# Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Authors: Jin Tong; Jian Sun
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the vigorous development of new media technologies such as Internet of Things, big data, and cloud computing, data-based sensor news (SN) will become the trend of news reporting in the future and the new normal of news production. Under this background, this paper further analyzes the relationship between SN production mode and traditional news production, including the inheritance of traditional news production value concept, as well as the breakthrough and change in form, media, and effect. In this paper, collaborative filtering (CF) algorithm is improved to solve the problems of data sparseness, user interest migration, and scalability in CF technology. In the calculation of news content similar degree (SD), the influence of part of speech and position of feature words in news is also considered, and the time window is used to establish a model that adapts to the change of user interest with time. In this method, the contribution degree of different attributes to distinguishing users is considered, and the attribute SD between users is accurately calculated, which effectively improves the accuracy of SN data acquisition results.


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## Body

## 1. Introduction

Under the logic of the Internet, technological innovation has brought about great changes to human life, and at the same time, alienation has become a common phenomenon. Most modern people’s lifestyles are quietly being constructed by technology and controlled by various comfortable “nets” compiled by technology [1]. Sensor news (SN) is a nonindependent news report type based on big data and supported by Internet logic. It is a way to capture data by the integration of sensor technology and artificial intelligence technology [2, 3] for news media to make news content. The Internet of Things not only connects things with the Internet but also connects things with things and people, thus constructing a network system. Today’s Internet of Things has a more profound impact on human activities, from smart bracelets, watches, Google Glass to large-scale unmanned aerial vehicle (UAV) detection and smart city construction, the Internet of Things is playing an increasingly important role [4].News recommendation algorithm is a hot topic in new media research [5, 6]. In the pre-Internet era, newspapers, radio, television, and other traditional media mainly recommended information for the audience by hand; at the initial stage of the development of the Internet, the popular recommendation methods of information were developed and widely used in websites. Collaborative filtering (CF) recommendation algorithm is a widely used personalized recommendation technology. A large amount of information has become a problem that must be faced and solved, so information filtering technology has become a technology like search engine and CF [7]. Search engine is to collect and analyze data from the massive information of the Internet, and search and query news information according to the index. CF is to analyze the relationship between two things, classify them, find out the close relationship between them, and make a prediction for the future on this basis, so as to help users obtain effective information [8, 9]. Therefore, it is of great significance for the development of personalized information services in China to focus on personalized recommendation CF technology. At the same time, CF algorithm involves many fields of knowledge. The research on CF is not only of great significance to the development of recommendation field but also plays a certain role in promoting other fields [10].In the age of big data, journalists collect data using self-made sensors or directly obtain data content from the government’s official sensor system, then analyze it to create SN reports [11]. It can be said that sensing technology has changed the one-way communication era’s blindness and arrogance, and that SN has also reconstructed the media’s ecological environment and stimulated a new news production mode. The impact of changing news content and users’ interests on news recommendation is discussed in this paper, and an improved CF algorithm that incorporates both news content and users’ interests is proposed to acquire SN data.


