# Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

Authors: Xiancai Kang; Chuangli Hua
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to explore how to realize network mining for associated users, an algorithm of associated user mining based on recommendation system is proposed. This method recommends key technical problems and solutions based on multi-information fusion to explore the research of user network data mining. The data mining algorithm of associated user network based on multi-information fusion is about 35% higher than the previous method. This article is combined with common scoring data to improve the accuracy of algorithm results. The experiment was carried out on the real data set FilmTrust to evaluate the proposed new algorithm, measure the prediction accuracy of the score prediction results, and compare the offline test results to verify the effectiveness of the new algorithm.


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## Body

## 1. Introduction

Recommender system (RS) can discover and capture users’ preferences and recommend relevant content to users. As a bridge between users and projects, RS plays a key role in many scenes of people’s daily life [1]. Today, recommendation system is online shopping, video websites, and search engines, and many other fields have been rapidly developed. Relevant researches in China emerge endlessly and become a hot topic in academia and industry in recent years [2].In the field of social networks, in recent years, due to the gradual increase of users and the more frequent update rate of information, the explosive growth of network data has led to the problem of information overload [3]. Many efforts have been made in academia and industry to accurately capture users’ needs or preferences and filter out useless or uninteresting content for users. Collaborative filtering, as a mainstream method in the field of personalized recommendation, seeks for the similarity between different individuals and selects the most similar individuals to meet the personalized needs of different users. Although collaborative filtering can effectively alleviate the phenomenon of information overload and is widely used, the actual effect of most existing methods based on collaborative filtering is not satisfactory in the face of data sparse and cold start scenarios [4]. In addition, the characteristics of social network make it complicated, and the results of traditional recommendation methods are not ideal when personalized recommendation is carried out for users in social network environment. On the basis of in-depth analysis of common recommendation algorithms, this article tries to combine the idea of multiple information fusion in the personalized collaborative filtering algorithm to help better understand users’ interests. In addition, the user item rating data commonly used in collaborative filtering algorithm is combined with the idea of matrix decomposition and the social network trust model to improve the problem of data sparsity and improve the prediction accuracy of recommendation system.In essence, the recommendation system solves the problem of information overload by pushing new items that users are not familiar with but may need or be interested in [5, 6]. The system usually receives a large number of user requests at the same time, analyzes the different needs of users based on their real demand environment, and adopts different recommendation mechanisms to respond to users. The system uses the user’s personal information, item-related information, and historical purchase records saved in the customized database to help generate recommendation results [7]. After obtaining the recommendation information, users may choose to adopt or reject the generated recommendation content, or they may give various forms of explicit or implicit feedback immediately or after a long time. The system then saves all these user behaviors and feedback information into the log system, so that users can obtain appropriate recommendation information when they use it again in the future [8]. Figures 1 and 2, respectively, describe the basic workflow and ideas of the recommendation system.Figure 1
Basic workflow of recommendation system.Figure 2
Basic idea of recommendation system.Generally, the main data sources of recommendation system include metadata of articles, such as category and keywords; basic user information, such as gender; and users’ historical preferences, such as ratings and browsing records [9]. There is no doubt that explicit user feedback can accurately express the actual needs or interests of users, but this kind of feedback data is relatively rare. However, it is relatively easy to obtain the implicit feedback information of users. After analysis and processing, it can also reflect users’ preferences from a certain angle, but the accuracy is not high, resulting in the need to spend more on processing data noise. However, if appropriate behavioral characteristics can be accurately selected, implicit feedback information can still obtain good recommendation results [10]. In different scenarios, the selection of behavior characteristics is also different, and the recommendation system will adopt different recommendation mechanisms to select data sources.


