# Personalized Recommendation System Design for Library Resources through Deep Belief Networks

Authors: Minyu Liu
Journal: Mobile Information Systems
 (2022)

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## Abstract

In recent years, with the continuous development of science and Internet technology, people’s lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users’ individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions.


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## Body

## 1. Introduction

In recent years, as Internet technology has penetrated into all aspects of daily life, people have moved from a stage of information scarcity to a stage of information overload. As a result, how to extract something of value from big data and massive amounts of information has been a hot research topic in recent years [1]. It is difficult for people to quickly obtain the resources they expect from the huge amount of data, which makes the use of information less efficient. In order to solve such problems, it is necessary to recommend information or products that are of interest to users from various aspects such as their information needs and interests [2]. As a branch of data mining, recommendation systems have developed rapidly in recent years and are used in various fields, such as e-commerce [3, 4], music [5], and movies [6]. Personalized recommendation technology is based on collecting and analyzing users’ historical information to build a user interest model and personalized active recommendations for different users. What is more, personalized recommendation technology can significantly improve access efficiency and service quality and by creating a user-centered personalized recommendation experience. In addition, it can effectively increase the frequency of users’ use of the system [7]. As a result, personalized recommendation systems are considered to be the most effective way to solve digital information overload. At the same time, with the rapid development of intelligent and semantic technologies [8, 9], the development of systems is increasingly focused on the habits and different preferences of users in specific environments and thus provides personalized value-added services. Therefore, personalized recommendations based on differentiated user needs are becoming an inevitable trend for the further development of digital libraries [10].The essence of a recommendation system is to associate the effective information extracted from a large amount of information with the user’s information, so as to save the user’s time in searching for information and thus increase the value of using information. The technical core part of recommendation system research lies in its recommendation capability, such as the use of suitable recommendation methods to meet different user interests and information needs, which can recommend the information resources that users need [11]. The corresponding information provider can accurately access the interests of different users, which could facilitate the stabilization of the customer base and control of industry trends, thus achieving a win-win situation for all parties. The advantage of recommendation systems over search engines and classified directories is that they use an active search strategy [12]. Specifically, a recommendation system can analyze the changing interests and needs of users by acquiring a large amount of historical behavioral data from them and provide them with the most interesting needs when they need to access information resources. This active recommendation strategy greatly solves the challenge of users not being able to accurately describe their needs when using search engines. Recommendation algorithms are used to predict user ratings for unseen resources based on basic information such as user ratings and reviews and to recommend the results to the target user [13]. However, information about the user is important for the recommendation system to consider when calculating recommendations for the target user, such as the user’s age, occupation, and user activity, and this information can also influence the effectiveness of the recommendations.Common recommendation algorithms are content-based recommendations [14], user statistics-based recommendations [15], utility-based recommendations [16], and association rule-based recommendations [17]. Kluver et al. proposed a new collaborative filtering algorithm based on item rating prediction, which addresses the problem that traditional similarity measures are low for user rating data, causing significant quality degradation in recommender systems, and applied a new collaborative filtering recommendation similarity metric in order to find the relationship between targets and users [18]. Seo et al. developed a personalized recommendation algorithm based on social network content to solve the problem of how to obtain information of interest quickly and efficiently to users from large amounts of data [19]. Padillo et al. introduced an efficient algorithm for mining association rules, which focuses on mining association rules between items in a large database of sales transactions [20]. The association rules can apply to large databases and complex products such as financial services, technical equipment, or consumer goods, where it is particularly important that the recommendation system is able to present the most appropriate product recommendations to the user [21]. Gyrard and Sheth proposed a knowledge-based recommendation technology that can analyze different types of product preferences and purchase behavior [22]. In addition, this technology allows knowledge-based recommendation systems to formalize user behavior and enhance their recommendation capabilities, allowing users to invisibly experience products and services of interest to them in terms of their purchasing power. In summary, the above recommendation algorithms can be seen in Table 1.Table 1
Comparison among different recommendation algorithms.
Recommendation algorithmAdvantageDisadvantageContent-basedThe recommended results are intuitive and easy to interpretComplex attributes of the user are difficult to handleUser statistics-basedNo new user issuesDemographic information on users is difficult to obtainUtility-basedMore sensitive to changes in user preferencesPoor flexibilityAssociation rule-basedEasy to find new points of interest for usersDifficult and time-consuming to extract association rulesTechnology and the widespread use of the internet of things are influencing and changing the way people live, especially with the development of information technology, which has contributed to the transformation of mobile libraries [23]. With the widespread use of smart devices, users are aspiring to be intelligent in their information needs as well as enjoying commercial services. Libraries, as information service providers, are expected to combine technology and resources to create new opportunities for mobile library services [24]. In the context of digital libraries, traditional library information services can no longer meet the needs of users. With the development of digital technology, users’ access to information is not limited to reading paper-based documents. However, due to the information literacy of users, it is difficult for them to meet their information needs from the vast amount of information available [25]. This requires libraries to provide proactive information services to users. Traditional interest-based recommendations based on users’ historical behavior are being widely used in digital libraries, but there are still limitations to the recommendations. The introduction of personalized recommendation technology in digital libraries can lead to personalized recommendation services [26]. Users’ interests are modelled according to their preferences and habits, and their differentiated needs are met through interest analysis and recommendations. The application of personalized recommendation technology in digital libraries can meet the needs of users as accurately as possible and in a timely manner, making full use of the vast amount of information storage and resources in the collection [27]. However, traditional personalized recommendations are based on keywords and transactional databases, and there are a number of problems. Users need to express their requests more accurately in order to get good recommendations. In addition, it is difficult to match resource descriptions with user interests, and there is a lack of semantic reasoning and flexible personalization of recommendations. The idea of personalized service has been applied to all aspects of life, from shopping to travel. The massive resources of digital libraries and the personalized information needs of users present a challenge to personalized library services [28]. The ubiquitous nature of library information services emphasizes the provision of ubiquitous personalized information services to users. As a result, the analysis of user scenarios and the construction of personalized recommendation models for users are in line with the characteristics of personalized information services in digital libraries [29]. What is more, it can meet the personalized information needs of digital library users.In the research on resource recommendation in digital libraries, the current approach to resource recommendation is relatively homogeneous. This is inconsistent with the advancement of technology and lacks the application of new technologies and integration with the field of artificial intelligence [30]. This study will use deep belief networks, which will not only enrich the research on digital libraries but also open up new ideas on how libraries can provide services. Compared to traditional mobile library services based on user needs and preferences, the deep belief network-based library resource recommendation service improves the frequency and delivery of library services by increasing the proactivity of library services. Most importantly, this approach enables libraries to change from passive service requests to active scenario-based service delivery. On the one hand, it meets the diverse information needs of users with intelligent mobile terminals. On the other hand, it highlights the personalized characteristics of digital library information services. This study establishes a personalized recommendation system for library resources based on deep belief networks, which expands the theoretical system of knowledge services and provides a new theoretical growth point for knowledge services.