## 2. Related Work

Foreign media have been incorporating sensor data into news reports since May 2013, when the Thor Digital News Center of Columbia University proposed the concept of “SN.” According to the literature [12], sensors will have a lot of potential in the field of news communication in the future. The literature [13] proposes four ways for media organizations to use sensors, as well as sensor application fields in news communication and challenges for traditional media personnel. Sensors, as important technologies for producing and collecting data, will gradually become a trend in the field of news dissemination, according to literature [14], but in practice, they still face security, legal, and ethical issues that require further consideration and research. The literature [15] discusses the practical application fields of SN, and predicts that sensors will play an increasingly important role in environmental news, investigative journalism, citizen participatory journalism, and drone news. According to literature [16], in the process of practical development, SN should pay attention to the authenticity of data, and sensor reports should strike a balance between technology, art, and humanity, as well as make news reports with temperature and depth. Sensors, as an effective medium for connecting various technologies, can promote cross-border integration of technologies and ensure their long-term viability, according to literature [17]. Sensor technologies should be used effectively by the media, and efforts should be made to help people better understand themselves and the world by utilizing sensor data information.Personalized recommendation system is the current hot spot of news recommendation algorithm research. Literature [18] suggests that considering that the similarity between users is not only related to the items that users overestimate but also to the degree of users’ interest in the items, a similarity calculation method based on users’ interest is proposed, which reduces the negative impact of data sparseness in traditional algorithms to some extent. Literature [19] calculates the mixed similar degree (SD) value between users according to the evaluation matrix of users and the news feature word matrix with time weight, which effectively improves the data sparseness problem in traditional algorithms. Literature [20, 21] combines the CF algorithm based on projects with demographic statistics based on user groups, which effectively solves the cold start problem of users. Literature [22] thinks that the influence of the algorithm on SN data acquisition results is far less than the data itself. Literature [23] holds that with the rapid expansion of information in the network, the computational complexity of CF algorithm in finding the nearest neighbor is greatly increased, so the requirements for the algorithm’s performance, computing power, and storage capacity of equipment are getting higher and higher. Literature [24] regards item attributes as a reference factor of recommendation. Firstly, it analyzes users’ preferences for item attributes, and then it generates recommendations for users in combination with users’ preferences for items, which overcomes the shortcomings of only considering evaluation information in the past and improves the recommendation effect. Literature [25] puts forward that both item characteristics and time factors should be applied to the recommendation process. Firstly, the feature similarity between items is calculated and the scoring matrix is predicted and filled. In the prediction process, the time factor is considered, and SD calculation and recommendation are carried out according to the matrix after prediction filling, which effectively improves the recommendation effect.In fact, for a single piece of information, a recommendation algorithm may not be as effective as a simple popular recommendation. As a result, it is best to combine the results of popular recommendation, content-based recommendation, CF-based recommendation, and even other recommendation methods in some form to create a collection of news information recommended by various algorithms, which can then be shown to users for reading and improving the recommendation effect.