## 2. Literature Review

The recommendation system originated from GroupsLens’ exploration of Movielens in the University of Minnesota has a history of more than 20 years. Later, Amazon applied recommendation system technology to e-commerce, analyzing users’ purchase records and inferring what they might be interested in. In recent years, recommendation system has been widely applied and extended in academic and enterprise fields. In the academic field, since ACM held the International Conference on Recommendation System in 2019, ACM has also set up a special group to study recommendation system. In the 24th seminar, recommendation system was separately listed as a hot item for discussion by SIGIR group of ACM Society. Amh et al. found that many enterprises also provided open data sets for researchers, among which the most famous was the recommendation system competition organized by Netflix [11]. Some Chinese scholars have also gradually deepened their research on recommendation systems. For example, they elaborate on the key technologies, architecture, and performance evaluation indexes of recommendation systems and analyze the problems to be overcome in recommendation research and the follow-up directions worth exploring. This article describes the core method, effect evaluation, and method practice of mobile recommendation system and prospects its future application direction. Deep learning is ground-breaking integrated into the recommendation system to study how to integrate massive multisource heterogeneous data to improve user satisfaction and analyze the differences and advantages between it and traditional recommendation system. Based on the classic CNN (convolutional neural network) network structure, a convolutional neural network model of image recognition and word recommendation system under the background of artificial intelligence is proposed to realize image recognition and word recommendation based on deep learning. An implicit feedback matrix decomposition algorithm based on emotion modeling between users and objects is proposed by learning potential factors in videos. Yin et al. focused on personalized recommendation in social networks, summarized the influence of popular social factors on the final results, elaborated the definition of trust and the calculation method of trust, and projected the research dilemma and development trend of trust networks [12]. Combined with all the returned information and the changing trend of social networks, a time-aware recommendation algorithm based on user feedback information is proposed. Weighted social networks are processed by time attenuation function, and the similarity calculation method is improved. The information returned by users is classified into positive feedback information. The other is negative feedback. Then, the influence of these feedback information on the algorithm is summarized, and experiments are carried out to prove that the proposed recommendation algorithm has better performance. Service recommendation in hot topic information physical system (CPS) is studied. In view of the high data sparsity in CPS which may affect the prediction accuracy, the potential similarity between users or services in CPS is mined to improve the prediction accuracy. In addition, Hu et al. constructed a prediction model using random walk and fully verified it on two effective data sets to prove the feasibility of the algorithm when most traditional collaborative filtering methods ignored contextual information such as network location. It is verified that network location is really useful in QoS (quality of service) prediction [13].In the field of enterprise, recommendation system is also an important technical means of many websites. For example, the very famous Facebook and Twitter use recommendation system to recommend friends to users, and Google use recommendation technology to push information that users may like. Netflix and Youtube use recommendation system technology to recommend content-related videos to users. Tmall, JINGdong and others rely on the recommendation system to provide users with commodity push. Doubanchai pushes fresh and interesting content to users. Percentage Point Group is the leading big data and recommendation engine technology company in China’s Internet. The research and development of recommendation engine and analysis engine has been quite mature and has been successfully used in more than 1500 enterprises.Shi et al. found that mobile Internet not only has the characteristics of open Internet but also has the characteristics of real-time, portable, and positioning interaction of mobile network [14]. The mobile Internet is much more portable for users. For operators, mobile Internet is more open and controllable than traditional Internet. Mobile Internet has penetrated into every field of people’s life and work. WAP is one of the most promising services in mobile Internet and the most personalized e-commerce tool. Gupta and Maiti believe that it has three main applications: first, public service, which means providing users with the latest real-time information, such as weather, news, and traffic; second, personal information services, including social networking, information search, email, portal browsing, and information query; the third is commercial economic services, which include basic office applications and the most potential mobile commerce applications, including shopping, payment, and reservation [15]