## 2. Personalized Recommendation System

### 2.1. Introduction of the Recommendation System

The basic task of a recommendation system is to connect users and goods, thus solving the problem of information overload. The main goal of a recommendation system is to help users find information of interest to them in a large amount of data, thus linking users with relevant information. Figure1 represents the process of a recommendation system linking users and large amounts of information.Figure 1
Process of a recommendation system linking users and information data.With the development and application of Internet technology, the number of information resources on the web has increased dramatically. As a result, users have to spend a lot of money to find the resources they are interested in, which leads to the problem of information overload. Traditional information-based search engines fail to consider the individual characteristics of users. They equate all user characteristics, return the same recommendations for different users, and have a large number of recommendations, thus not solving the fundamental problem of user information overload. The problem of information overload cannot be solved at all. As a result, it can be seen that it is an urgent problem in the field of information services to provide users with recommendations based on their individual preferences and in the form of active push.By analyzing various traditional personalized recommendation systems, the framework for a generic recommendation model can be constructed (Figure2). The main difference between information recommendation and information retrieval platforms is the information service model and working mechanism. Specifically, recommendation systems are based on an information push service model, which is user-centered and does not require users to actively describe the resources they are interested in. Information retrieval systems are based on an information extraction service model, which is information resource-centered and user-led in the information retrieval process. When the search results cannot meet the needs, the user is required to modify the keywords and query again.Figure 2
Generic recommendation model.