## 3. Research Method

### 3.1. Optimization Path of SN Development

Intelligent sensor technology can sense the subtle changes of the environment, complete the functions of real-time collection of scientific data, various data presentation modes, recording the change process, and so on, and can be connected with computers through standardized digital interfaces to realize the network transmission, distribution, and sharing of information. In recent years, new research and development directions such as microsensor, multi-sensor data fusion, networking, Bluetooth sensor, nanosensor, and biosensor have emerged.Digital technology has changed the way of news collection, processing, and diffusion to a great extent, and the concept of the public is no longer as narrow and convenient as it used to be. It is now everywhere. The public is not only the target of news production but also the producer of news. The media have to consider the influence of the public on news. Sensors rely on new media technologies, such as big data and Internet of Things, which make it possible to collect information comprehensively, timely, and extensively. Relying on sensors, panoramic news enriches the richness and comprehensiveness of data, and excavates the appearance of dark matter and black forest in a wider field. The “small data era” based on sampling samples will be replaced by the “big data era” in the future.The emergence of SN is a great change both in the news concept and in the news practice field, especially in the realization of broad public participation and breaking the data monopoly, which shows innate technological advantages. However, in the related practice of Chinese media industry, SN production also faces some problems and challenges. The optimized path of SN development is shown in Figure1.Figure 1
Optimization path of SN development.Although the means and forms of news communication are constantly changing, some core ideas, such as interview principles and editing concepts, have not changed fundamentally, and these basic news capabilities are still required in SN. On this foundation, it should also have data collection and analysis capabilities, as well as the ability to disseminate digital information. All kinds of data emerge one after another in the data age, but valuable data are frequently submerged in the information ocean, and their significance cannot be presented naturally. In the data age, mining and interpreting data have become essential skills for media professionals. It is also important to have a sense of collective cooperation and to understand your role in each attack. As a result, teamwork is critical at all levels of the media industry in the data age. One of the benefits of SN production is predictive reporting. By increasing investment in the field of advantageous reporting, the audience will have a better understanding of this news production mode in the future. It is becoming increasingly necessary to speak by data to gain insight into nature and society, and these data will increasingly come from the Internet of Things in large quantities in the future. Many sensors in the Internet of Things have the primary function of monitoring the changing process and trend of some objects. As a result, predicting the future using sensor data is unavoidable.With the further development of Internet of Things and sensor technology, it has become normal for the media industry to use sensors for news reporting. Then, the electronic media overcomes the sensory division of the visual space and provides a vast world for people’s high participation and intervention. “and SN is the expression of the so-called human extension.”. News media need to consider some legal and security risks that the public may encounter in the process of collecting data. It is unreasonable for the public to collect information or content for the media at the expense of their own safety, or to accept some substantial results when they choose to take risks, which is the irresponsible performance of the media. The media is not only responsible for the subject and audience of news stories but also for those who help in collecting information. This requires the media to consider how to help and guide the public before reporting.