## 3. Methods

### 3.1. Classification and Comparison of Recommendation Systems

Recommendation systems can be classified in different ways according to different analysis perspectives. Through sorting and analysis, the following three recommendation systems based on classification are mainly introduced:(1)
Different recommendation results are divided into:Based on the recommendation of mass behavior, popular items are pushed to all users by calculating the current popular items. It can push dynamic hot and concerned content to each user in real time, especially for items seeking promotion, which has a good practical effect. Its performance is relatively stable in popular movies, focus news, and other application fields, but the accuracy of recommendation results is not satisfactory [16].Personalised recommendations are based on user experience, based on data such as historical preferences. It mainly uses the historical data of users to analyze the habits and characteristics of different individuals or based on the needs of the current environment to achieve targeted services. It is characterized by giving full consideration to the differences of individual preferences of users, making appropriate judgments and pushing the results to users. It has good recommendation accuracy and is an ideal recommendation method. But the cost is high [17] (see Table 1).Table 1
Characteristics summary.
Recommendations based on crowd behaviorPersonalized recommendationAdvantagesHot information recommendation effect is goodGood accuracyDisadvantagesAccuracy is poorHigh input costCollaborative filter-based recommendation realizes recommendation by analyzing current users’ preferences and demands for items and information and calculating the similarity of items or users themselves. As one of the most widely used recommendation methods, it is independent of the characteristics of the item itself and belongs to the domain. In addition, it does not need to conduct modeling for users and products, and the recommendation results are open, which provides good technical support for discovering users’ own interests or preferences [18]. The main problem of recommendation based on collaborative filtering lies in its excessive dependence on historical data, which leads to its deficiencies in many related aspects, such as cold start, sparse data, insufficient accuracy, poor flexibility, and other problems. Table 2 compares the characteristics of the three methods, as shown in Table 2.Table 2
Characteristics summary.
Recommendations based on demographicsContent-based recommendationsRecommendations based on collaborative filteringAdvantagesEasy to implementDepend on lessNo cold start problemGrasp users’ taste accuratelyLow modeling requirementsNo machine understanding requiredHigh openness, good referenceDisadvantagesAccuracy is poorIgnoring users’ tastesSensitive information is difficult to obtainAccuracy depends on modeling comprehensivenessIgnore the user’s attitude towards the object cold start problem existsSparse matrix affects accuracyCold start problemIt is hard to update in real time