### 2.2. Context Awareness in the Recommendation System

User scenario-based information services are the main service approach for library information services, and therefore, the information needs of users should be fully considered. Information needs based on user scenarios refer to the sense of lack and dissatisfaction of mobile library users in order to achieve a goal in a specific scenario. In other words, the library information needs based on user scenarios are based on artificial intelligence. In contrast to traditional library information services, the user’s information needs are the result of AI analysis and the basis for library information services. The situational awareness-based recommendation system is a personalized information service that is provided to the user in the course of their normal life, without their request. On the one hand, the user can interact with the context-aware system and provide contextual information. On the other hand, the system recommends the information to the user. When the system recommends information to the user, at the macro level, the information recommended by the system changes the information structure of the user. At the micro level, the user’s information behavior will change at any time as the recommended information is displayed on the user’s mobile device.The essence of library users receiving context-based information recommendations is the matching of user context with mobile library resources, with context becoming an important basis for recommendations in addition to user history. The process of users receiving scenario-based information recommendations involves two aspects (Figure3). Firstly, the user is required to provide contextual information. The user profile, mobile profile, technology profile, service profile, and resource profile are the basis for the recommendation of mobile library resources based on the profile and the basis for the user to receive information back to the system. Secondly, by matching the scenarios provided by users with mobile library resources, mobile library resources are recommended to users in the current scenario to meet their information needs and information acceptance preferences. At the same time, the scenarios of users’ acceptance of library resources are fed back to the recommendation system to update the preferences based on users’ scenarios and improve the system’s self-adaptability. The essence of user profile-based information recommendation is that it is achieved by matching user profile information with resources. As a result, the quality of the user profile determines the ease of resource recommendation and the quality of the resource recommendation service.Figure 3
Process of users receiving scenario-based information recommendations.


### 2.3. Recommended System Evaluation Indicators

Recommendation accuracy is a measure of how well a recommendation system fits a user’s behavior and describes whether the recommendation algorithm accurately predicts the user’s interests and preferences. This metric can be calculated through offline experiments and is relatively easy to obtain; hence, it has received a lot of attention from academic researchers. Prediction accuracy is calculated in offline experiments by dividing the offline data set into different data sets, including a training set, a test set, and a validation set. User behavior is fitted by constructing a recommendation algorithm model, training the algorithm model with the training set, and then bringing it into the test set to generate recommendation results. Finally, the overlap between the generated recommendation results and the validation set is used as the prediction accuracy of the recommendation algorithm.TopN recommendation’s prediction accuracy is measured in terms of recall and accuracy rates. The recall rate can be expressed as follows:(1)R=∑m∈MPm∩​Sm∑m∈MSm,where R refers to the recall rate, Pm refers to the set of prediction results, and Sm indicates the validation set.In addition, the accuracy rate is defined as follows:(2)A=∑m∈MPm∩​Sm∑m∈MPm,where A refers to the accuracy rate.In order to provide a more comprehensive and objective review of TopN recommendations, different sets of recommendation results are usually set. The accuracy and recall of these result sets are then calculated separately. Accuracy and recall are by definition relatively contradictory quantities that are inversely proportional to each other. That is, as accuracy increases, recall decreases. Therefore, in order to balance these two metrics, the algorithm prediction accuracy is generally described using a composite evaluation metric,x, which is defined as follows:(3)X=2×R×AR+A.