### 3.2. SN Data Acquisition Based on CF Algorithm

The traditional way of news dissemination makes every news reader see the same content, some of which have one side for thousands of people. The main reason is that the news published in newspapers or portals are divided into different sections based on news categories, and the selected news materials are the same, and the editing classification tends to be consistent. The core problem of the recommendation system is how to mine user preferences and build a user portrait model to predict user preferences. The system leads users, provides users with information ranking, cultivates reading habits, guides users’ behavior, makes users rely more and more on websites, and improves users’ loyalty [20].Content-based recommendation algorithm is to find out the information of similar content from the content and feature information that users are concerned about, and then make personalized recommendation. Actually, it is to recommend similar items with the content that users like according to historical information. Among them, the most important part of this recommendation process depends on content analyzer, text filter, and text learner. It has the characteristics of interpretability, which can give the reason of recommendation at the same time, and enhance the trust of users. But, it also has one of the biggest disadvantages, that is, it cannot recommend potentially favorite content.In the project-based CF algorithm, the historical data of user access news can be expressed in the form of matrixDn∗m.(1)Dn∗m=D1D2⋮DN=d11d12⋯d1md21d22⋯d2m⋮⋮⋱⋮dn1dn2dnm,where n represents the total number of users accessing the news system; m represents the total number of news visited by n users; dij represents whether user i has read the news j; 1 represents reading; and 0 represents not reading.Because of the text characteristics of news, SD of news should not only consider SD based on user access but also consider the similarity of news content. Therefore, when calculating SD of news content, consider the influence of part of speech and word position. To solve these problems, this paper proposes a Collaborative Filtering Recommendation Based on Time and Trust (CFRTT) algorithm, which improves the CF algorithm by constructing a user interest migration model and a Trust degree (TD) model, and effectively alleviates the influence of user interest changes and data sparseness on recommendation accuracy.Affected by various external factors, users’ interests and preferences are constantly changing with time. Time weight function based on forgetting curve can effectively improve the accuracy of SN data acquisition results. The formula of its time weight function is shown in formula (2).(2)ft,i=et−Tmin/Tmax−Tmin−1.Among them,Tmax represents the time interval between the user using the system and the latest scoring, Tmin represents the time interval between the user using the system and the user’s earliest scoring, and t represents the scoring time of the current project.In some cases, there is only one way for a user to establish an indirect trust relationship with another user. The product of direct TD among users in the transfer path is taken as the indirect TD value of users, based on two transfer rules of trust. The function is shown in formula (3).(3)I_Tu,v=D_Tu,n1∗D_Tn1,n2∗⋯∗D_Tnk,v,where D_Tu,n1,D_Tn1,n2,D_Tnk,v represents the direct TD between nodes.In the case of multi-paths, the largest TD value in all paths is selected as the indirect TD value between users, and the function is shown in formula (4).(4)I_Tu,v=maxI_Tp1u,v,I_Tp2u,v,…,I_Tpku,v.In whichI_Tp1u,v,I_Tp2u,v,I_Tpku,v represents the indirect TD values of users obtained through the k paths of p1,p2,…,pk, respectively.Combine TD with SD based on time to generate comprehensive weight, and make prediction recommendation according to the generated comprehensive weight. The combination method of TD and SD is shown in formula (5).(5)Wu,v=γD_Tu,vI_Tu,v+1−γsimimeu,v.In whichsimimeu,v represents the user similarity based on time, D_Tu,v represents the direct TD of user u and user v, and I_Tu,v represents the indirect TD of user u and user v.The recommended process of CFRTT algorithm is shown in Figure2.Figure 2
CFRTT algorithm recommendation process.In the age of big data, the placement of sensors obscures residents’ personal information. Journalists must pay close attention to ethical issues arising from the disclosure of personal information. Some news organizations purposefully expose other people’s privacy in order to gain attention. The existence of the aforementioned behaviors can have a significant impact on the news industry’s overall development environment and can easily lead to social chaos. Journalists must pay more attention to ethical issues in order to solve the problems mentioned above.As the amount of network information grows, so does the number of users and projects, and the traditional CF algorithm runs into scalability issues. However, in the case of large user and project data, real-time recommendation cannot be guaranteed. As a result, a personalized recommendation based on clustering and collaborative filtering (PRC CF) algorithm is proposed based on the above.Jaccard coefficient measurement is the most commonly used method to calculate the user attribute SD, which can represent the SD between two sets. Therefore, the SD calculation formula of user feature attributes is shown in formula (6).(6)simattru,v=w1⋅g1u,v+w2⋅g2u,v+⋯+wn⋅gnu,v.In whichsimattru,v represents the characteristic attribute SD of the user u,v, and gnu,v represents the similarity relation of the nth attribute of the user u,v. The entropy method is used to determine the weight of user attributes.News content SD can be calculated by using the inner product method, cosine SD calculation method, etc. The cosine SD calculation method is adopted here, and the calculation function is shown in formula (7).(7)sim2i,j=DWi′∗DWj′DWi′∗DWj′,DWi′where DWi′represents the vector space model of news i and DWj′ represents the vector space model of news j.Only SD matching at the tag level can be carried out from its unique high-frequency keyword level, which will only produce superficial topics and tag recommendations, but cannot produce deeper recommendations that match users’ temperament, personality, lifestyle, and so on. Computers can only help us quickly complete simple and repetitive tasks, but it is difficult to meet some delicate psychological needs in news information reading at a deeper level.