### 3.2. User-Based Collaborative Filtering Algorithm

User-based collaborative filtering algorithm (UserCF) was first proposed in 1992, which was used to realize mail filtering function in the early stage and is the most famous collaborative filtering algorithm in the field of recommendation system. The algorithm is mainly divided into two stages: firstly, mining the user set with similar preferences or needs and then looking for the corresponding content that the current user may like but does not know from the set and pushing it to the current user. The selection of algorithm similarity can be considered from different intersection degrees: the similarity between different users can be measured by the preference degree of a certain user for all items, or the similarity between different items can be measured by the preference degree of all users for a certain item [19]. Its basic principle is to use KNN (K-nearest neighbor) algorithm to find the group with similar preferences to the current user, “nearest neighbor,” and make recommendations to the current user by using the historical preference data of the nearest neighbor group.Suppose there arem users and n items in the user-item scoring matrix R. If user u gives a rating for item i, it can be replaced by the updated matrix R. If there is no rating, it can be replaced by empty. Each row in rni represents the rating vector of all users. Then, as for the similarity between the current user and other users about the scoring vector, cosine similarity is generally used to solve the problem, as shown in
(1)simu,v=Ru∙RvRuRv.Ru and Rv represent the scoring vectors of user u and user v, respectively. The item that the current user is not interested in before but similar users are interested in is designed as the candidate set, and the predicted score P of the current user u for item i in the candidate set is calculated as shown in
(2)Pu,i=∑simu,v∗rvi∑vesimu,v.The algorithm findsK similar items by giving the items that the current user u has scored, and the current user u gives prediction scores for these similar items, as shown in
(3)Pui=∑simj,i∗rvj∑vesimj,i.For example, news websites need to focus on the popularity and timeliness of news ontology recommendation. UserCF can push some news contents that are immediately followed by the “near neighbors” who share common interests with the target users, which can not only ensure instant access to hot information but also meet personalized needs in a certain sense. From a technical point of view, UserCF only needs to maintain the similarity table related to users, while ItemCF needs to constantly update the item correlation table, due to the rapid updating of information such as news, which brings some difficulties to technical implementation. Therefore, UserCF is obviously more suitable in this kind of application domain [20].In areas such as books and movies, ItemCF can maximize its features. Since the user interests of such websites are generally stable, and their main task is to provide and recommend items related to their interests to users, ltemCF algorithm is more suitable for the application and promotion of such websites. From a technical point of view, on the contrary to news websites, books and other websites need ItemCF more to maintain item similarity tables, rather than focusing on user similarity (see Table3).Table 3
Comparison of UserCF and ItemCF.
UserCFltemCFPerformanceApplicable to scenarios with a small number of users.It is very expensive to calculate the user similarity matrix when there are many users.Applicable to the scenario where the number of items is significantly smaller than the number of users, and it costs a lot to calculate the item similarity matrix when there are many items.FieldIt applies to areas with strong timeliness and less obvious user interest.It is suitable for the fields with rich long tail items and strong personalized needs of users.Real-time performanceNew user behavior does not necessarily result in immediate changes in recommendation results.New user behavior is bound to result in real-time changes in recommendation results.Cold startNew users cannot make personalized recommendations immediately after they have behaviors for a few items.By acting on an item, a new user can recommend other items related to that item.Model-based methods not only overcome the technical problem of high computational complexity of memory-based collaborative filtering recommendation by using matrix decomposition but also ensure a significant improvement in service efficiency and strengthen the scalability of the system itself [21]. The recommendation idea based on model is to transform the high-dimensional user-item rating matrix into the product of two different low-dimensional feature vector matrices by dimensionality reduction. The matrix decomposition can be defined as shown in
(4)R≅PQT.After decomposition,P∈R represents the user eigenmatrix, and f represents the dimension of the vector after dimensionality reduction. Line i in P is the eigenvector for user i. Q∈R represents the feature matrix of the item, and the jth row is the feature vector of the jth item. Through modeling and continuous iteration, matrices P and Q with the best learning effect are obtained to achieve the final score prediction. The definition of user u’s score prediction for item i is shown in
(5)rui=puqi.SVD (singular value decomposition) is a typical matrix decomposition technique. SVD was successfully applied in the field of latent semantic indexing in the early stage. Due to its excellent dimensionality reduction ability, Sarwar et al. introduced SVD into the recommendation algorithm to solve the problem of data sparsity and optimize the recommendation quality on the sparse score matrix [22]. In addition to being decomposed into two lower dimensional eigenvector matrices, SVD method can also generate diagonal matrix containing singular values in the recommendation system, which is convenient to find the internal relationship between users and projects. The size and quantity of singular values determine the variation range of dimension.Before decomposition using SVD. First, the missing term in matrixR is replaced by the average score to obtain a dense matrix. Let the matrix filled with blank items be represented by R′, then SVD decomposition of R′ is shown in
(6)R′=U∑VT.Since all singular values in the matrix contain information in the original matrix, SVD uses the sum of singular values to define the concept of information and sets thresholds to ensure the validity of information. If the sum of squares of all singular values isP and the sum of squares of the first f (f∈Z) singular values is P, then the threshold value of information content is shown in
(7)σ=PfP.In general,σ≥99% is required, and f usually takes the minimum integer value that satisfies the premise that a≥99%. Therefore, the diagonal matrix is a new diagonal matrix with only the first f singular values, and the first f singular value vectors are selected from U and V, respectively, to form Uf,Vf and matrix Rf through continuous dimensionality reduction, as shown in
(8)R′≅Rf.The latent factor model (LFM) was proposed in 2004. The main principle of this method lies in the close connection between users’ preferences and items by using the latent features. The latent factor model maps all users’ information to a dimensionf, that is, decompose the rating matrix R into a product of lower-dimensional matrices as shown in
(9)R¯=PTQ.In reality, there will be some inherent attributes of the user and the object that are not directly related to both. To address this problem, a novel improvement strategy is proposed in the Netflix Prize, which adds a bias term on the basis of the original SVD model, known as the BiasSVD model. The prediction formula after adding bias is shown in(10)r¯ui=PTuiqi+bi+bu+μ.


