specific copy algorithm to mine and analyze the user's past behavior data, predict the user's potential interests and preferences, and copy the items that the user is interested in. However, the legacy technology based on hybrid filtering has problems such as data, the analytic nature of the hybrid recommendation, the cold start problem, and the difficulty of multi-algorithm fusion. Therefore, this article conducts a comprehensive and specific study on the above-mentioned problems and proposes corresponding solutions. In the paper, the spectrogram generated by noisy speech is regarded as the result of a clean image being affected by noise, and the denoising or defogging technology that maintains the edge while smoothing the noise is used to process the spectrogram of noisy speech. Regard the spectrogram of noisy speech as a foggy or noisy image, and the image filtering strategy is used to extract pure speech signals and achieve the purpose of speech emphasis. This paper uses a variety of different visualization methods to visually analyze and research many film and television works. Through more intuitive visualization results, in order to more clearly analyze the characteristics, content, and other similarities and differences of these film and television works, a vertical and horizontal comparison and investigation were carried out.
Funding This paper was supported by the fund name as Study on government coordination mechanism between pilot Free Trade zones and peripheral economic function zones (Project Number: 21CZZ033).
Data availability Data will be made available on request.

Declarations
Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval This article does not contain any studies with human participants performed by any of the authors.