## 3.1. Optimization Path of SN Development

Intelligent sensor technology can sense the subtle changes of the environment, complete the functions of real-time collection of scientific data, various data presentation modes, recording the change process, and so on, and can be connected with computers through standardized digital interfaces to realize the network transmission, distribution, and sharing of information. In recent years, new research and development directions such as microsensor, multi-sensor data fusion, networking, Bluetooth sensor, nanosensor, and biosensor have emerged.Digital technology has changed the way of news collection, processing, and diffusion to a great extent, and the concept of the public is no longer as narrow and convenient as it used to be. It is now everywhere. The public is not only the target of news production but also the producer of news. The media have to consider the influence of the public on news. Sensors rely on new media technologies, such as big data and Internet of Things, which make it possible to collect information comprehensively, timely, and extensively. Relying on sensors, panoramic news enriches the richness and comprehensiveness of data, and excavates the appearance of dark matter and black forest in a wider field. The “small data era” based on sampling samples will be replaced by the “big data era” in the future.The emergence of SN is a great change both in the news concept and in the news practice field, especially in the realization of broad public participation and breaking the data monopoly, which shows innate technological advantages. However, in the related practice of Chinese media industry, SN production also faces some problems and challenges. The optimized path of SN development is shown in Figure1.Figure 1
Optimization path of SN development.Although the means and forms of news communication are constantly changing, some core ideas, such as interview principles and editing concepts, have not changed fundamentally, and these basic news capabilities are still required in SN. On this foundation, it should also have data collection and analysis capabilities, as well as the ability to disseminate digital information. All kinds of data emerge one after another in the data age, but valuable data are frequently submerged in the information ocean, and their significance cannot be presented naturally. In the data age, mining and interpreting data have become essential skills for media professionals. It is also important to have a sense of collective cooperation and to understand your role in each attack. As a result, teamwork is critical at all levels of the media industry in the data age. One of the benefits of SN production is predictive reporting. By increasing investment in the field of advantageous reporting, the audience will have a better understanding of this news production mode in the future. It is becoming increasingly necessary to speak by data to gain insight into nature and society, and these data will increasingly come from the Internet of Things in large quantities in the future. Many sensors in the Internet of Things have the primary function of monitoring the changing process and trend of some objects. As a result, predicting the future using sensor data is unavoidable.With the further development of Internet of Things and sensor technology, it has become normal for the media industry to use sensors for news reporting. Then, the electronic media overcomes the sensory division of the visual space and provides a vast world for people’s high participation and intervention. “and SN is the expression of the so-called human extension.”. News media need to consider some legal and security risks that the public may encounter in the process of collecting data. It is unreasonable for the public to collect information or content for the media at the expense of their own safety, or to accept some substantial results when they choose to take risks, which is the irresponsible performance of the media. The media is not only responsible for the subject and audience of news stories but also for those who help in collecting information. This requires the media to consider how to help and guide the public before reporting.