### 3.3. Personalized Collaborative Filtering Algorithm Based on Multiencryption Fusion

Recommendation system has always provided users with personalized suggestions based on users’ hobbies or needs. In the past ten years, it has received more and more attention and recognition, and now, it is a hot research field in the academic and industrial circles. Generally speaking, the body of recommendation policy-targeted resolution usually includes historical data of users, semantic content, and association between items. The basic idea of collaborative filtering methods commonly used in the field of recommendation systems is to make interest prediction or demand prediction by looking for similar users’ preferences or items other than the target users [23]. User rating information for items is the most common information used in collaborative filtering recommendations. Nowadays, social networking sites have become an indispensable part of the Web 2.0 environment. Due to the rapid development of social networking, the way people search for information, share resources, and communicate with each other is gradually changing. Social media sites update a huge amount of information every day, making it increasingly difficult for users to find what they really need or are interested in. In this case, rating data alone is not enough to accurately predict users’ interests and needs. Users can rate projects on social networking sites, add other users as friends, join online interest groups, and even tag projects. In addition to simple rating information, other available data such as social network data also indicate the preferences of specific users to a certain extent [24]. Therefore, in recent years, more and more scholars and engineers have begun to study whether specific data in the social network environment can help improve the quality of recommendation results. At the same time, it is also helpful to alleviate the problem of cold start.Although, currently, some researchers have integrated personalized information into recommendation systems and achieved good improvement in accuracy, few researchers have integrated several different types of information into the algorithm. For developers of recommendation systems, diversified information can help them understand users’ needs or preferences more accurately and improve the quality of service. Diversified information including basic rating information, social friend information, and tag information is integrated into UserCF algorithm, and two real data sets (Last.fm and Movielens) are used to evaluate the proposed method and then compared with some common collaborative filtering algorithms to test its actual effect.


## 3.1. Classification and Comparison of Recommendation Systems

Recommendation systems can be classified in different ways according to different analysis perspectives. Through sorting and analysis, the following three recommendation systems based on classification are mainly introduced:(1)
Different recommendation results are divided into:Based on the recommendation of mass behavior, popular items are pushed to all users by calculating the current popular items. It can push dynamic hot and concerned content to each user in real time, especially for items seeking promotion, which has a good practical effect. Its performance is relatively stable in popular movies, focus news, and other application fields, but the accuracy of recommendation results is not satisfactory [16].Personalised recommendations are based on user experience, based on data such as historical preferences. It mainly uses the historical data of users to analyze the habits and characteristics of different individuals or based on the needs of the current environment to achieve targeted services. It is characterized by giving full consideration to the differences of individual preferences of users, making appropriate judgments and pushing the results to users. It has good recommendation accuracy and is an ideal recommendation method. But the cost is high [17] (see Table 1).Table 1
Characteristics summary.
Recommendations based on crowd behaviorPersonalized recommendationAdvantagesHot information recommendation effect is goodGood accuracyDisadvantagesAccuracy is poorHigh input costCollaborative filter-based recommendation realizes recommendation by analyzing current users’ preferences and demands for items and information and calculating the similarity of items or users themselves. As one of the most widely used recommendation methods, it is independent of the characteristics of the item itself and belongs to the domain. In addition, it does not need to conduct modeling for users and products, and the recommendation results are open, which provides good technical support for discovering users’ own interests or preferences [18]. The main problem of recommendation based on collaborative filtering lies in its excessive dependence on historical data, which leads to its deficiencies in many related aspects, such as cold start, sparse data, insufficient accuracy, poor flexibility, and other problems. Table 2 compares the characteristics of the three methods, as shown in Table 2.Table 2
Characteristics summary.
Recommendations based on demographicsContent-based recommendationsRecommendations based on collaborative filteringAdvantagesEasy to implementDepend on lessNo cold start problemGrasp users’ taste accuratelyLow modeling requirementsNo machine understanding requiredHigh openness, good referenceDisadvantagesAccuracy is poorIgnoring users’ tastesSensitive information is difficult to obtainAccuracy depends on modeling comprehensivenessIgnore the user’s attitude towards the object cold start problem existsSparse matrix affects accuracyCold start problemIt is hard to update in real time