### 2.4. User Behavior Data Analysis

The key to a recommendation system is the user data, which needs to be analyzed in depth to grasp the explicit patterns in the data in order to further explore the implicit information in the data. The user behavior data in a recommendation system is an important piece of research data, which includes information about users and products, divided into explicit and implicit feedback, which indicates the user’s preference for different products or services. By analyzing this information, a systematic grasp of the user’s changing behavior and interest preferences can be obtained, which will facilitate the construction of personalized recommendation systems.The opposite of explicit feedback is implicit feedback, which is information that does not explicitly reflect the different preferences of users through the information available. It is more common to find implicit feedback that can be explored, such as the number of times a user browses or visits a site, which may indicate user preferences. Implicit feedback information is more voluminous than explicit feedback information and is not as easy to obtain and is obtained by analyzing and mining large amounts of data. However, this information is more accurate than explicit feedback in describing the user’s interest preferences, which cannot even be uncovered by the user himself, which is the unconscious behavior revealing the user’s preferences. Table2 compares explicit and implicit feedback information from different perspectives.Table 2
Comparison between implicit and explicit feedback information.
Implicit feedbackExplicit feedbackUser’s preferenceClearlyNot clearlyData volumeSmallLargePositive or negative feedbackBoth feedbackPositive feedbackReal time of dataReal timeRetardance


## 2.1. Introduction of the Recommendation System

The basic task of a recommendation system is to connect users and goods, thus solving the problem of information overload. The main goal of a recommendation system is to help users find information of interest to them in a large amount of data, thus linking users with relevant information. Figure1 represents the process of a recommendation system linking users and large amounts of information.Figure 1
Process of a recommendation system linking users and information data.With the development and application of Internet technology, the number of information resources on the web has increased dramatically. As a result, users have to spend a lot of money to find the resources they are interested in, which leads to the problem of information overload. Traditional information-based search engines fail to consider the individual characteristics of users. They equate all user characteristics, return the same recommendations for different users, and have a large number of recommendations, thus not solving the fundamental problem of user information overload. The problem of information overload cannot be solved at all. As a result, it can be seen that it is an urgent problem in the field of information services to provide users with recommendations based on their individual preferences and in the form of active push.By analyzing various traditional personalized recommendation systems, the framework for a generic recommendation model can be constructed (Figure2). The main difference between information recommendation and information retrieval platforms is the information service model and working mechanism. Specifically, recommendation systems are based on an information push service model, which is user-centered and does not require users to actively describe the resources they are interested in. Information retrieval systems are based on an information extraction service model, which is information resource-centered and user-led in the information retrieval process. When the search results cannot meet the needs, the user is required to modify the keywords and query again.Figure 2
Generic recommendation model.


## 2.2. Context Awareness in the Recommendation System

User scenario-based information services are the main service approach for library information services, and therefore, the information needs of users should be fully considered. Information needs based on user scenarios refer to the sense of lack and dissatisfaction of mobile library users in order to achieve a goal in a specific scenario. In other words, the library information needs based on user scenarios are based on artificial intelligence. In contrast to traditional library information services, the user’s information needs are the result of AI analysis and the basis for library information services. The situational awareness-based recommendation system is a personalized information service that is provided to the user in the course of their normal life, without their request. On the one hand, the user can interact with the context-aware system and provide contextual information. On the other hand, the system recommends the information to the user. When the system recommends information to the user, at the macro level, the information recommended by the system changes the information structure of the user. At the micro level, the user’s information behavior will change at any time as the recommended information is displayed on the user’s mobile device.The essence of library users receiving context-based information recommendations is the matching of user context with mobile library resources, with context becoming an important basis for recommendations in addition to user history. The process of users receiving scenario-based information recommendations involves two aspects (Figure3). Firstly, the user is required to provide contextual information. The user profile, mobile profile, technology profile, service profile, and resource profile are the basis for the recommendation of mobile library resources based on the profile and the basis for the user to receive information back to the system. Secondly, by matching the scenarios provided by users with mobile library resources, mobile library resources are recommended to users in the current scenario to meet their information needs and information acceptance preferences. At the same time, the scenarios of users’ acceptance of library resources are fed back to the recommendation system to update the preferences based on users’ scenarios and improve the system’s self-adaptability. The essence of user profile-based information recommendation is that it is achieved by matching user profile information with resources. As a result, the quality of the user profile determines the ease of resource recommendation and the quality of the resource recommendation service.Figure 3
Process of users receiving scenario-based information recommendations.