## 3.2. SN Data Acquisition Based on CF Algorithm

The traditional way of news dissemination makes every news reader see the same content, some of which have one side for thousands of people. The main reason is that the news published in newspapers or portals are divided into different sections based on news categories, and the selected news materials are the same, and the editing classification tends to be consistent. The core problem of the recommendation system is how to mine user preferences and build a user portrait model to predict user preferences. The system leads users, provides users with information ranking, cultivates reading habits, guides users’ behavior, makes users rely more and more on websites, and improves users’ loyalty [20].Content-based recommendation algorithm is to find out the information of similar content from the content and feature information that users are concerned about, and then make personalized recommendation. Actually, it is to recommend similar items with the content that users like according to historical information. Among them, the most important part of this recommendation process depends on content analyzer, text filter, and text learner. It has the characteristics of interpretability, which can give the reason of recommendation at the same time, and enhance the trust of users. But, it also has one of the biggest disadvantages, that is, it cannot recommend potentially favorite content.In the project-based CF algorithm, the historical data of user access news can be expressed in the form of matrixDn∗m.(1)Dn∗m=D1D2⋮DN=d11d12⋯d1md21d22⋯d2m⋮⋮⋱⋮dn1dn2dnm,where n represents the total number of users accessing the news system; m represents the total number of news visited by n users; dij represents whether user i has read the news j; 1 represents reading; and 0 represents not reading.Because of the text characteristics of news, SD of news should not only consider SD based on user access but also consider the similarity of news content. Therefore, when calculating SD of news content, consider the influence of part of speech and word position. To solve these problems, this paper proposes a Collaborative Filtering Recommendation Based on Time and Trust (CFRTT) algorithm, which improves the CF algorithm by constructing a user interest migration model and a Trust degree (TD) model, and effectively alleviates the influence of user interest changes and data sparseness on recommendation accuracy.Affected by various external factors, users’ interests and preferences are constantly changing with time. Time weight function based on forgetting curve can effectively improve the accuracy of SN data acquisition results. The formula of its time weight function is shown in formula (2).(2)ft,i=et−Tmin/Tmax−Tmin−1.Among them,Tmax represents the time interval between the user using the system and the latest scoring, Tmin represents the time interval between the user using the system and the user’s earliest scoring, and t represents the scoring time of the current project.In some cases, there is only one way for a user to establish an indirect trust relationship with another user. The product of direct TD among users in the transfer path is taken as the indirect TD value of users, based on two transfer rules of trust. The function is shown in formula (3).(3)I_Tu,v=D_Tu,n1∗D_Tn1,n2∗⋯∗D_Tnk,v,where D_Tu,n1,D_Tn1,n2,D_Tnk,v represents the direct TD between nodes.In the case of multi-paths, the largest TD value in all paths is selected as the indirect TD value between users, and the function is shown in formula (4).(4)I_Tu,v=maxI_Tp1u,v,I_Tp2u,v,…,I_Tpku,v.In whichI_Tp1u,v,I_Tp2u,v,I_Tpku,v represents the indirect TD values of users obtained through the k paths of p1,p2,…,pk, respectively.Combine TD with SD based on time to generate comprehensive weight, and make prediction recommendation according to the generated comprehensive weight. The combination method of TD and SD is shown in formula (5).(5)Wu,v=γD_Tu,vI_Tu,v+1−γsimimeu,v.In whichsimimeu,v represents the user similarity based on time, D_Tu,v represents the direct TD of user u and user v, and I_Tu,v represents the indirect TD of user u and user v.The recommended process of CFRTT algorithm is shown in Figure2.Figure 2
CFRTT algorithm recommendation process.In the age of big data, the placement of sensors obscures residents’ personal information. Journalists must pay close attention to ethical issues arising from the disclosure of personal information. Some news organizations purposefully expose other people’s privacy in order to gain attention. The existence of the aforementioned behaviors can have a significant impact on the news industry’s overall development environment and can easily lead to social chaos. Journalists must pay more attention to ethical issues in order to solve the problems mentioned above.As the amount of network information grows, so does the number of users and projects, and the traditional CF algorithm runs into scalability issues. However, in the case of large user and project data, real-time recommendation cannot be guaranteed. As a result, a personalized recommendation based on clustering and collaborative filtering (PRC CF) algorithm is proposed based on the above.Jaccard coefficient measurement is the most commonly used method to calculate the user attribute SD, which can represent the SD between two sets. Therefore, the SD calculation formula of user feature attributes is shown in formula (6).(6)simattru,v=w1⋅g1u,v+w2⋅g2u,v+⋯+wn⋅gnu,v.In whichsimattru,v represents the characteristic attribute SD of the user u,v, and gnu,v represents the similarity relation of the nth attribute of the user u,v. The entropy method is used to determine the weight of user attributes.News content SD can be calculated by using the inner product method, cosine SD calculation method, etc. The cosine SD calculation method is adopted here, and the calculation function is shown in formula (7).(7)sim2i,j=DWi′∗DWj′DWi′∗DWj′,DWi′where DWi′represents the vector space model of news i and DWj′ represents the vector space model of news j.Only SD matching at the tag level can be carried out from its unique high-frequency keyword level, which will only produce superficial topics and tag recommendations, but cannot produce deeper recommendations that match users’ temperament, personality, lifestyle, and so on. Computers can only help us quickly complete simple and repetitive tasks, but it is difficult to meet some delicate psychological needs in news information reading at a deeper level.