## 3.2. User-Based Collaborative Filtering Algorithm

User-based collaborative filtering algorithm (UserCF) was first proposed in 1992, which was used to realize mail filtering function in the early stage and is the most famous collaborative filtering algorithm in the field of recommendation system. The algorithm is mainly divided into two stages: firstly, mining the user set with similar preferences or needs and then looking for the corresponding content that the current user may like but does not know from the set and pushing it to the current user. The selection of algorithm similarity can be considered from different intersection degrees: the similarity between different users can be measured by the preference degree of a certain user for all items, or the similarity between different items can be measured by the preference degree of all users for a certain item [19]. Its basic principle is to use KNN (K-nearest neighbor) algorithm to find the group with similar preferences to the current user, “nearest neighbor,” and make recommendations to the current user by using the historical preference data of the nearest neighbor group.Suppose there arem users and n items in the user-item scoring matrix R. If user u gives a rating for item i, it can be replaced by the updated matrix R. If there is no rating, it can be replaced by empty. Each row in rni represents the rating vector of all users. Then, as for the similarity between the current user and other users about the scoring vector, cosine similarity is generally used to solve the problem, as shown in
(1)simu,v=Ru∙RvRuRv.Ru and Rv represent the scoring vectors of user u and user v, respectively. The item that the current user is not interested in before but similar users are interested in is designed as the candidate set, and the predicted score P of the current user u for item i in the candidate set is calculated as shown in
(2)Pu,i=∑simu,v∗rvi∑vesimu,v.The algorithm findsK similar items by giving the items that the current user u has scored, and the current user u gives prediction scores for these similar items, as shown in
(3)Pui=∑simj,i∗rvj∑vesimj,i.For example, news websites need to focus on the popularity and timeliness of news ontology recommendation. UserCF can push some news contents that are immediately followed by the “near neighbors” who share common interests with the target users, which can not only ensure instant access to hot information but also meet personalized needs in a certain sense. From a technical point of view, UserCF only needs to maintain the similarity table related to users, while ItemCF needs to constantly update the item correlation table, due to the rapid updating of information such as news, which brings some difficulties to technical implementation. Therefore, UserCF is obviously more suitable in this kind of application domain [20].In areas such as books and movies, ItemCF can maximize its features. Since the user interests of such websites are generally stable, and their main task is to provide and recommend items related to their interests to users, ltemCF algorithm is more suitable for the application and promotion of such websites. From a technical point of view, on the contrary to news websites, books and other websites need ItemCF more to maintain item similarity tables, rather than focusing on user similarity (see Table3).Table 3
Comparison of UserCF and ItemCF.
UserCFltemCFPerformanceApplicable to scenarios with a small number of users.It is very expensive to calculate the user similarity matrix when there are many users.Applicable to the scenario where the number of items is significantly smaller than the number of users, and it costs a lot to calculate the item similarity matrix when there are many items.FieldIt applies to areas with strong timeliness and less obvious user interest.It is suitable for the fields with rich long tail items and strong personalized needs of users.Real-time performanceNew user behavior does not necessarily result in immediate changes in recommendation results.New user behavior is bound to result in real-time changes in recommendation results.Cold startNew users cannot make personalized recommendations immediately after they have behaviors for a few items.By acting on an item, a new user can recommend other items related to that item.Model-based methods not only overcome the technical problem of high computational complexity of memory-based collaborative filtering recommendation by using matrix decomposition but also ensure a significant improvement in service efficiency and strengthen the scalability of the system itself [21]. The recommendation idea based on model is to transform the high-dimensional user-item rating matrix into the product of two different low-dimensional feature vector matrices by dimensionality reduction. The matrix decomposition can be defined as shown in
(4)R≅PQT.After decomposition,P∈R represents the user eigenmatrix, and f represents the dimension of the vector after dimensionality reduction. Line i in P is the eigenvector for user i. Q∈R represents the feature matrix of the item, and the jth row is the feature vector of the jth item. Through modeling and continuous iteration, matrices P and Q with the best learning effect are obtained to achieve the final score prediction. The definition of user u’s score prediction for item i is shown in
(5)rui=puqi.SVD (singular value decomposition) is a typical matrix decomposition technique. SVD was successfully applied in the field of latent semantic indexing in the early stage. Due to its excellent dimensionality reduction ability, Sarwar et al. introduced SVD into the recommendation algorithm to solve the problem of data sparsity and optimize the recommendation quality on the sparse score matrix [22]. In addition to being decomposed into two lower dimensional eigenvector matrices, SVD method can also generate diagonal matrix containing singular values in the recommendation system, which is convenient to find the internal relationship between users and projects. The size and quantity of singular values determine the variation range of dimension.Before decomposition using SVD. First, the missing term in matrixR is replaced by the average score to obtain a dense matrix. Let the matrix filled with blank items be represented by R′, then SVD decomposition of R′ is shown in
(6)R′=U∑VT.Since all singular values in the matrix contain information in the original matrix, SVD uses the sum of singular values to define the concept of information and sets thresholds to ensure the validity of information. If the sum of squares of all singular values isP and the sum of squares of the first f (f∈Z) singular values is P, then the threshold value of information content is shown in
(7)σ=PfP.In general,σ≥99% is required, and f usually takes the minimum integer value that satisfies the premise that a≥99%. Therefore, the diagonal matrix is a new diagonal matrix with only the first f singular values, and the first f singular value vectors are selected from U and V, respectively, to form Uf,Vf and matrix Rf through continuous dimensionality reduction, as shown in
(8)R′≅Rf.The latent factor model (LFM) was proposed in 2004. The main principle of this method lies in the close connection between users’ preferences and items by using the latent features. The latent factor model maps all users’ information to a dimensionf, that is, decompose the rating matrix R into a product of lower-dimensional matrices as shown in
(9)R¯=PTQ.In reality, there will be some inherent attributes of the user and the object that are not directly related to both. To address this problem, a novel improvement strategy is proposed in the Netflix Prize, which adds a bias term on the basis of the original SVD model, known as the BiasSVD model. The prediction formula after adding bias is shown in(10)r¯ui=PTuiqi+bi+bu+μ.