## 2.3. Recommended System Evaluation Indicators

Recommendation accuracy is a measure of how well a recommendation system fits a user’s behavior and describes whether the recommendation algorithm accurately predicts the user’s interests and preferences. This metric can be calculated through offline experiments and is relatively easy to obtain; hence, it has received a lot of attention from academic researchers. Prediction accuracy is calculated in offline experiments by dividing the offline data set into different data sets, including a training set, a test set, and a validation set. User behavior is fitted by constructing a recommendation algorithm model, training the algorithm model with the training set, and then bringing it into the test set to generate recommendation results. Finally, the overlap between the generated recommendation results and the validation set is used as the prediction accuracy of the recommendation algorithm.TopN recommendation’s prediction accuracy is measured in terms of recall and accuracy rates. The recall rate can be expressed as follows:(1)R=∑m∈MPm∩​Sm∑m∈MSm,where R refers to the recall rate, Pm refers to the set of prediction results, and Sm indicates the validation set.In addition, the accuracy rate is defined as follows:(2)A=∑m∈MPm∩​Sm∑m∈MPm,where A refers to the accuracy rate.In order to provide a more comprehensive and objective review of TopN recommendations, different sets of recommendation results are usually set. The accuracy and recall of these result sets are then calculated separately. Accuracy and recall are by definition relatively contradictory quantities that are inversely proportional to each other. That is, as accuracy increases, recall decreases. Therefore, in order to balance these two metrics, the algorithm prediction accuracy is generally described using a composite evaluation metric,x, which is defined as follows:(3)X=2×R×AR+A.


## 2.4. User Behavior Data Analysis

The key to a recommendation system is the user data, which needs to be analyzed in depth to grasp the explicit patterns in the data in order to further explore the implicit information in the data. The user behavior data in a recommendation system is an important piece of research data, which includes information about users and products, divided into explicit and implicit feedback, which indicates the user’s preference for different products or services. By analyzing this information, a systematic grasp of the user’s changing behavior and interest preferences can be obtained, which will facilitate the construction of personalized recommendation systems.The opposite of explicit feedback is implicit feedback, which is information that does not explicitly reflect the different preferences of users through the information available. It is more common to find implicit feedback that can be explored, such as the number of times a user browses or visits a site, which may indicate user preferences. Implicit feedback information is more voluminous than explicit feedback information and is not as easy to obtain and is obtained by analyzing and mining large amounts of data. However, this information is more accurate than explicit feedback in describing the user’s interest preferences, which cannot even be uncovered by the user himself, which is the unconscious behavior revealing the user’s preferences. Table2 compares explicit and implicit feedback information from different perspectives.Table 2
Comparison between implicit and explicit feedback information.
Implicit feedbackExplicit feedbackUser’s preferenceClearlyNot clearlyData volumeSmallLargePositive or negative feedbackBoth feedbackPositive feedbackReal time of dataReal timeRetardance


## 3. Recommendation System Based on Deep Belief Networks

Deep neural networks are neural networks with multiple layers of structure in terms of structural hierarchy. Deep belief networks are a typical representative of these.


### 3.1. Boltzmann Machine

The Boltzmann machine is a stochastic recurrent neural network structure, and the model is also an energy-based network structure. Figure4 shows the structure of a Boltzmann machine. It can be seen that it is a network structure consisting of two sets of neurons, all of which communicate with each other and each of which has two states, off and on, denoted by 0 and 1, respectively. In this structure, the visible layer can be seen as the data receiver and the hidden layer as the feature extractor.Figure 4
Structure of a Boltzmann machine.The structural difference between a semiconstrained Boltzmann machine (Figure5) and a Boltzmann machine is that it does not have the connections between the hidden layer neurons. The advantage of this structure is that it saves a lot of training time. However, since the neurons in the visible layers are still interconnected, it still takes a lot of time to solve large data and is not very useful in practice.Figure 5
Structure of a semiconstrained Boltzmann machine.