## 4. Result Analysis and Discussion

News production gains new development opportunities and a certain data base under the conditions of big data, but it also faces more changes. The traditional crowd has given way to the “data group,” and interviews with people have given way to data mining and analysis. However, in the innovative practice, the shadow of traditional news values can still be seen. SN has not deviated from the traditional news production framework, and the relationship between them is both “changing” and “unchanging,” necessitating a dialectical approach.Journalists should not only improve the accuracy of data collection by hardware and software but also invest manpower to verify the authenticity of data and minimize possible data errors. Based on the excellent news report stories, the data can exert the maximum efficiency. SN should not be defined as “pure data” news but should be combined with other news reporting methods and presentation methods, so that data can give breadth and depth to news stories. First of all, determine the number of users’ neighboring sets and the number of news recommendations. In order to better distinguish the experimental results, the number of neighboring sets is 30 and the number of recommended news is set to 15. As can be seen from Figure3, when σ=0.5, F-measure value is the highest, so σ=0.5 in the formula is set.Figure 3
Influence of different values ofσ on CFRTT algorithm.SN’s inheritance of traditional news is mainly reflected in the thinking of news production. Under the traditional news concept, SN is a tool for collecting news data sources, and the fundamental goal of news production is to explore the truth of events. In the context of big data, this core has not changed, at least at this stage. However, it is worth noting that with the deepening of data technology, the media form and news participation are changed, which leads to the change of media functions, thus leading to a more complex and flexible judgment standard for facts. The truth and value judgment of facts are also complicated because of the difference of its connotation and extension.The results of Mean Absolute Error (MAE) values when differentγ values are selected, as shown in Figure 4.Figure 4
Influence ofγ weight on MAE.As can be seen from Figure4, the MAE value increases with the increase of γ, which shows that too much weight of TD in the comprehensive weight will reduce the accuracy of the algorithm, so one can choose γ=0.4 as the best weight value of TD.Compared with traditional investigative news, the application of sensors in investigative news reduces the risk of investigation. Journalists can rely on a large amount of data provided by sensors. As long as the data analysis is accurate, traditional unannounced visits and in-depth interviews can be reduced when explaining problems with data, and journalists can reduce their dependence on experts when pursuing the truth.In the stage of information collection, the formation mechanism of news information for data and field information collection will greatly depend on the realization of computer technology automation and data mining technology. Generally speaking, the traditional information description is based on the linear direction, which can be used as a means to reveal the relationship between information. This method presents a single two-dimensional plane.Let the transmission pathL=3, weight γ=0.4, and the nearest neighbor number take T=5,10,15,20. Under this condition, compare the MAE value of clustering number k=2,4,6,8,10. The comparison result is shown in Figure 5.Figure 5
Influence of cluster numberk on MAE.As can be seen from Figure5, with the increase of k value of the user cluster number, MAE value first becomes smaller, because with the clustering of users’ attribute characteristics, users in the same cluster are similar in attribute and taste, so the nearest neighbor group selected from users with similar characteristics is more reliable, and the obtained SN data acquisition result is more accurate.Data collection, mining, statistical analysis, and visualization can be summarized as the process of the new news reports. The importance and function of audience participation is highlighted during the information dissemination stage. More social subjects, rather than simple news parties, such as so-called news participants, become subjects of news production as a result of big data and related technologies. It began by relying on computers and communication equipment, which resulted in a fundamental shift in the data news communication mode.Figure6 shows the F-measure values of SN data acquisition results of CFRTT algorithm, traditional CF algorithm, literature [18], and literature [22] algorithm when the number of recommended news items is 10–40, respectively.Figure 6
F-measure value of each algorithm under different recommended items.The results show that the algorithm of this paper has the highest value of F-measure, which reaches the highest value when the number of recommended news articles is 35.Visualization is an effective way to explore and understand large data from the standpoint of exploration. Graphics are something that humans are very good at. Placing numbers in visual space can encourage the brain to seek out and investigate hidden meanings. Visualization is not a cold tool when it comes to expression. Visual communication has progressed beyond the use of tools to become a medium for communicating ideas. The media can use various visual components to not only help readers understand news stories but also to convey emotions and ideas, as well as express media opinions.At present, with the increasing importance of data information, it is becoming a trend to use data to make news. Production and data collection are only the most basic applications of SN. With the development and opening of various mobile terminals, positioning sensors, and physiological sensors of wearable devices, it will be no longer difficult to accurately push local news and information services to users, and monitor and feedback user experience in real time.Figure7 shows the MAE values of SN data acquisition results of the recommendation algorithm, traditional CF algorithm, literature [18], and literature [22] algorithm in this paper when the number of news neighbor sets is 50–80 and the number of recommended news items is 20, respectively.Figure 7