## 3.3. Personalized Collaborative Filtering Algorithm Based on Multiencryption Fusion

Recommendation system has always provided users with personalized suggestions based on users’ hobbies or needs. In the past ten years, it has received more and more attention and recognition, and now, it is a hot research field in the academic and industrial circles. Generally speaking, the body of recommendation policy-targeted resolution usually includes historical data of users, semantic content, and association between items. The basic idea of collaborative filtering methods commonly used in the field of recommendation systems is to make interest prediction or demand prediction by looking for similar users’ preferences or items other than the target users [23]. User rating information for items is the most common information used in collaborative filtering recommendations. Nowadays, social networking sites have become an indispensable part of the Web 2.0 environment. Due to the rapid development of social networking, the way people search for information, share resources, and communicate with each other is gradually changing. Social media sites update a huge amount of information every day, making it increasingly difficult for users to find what they really need or are interested in. In this case, rating data alone is not enough to accurately predict users’ interests and needs. Users can rate projects on social networking sites, add other users as friends, join online interest groups, and even tag projects. In addition to simple rating information, other available data such as social network data also indicate the preferences of specific users to a certain extent [24]. Therefore, in recent years, more and more scholars and engineers have begun to study whether specific data in the social network environment can help improve the quality of recommendation results. At the same time, it is also helpful to alleviate the problem of cold start.Although, currently, some researchers have integrated personalized information into recommendation systems and achieved good improvement in accuracy, few researchers have integrated several different types of information into the algorithm. For developers of recommendation systems, diversified information can help them understand users’ needs or preferences more accurately and improve the quality of service. Diversified information including basic rating information, social friend information, and tag information is integrated into UserCF algorithm, and two real data sets (Last.fm and Movielens) are used to evaluate the proposed method and then compared with some common collaborative filtering algorithms to test its actual effect.