### 3.2. Constrained Boltzmann Machine

Compared to the semiconstrained Boltzmann machine and Boltzmann machine, the constrained Boltzmann machine is simpler and more efficient. As can be seen in Figure6, the connections between the neurons in the visible layer are also removed, and this structure has better computational properties. The state of the neurons in the hidden layer can be easily obtained if the state of the neurons in the visible layer is known because they are independent of each other and do not affect each other. Similarly, given the states of the neurons in the hidden layer, the states of the neurons in the visible layer are also independent of each other, which makes the computation of the data very convenient.Figure 6
Structure of a constrained Boltzmann machine.The constrained Boltzmann machine model is an energy-based neural net structure, and therefore, its energy state is as follows:(4)Ew,k∣α=−∑i=1nxi×wi−∑j=1myj×kj−∑i=1n∑j=1mwi×zij×kj,where wi is the state of the i-th visible unit, kj is the state of the j-th hidden unit, zij refers to the connection weight between the visible unit i and the hidden unit j, xi refers to the internal biasing of neurons in the visual layer, and yi refers to the internal biasing of neurons in the hidden layer.After that, the joint distribution can be expressed as follows:(5)Pw,k|α=expEw,k∣αYα,where Yα refers to the normalized function.In summary, the framework of the deep belief network can be seen in Figure7.Figure 7
Framework of deep belief network.


### 3.3. Learning Process of Deep Belief Network

The greatest strength of deep belief networks is the ability to learn features, achieved through a layer-by-layer learning strategy, where features at higher levels are learned from features at lower levels. The higher-level features are features that are more abstract in terms of higher differences between elements of lower dimensionality taken from the higher dimensional complex lower-level input elements but also better reflect the information contained in the input data structure. Therefore, if the dimensionality of the input data features is high, this learning feature of the deep belief network enables it to greatly improve the efficiency of model training and the generalization ability. Diversification from the perspective of the data can demonstrate that adding a feature layer structure can improve the lower bound on the log probability of the training data. To be specific, Figure8 describes the two different training processes of deep belief networks.Figure 8
Different training processes of deep belief networks.In order to ensure the performance of library services and to provide rich material support for libraries, it is necessary to optimize the personalized services of mobile libraries in the context of resources. In the process of optimizing personalized services in mobile libraries, the optimization of personalized services in resource scenarios can be achieved by improving the quality of resources, integrating the breadth and depth of resources, and visualizing resources. In building resource scenarios for mobile libraries, resources are integrated from a disordered state to an ordered state, and resources are optimized. This process is also a progressive process of optimizing the personalized service in the resource scenario, in which the efficiency of the service to the user is increased and the value of the resource is realized. The format of the information content is the driving force behind the personalization of the resource scenario, the core source of value for the personalization of the resource scenario, and the goal of the personalization of the service.


## 3.1. Boltzmann Machine

The Boltzmann machine is a stochastic recurrent neural network structure, and the model is also an energy-based network structure. Figure4 shows the structure of a Boltzmann machine. It can be seen that it is a network structure consisting of two sets of neurons, all of which communicate with each other and each of which has two states, off and on, denoted by 0 and 1, respectively. In this structure, the visible layer can be seen as the data receiver and the hidden layer as the feature extractor.Figure 4
Structure of a Boltzmann machine.The structural difference between a semiconstrained Boltzmann machine (Figure5) and a Boltzmann machine is that it does not have the connections between the hidden layer neurons. The advantage of this structure is that it saves a lot of training time. However, since the neurons in the visible layers are still interconnected, it still takes a lot of time to solve large data and is not very useful in practice.Figure 5
Structure of a semiconstrained Boltzmann machine.