MAE value of each algorithm with different number of neighbor sets.The results show that the MAE value of this algorithm can reach the minimum under different number of neighbor sets, which shows that the difference value recommended by this algorithm is the smallest and the recommendation quality is the highest.Big data itself has a variety of attribute values. Comprehensively use different data analysis methods to mine from multiple angles, so as to highlight the value of multiple data including news, and the news audience can find their own values and really participate in reading. With the help of media visualization technology, news production has become the integration of multimedia technology and visual arts. Through innovative ways, create multi-level visual charts, supplemented by words, to achieve accurate proportion of the use of interactive design and other artistic means, to show visual data, to achieve specific thematic news narratives and superb information charts for the audience.Take the best cluster numberk=8, and then use CFRTT algorithm and PRCCF algorithm to find the nearest neighbor group and compare the results. The results are shown in Figure 8.Figure 8
Efficiency of nearest neighbor searching based on the algorithm.It can be seen from Figure8 that the PRCCF algorithm can search more neighbor users than CFRTT algorithm, and the smaller the search space, the more obvious the effect, so the PRCCF algorithm proposed in this paper can effectively improve the nearest neighbor searching efficiency, that is, the real-time performance of the algorithm.News visualization is the evolution and innovation of traditional narrative mode in the context of new technology. The concept of news can easily be deduced from sensor data, and SN’s final presentation is a visually diverse and interactive experience. In comparison to the traditional narrative form, the visual approach makes data more readable, which can improve not only news consumption but also the objectivity and credibility of news and interactive data features, such as participation in traditional news. The dynamic relationship between independent and dependent variables under complex social conditions is analyzed on this basis, and event development and social trends are predicted. These developments have resulted in the emergence of big data socialization in the new media era, as well as the ability to read news on the fly.Under the above conditions, compare the MAE values of CFRTT algorithm, PRCCF algorithm, and traditional CF algorithm with different nearest neighbor numbersT=30∼60. The experimental results are shown in Figure 9.Figure 9
Comparison of algorithm MAE values.It can be seen from Figure9 that the number of nearest neighbors affects the recommendation performance of the algorithm. As the number of nearest neighbors increases, the MAE value decreases continuously, but when it reaches a certain number, the change of recommendation accuracy is no longer obvious. Furthermore, the recommendation accuracy of PRCCF algorithm is slightly higher than that of CFRTT algorithm, because PRCCF algorithm divides users with similar attributes into the same cluster by clustering, and only searches among users with similar attributes when searching the nearest neighbor, which reduces the calculation amount and improves the reliability of neighbor users’ recommendation.The expression of audience opinions is smoother than it was in the traditional media era. Individual audiences’ opinions will be blocked in some channels, such as being deleted, but they will be able to express themselves through other channels, and it is extremely difficult, if not impossible, to completely control all communication channels. Overall, the likelihood of authoritative forces outside the audience interfering with the formation and dissemination of the audience’s opinions has been greatly reduced. If there are still factors that limit freedom of expression, one of the most important considerations is the specific situation in which the audience expresses their opinions, such as peer pressure and the influence of opinion leaders.


## 5. Conclusion

In the era of big data, the traditional news production model will inevitably undergo innovation, which will be determined by the evolution of the times and the needs of the audience. The thinking and idea of news production will be impacted by this transformation, but it will not change, and the development of SN at this stage is bound to be subject to the macro idea of traditional news. The CFRTT and PRCCF algorithms proposed in this paper can improve the accuracy of SN data acquisition results, and the CTTCF algorithm can improve the algorithm’s real-time performance while increasing recommendation accuracy. This demonstrates that the proposed algorithm generates high-quality recommendations. To improve the performance of SN data acquisition, we will look at semantic similarity between texts and the impact of clustering.



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*Source: 1504454-2022-02-12.xml*