## 4. Results and Analysis

Data mining is a process of knowledge discovery [25]. Data mining is a process of knowledge discovery. Specifically, according to the demand of data mining, the unified preprocessing of data is carried out to obtain a specific and unified data form convenient for mining. Mining potential and useful knowledge from these data is the function of data mining in decision support. Data mining can be simply described as a process of deep analysis of massive data, finding and summarizing potential rules, and transforming them into corresponding models. Data mining is essentially the technical processing of commercial information [26].Data mining can be divided into four stages: problem definition, data preprocessing, data mining, and pattern evaluation. (1) Problem definition is the process of determining requirements, determining which valuable information is in a large amount of data and how to find interesting data. Problem definition makes data mining more purposeful and meaningful. (2) The preprocessing of data sources is the extraction and purification of data to obtain the data needed for data mining. It can be divided into three steps: the first step is to select the data related to user needs from the mass data; the second step is to remove irrelevant formats in the data, that is, noise. The third step is to convert the extracted and purified data into a unified format suitable for data mining. (3) Data mining is to mine the information in the data that users are interested in through algorithms. According to different needs, the results of data mining are not the same. (4) Mode evaluation is an indispensable stage in the whole system. It can be used to judge whether data mining is valuable or needs to be remined. If data mining is found to be inconsistent with previously discovered knowledge or inconsistent with user requirements, mining again is required.In most cases, users are not very clear about their needs, and data mining can find a variety of different knowledge patterns at the same time to help them make decisions [27]. Data mining in the field of mobile Internet can improve market competitiveness, effectively reduce equipment and labor costs, and improve economic benefits to a large extent under the condition of meeting user needs and quality services. It has become a topic of universal concern for decision-makers.Most of the time, users have a very clear vision of their needs; data tracking in the area of time can be asked to present a variety of different knowledge models to help the decision. Data mining in the field of mobile Internet can improve market competitiveness, effectively reduce equipment and labor costs, and largely improve economic benefits under the condition of satisfying user needs and providing high-quality services, which has become a topic of general concern for decision-makers [25].Due to the need to collect trust relationships in social networks, typical data sets containing trust relationships in social networks include Epinions and FilmTrust. Epinions is a consumer rating site that allows users to rate their products and add people they trust to their trust lists. FilmTrust is a website that integrates social trust relationship and user rating, so that users can assign trust values to other users. The data set collected by the website contains user social trust data and user rating data. The offline comparison test adopts FilmTrust data set. According to the weight conversion formula, the traditional binary trust value (0,1) is converted (see Figure3).Figure 3
Trust data statistics.


## 5. Conclusion

With the rapid development and wide application of the Internet, generations of products of the Internet era have been bred. Social networks in the Web2.0 era are attracting the attention of the whole world. Social network has become an open platform and channel for information circulation. It quietly changes the way users use information and share it. The information overload caused by massive amounts of data is becoming a huge barrier to use. Recommendation system provides personalized suggestions based on users’ hobbies or behaviors, which has become one of the methods to solve this problem. Combined with the relevant features of social networks, the recommendation algorithm should be improved to improve the accuracy of the recommendation system, so as to provide users with high-quality personalized recommendation services and help solve the problem of information overload.In view of the information overload problem in the current Internet environment, this article makes some studies on data sparsity and cold start problems in the research of recommendation system and can alleviate them to some extent. Although there has been some improvement in the recommendation results, there are still a lot of work worth further study. Future research will continue to focus on the following issues:(1)
In real society, social network friend relationships are not necessarily established based on users’ interests. How to find user preferences more accurately is still a key issue(2)
The experiment is based on the data set in the offline environment. Once the algorithm goes online, it is bound to encounter many problems to be solved(3)
The data set selected in the experiment cannot completely cover the user characteristics in the whole Internet environment. How to obtain good recommendation results in the big data environment needs further study(4)
Users’ preferences in the real environment will change with the influence of objective and subjective factors such as time, so how to dynamically capture users’ interests needs further attention



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*Source: 2417826-2022-07-08.xml*