## 3.2. Constrained Boltzmann Machine

Compared to the semiconstrained Boltzmann machine and Boltzmann machine, the constrained Boltzmann machine is simpler and more efficient. As can be seen in Figure6, the connections between the neurons in the visible layer are also removed, and this structure has better computational properties. The state of the neurons in the hidden layer can be easily obtained if the state of the neurons in the visible layer is known because they are independent of each other and do not affect each other. Similarly, given the states of the neurons in the hidden layer, the states of the neurons in the visible layer are also independent of each other, which makes the computation of the data very convenient.Figure 6
Structure of a constrained Boltzmann machine.The constrained Boltzmann machine model is an energy-based neural net structure, and therefore, its energy state is as follows:(4)Ew,k∣α=−∑i=1nxi×wi−∑j=1myj×kj−∑i=1n∑j=1mwi×zij×kj,where wi is the state of the i-th visible unit, kj is the state of the j-th hidden unit, zij refers to the connection weight between the visible unit i and the hidden unit j, xi refers to the internal biasing of neurons in the visual layer, and yi refers to the internal biasing of neurons in the hidden layer.After that, the joint distribution can be expressed as follows:(5)Pw,k|α=expEw,k∣αYα,where Yα refers to the normalized function.In summary, the framework of the deep belief network can be seen in Figure7.Figure 7
Framework of deep belief network.


## 3.3. Learning Process of Deep Belief Network

The greatest strength of deep belief networks is the ability to learn features, achieved through a layer-by-layer learning strategy, where features at higher levels are learned from features at lower levels. The higher-level features are features that are more abstract in terms of higher differences between elements of lower dimensionality taken from the higher dimensional complex lower-level input elements but also better reflect the information contained in the input data structure. Therefore, if the dimensionality of the input data features is high, this learning feature of the deep belief network enables it to greatly improve the efficiency of model training and the generalization ability. Diversification from the perspective of the data can demonstrate that adding a feature layer structure can improve the lower bound on the log probability of the training data. To be specific, Figure8 describes the two different training processes of deep belief networks.Figure 8
Different training processes of deep belief networks.In order to ensure the performance of library services and to provide rich material support for libraries, it is necessary to optimize the personalized services of mobile libraries in the context of resources. In the process of optimizing personalized services in mobile libraries, the optimization of personalized services in resource scenarios can be achieved by improving the quality of resources, integrating the breadth and depth of resources, and visualizing resources. In building resource scenarios for mobile libraries, resources are integrated from a disordered state to an ordered state, and resources are optimized. This process is also a progressive process of optimizing the personalized service in the resource scenario, in which the efficiency of the service to the user is increased and the value of the resource is realized. The format of the information content is the driving force behind the personalization of the resource scenario, the core source of value for the personalization of the resource scenario, and the goal of the personalization of the service.


## 4. Conclusion

The study of deep belief networks and recommender systems has been a hot research topic in related fields. How to effectively combine these two techniques to facilitate deep belief network technology while significantly improving the efficiency and performance of recommender systems is a problem that researchers have been working on. This paper firstly analyses the characteristics of libraries and sorts out the development changes as well as the characteristics of digital libraries. Secondly, it analyses the current situation of the application of deep belief networks in the environment of big data and the internet of things and applies the recommendation system to digital libraries. Based on the analysis of the current personalized recommendation services in libraries based on deep belief networks, the factors influencing the acceptance of users of personalized recommendation services in libraries are obtained. In summary, this research uses deep belief network algorithms to construct digital library scenarios based on the construction of a personalized recommendation system for library resources. The ontology similarity calculation method is then used to calculate the scenario ontology from the perspective of users’ information needs, which is used as the basis for personalized service recommendations, and a personalized service recommendation algorithm is derived based on the library. However, the division of library scenario dimensions is not static. In light of the development of libraries and the development of information technology, further research is needed on the division of library scenarios. The effective delineation of library scenarios, an accurate understanding of the scope of library scenarios, and a thorough study of the various elements of library scenarios are the basis for the perception of library user scenarios and personalized recommendation services.



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*Source: 7870724-2022-07-08.xml*