Personalized Book Recommendation Research Articles

Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering

This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and sad books is suggested for happy users to acknowledge the nuanced interplay of emotions. Neutral-themed books are proposed to users with a neutral mood for balance. The system employs an emotion classification algorithm for accurate mood detection, analyzing book summaries to extract emotional tones. Users' emotional states are evaluated using a Likert scale, adjusting recommendations accordingly. The methodology involves data collection, preprocessing, emotion classification, and mood meter operation. Results demonstrate the system's effectiveness in providing tailored book recommendations, enriching users' reading experiences.

Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot

In this paper, we study the personalized book recommender system in a child–robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children’s fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain-specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance, and it can operate on embedded consumer devices with limited computational resources.

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

Retracted: Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Retracted: Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Research on Personalized Book Recommendation Based on Improved Similarity Calculation and Data Filling Collaborative Filtering Algorithm.

(Purpose/Significance). This paper aims at the problems of inaccurate recommendation effect caused by data sparseness and cold start in the traditional collaborative filtering-based book personalized recommendation algorithm. So this paper proposes a collaborative filtering recommendation algorithm which improves the similarity solution method and the filling method of missing data. (Method/Process). By considering the influence of the user's common rating book collection on the similarity calculation, the average rating value of all books is used as the threshold, and the user's common rating weight is introduced into the user's similarity calculation. As for data filling, according to the user's average rating, the basic attributes such as the age and gender of users are coded, and then Euclidean distance is initially calculated, making hierarchical clustering on users. What's more, Shope-one algorithm is used to calculate the filling value of the former m similar users，and add the weight value of the degree simultaneously to get the final filling value, so as to improve the data filling method. (Result/Conclusion). Experiments were carried out with the data set of Book-Crossing Data set through Python. The experimental results show that the improved collaborative filtering algorithm has a significantly improvement in the accuracy and quality of book recommendation.

University Library Lending System Model Based on Fractional Differential Equations

Abstract How to find books suitable for them from the massive book information is a problem that needs to be considered at present for university library users. This paper proposes a personalized recommendation system for digital libraries utilizing fractional differential equations. At the same time, we use the idea of a collaborative filtering algorithm to recommend books for new users. Finally, we use the accurate data of the library to design a personalized book recommendation system for university libraries. The research shows that the university library lending system based on fractional differential equations has improved user experience.

Realization of Book Collaborative Filtering Personalized Recommendation System Based on Linear Regression Equation

Abstract In the context of information construction, faced with a large number of network information data, in order to obtain more valuable information in an effective time, researchers put forward a recommendation algorithm for information explosion. Because the recommendation algorithm has been successfully applied in many fields such as business, the relevant algorithm system has also begun to be fully implemented in the field of books. There are many kinds of recommendation algorithms, among which collaborative filtering recommendation technology is the most widely used. Therefore, based on the humanized library service concept, this paper mainly studies the influencing factors of reader satisfaction, and on the basis of constructing multiple linear regression model, clarifies the practical significance of constructing collaborative filtering personalized recommendation system for libraries. Then, an improved clustering algorithm is proposed to reduce the dimensionality of the original matrix, and an empirical analysis is made on the book collaborative filtering personalized recommendation system based on linear regression equation by using the operation idea of calculating the score according to the borrowing time. The final results show that, according to the multiple linear regression model between reader satisfaction and its influencing factors, in order to improve library service quality and build a good learning and reading environment, collaborative filtering algorithm should be combined to build a personalized book recommendation system, and this system is feasible in practical application..

Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Personalized recommendation is one of the important contents of personalized service in university libraries. Accurate and in-depth understanding of users is the premise of personalized recommendation. This paper proposes a personalized book recommendation algorithm based on deep learning models according to the characteristics and laws of user savings in university libraries. The method first uses the long short-term memory network (LSTM) to improve the deep autoencoder (DAE) so that the model can extract the temporal features of the data. Then, the Softmax function is used to obtain the book recommendation result of the current user. The proposed method is verified based on actual library lending data. The experimental results show that the proposed method has performance advantages compared with several existing recommendation methods.

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Digital library as one of the most important ways in helping students acquire professional knowledge and improve their professional level has gained great attention in recent years. However, its large collection (especially the book resources) hinders students from finding the resources that they are interested in. To overcome this challenge, many researchers have already turned to recommendation algorithms. Compared with traditional recommendation tasks, in the digital library, there are two challenges in book recommendation problems. The first is that users may borrow books that they are not interested in (i.e., noisy borrowing behaviours), such as borrowing books for classmates. The second is that the number of books in a digital library is usually very large, which means one student can only borrow a small set of books in history (i.e., data sparsity issue). As the noisy interactions in students’ borrowing sequences may harm the recommendation performance of a book recommender, we focus on refining recommendations via filtering out data noises. Moreover, due to the the lack of direct supervision information, we treat noise filtering in sequences as a decision-making process and innovatively introduce a reinforcement learning method as our recommendation framework. Furthermore, to overcome the sparsity issue of students’ borrowing behaviours, a clustering-based reinforcement learning algorithm is further developed. Experimental results on two real-world datasets demonstrate the superiority of our proposed method compared with several state-of-the-art recommendation methods.

A Study on the Development and Evaluation of Personalized Book Recommendation Systems in University Libraries Based on Individual Loan Records

A Study on the Development and Evaluation of Personalized Book Recommendation Systems in University Libraries Based on Individual Loan Records

College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm

When the number of books provided by library is relatively large, it becomes difficult for user to select appropriate book from a lot of candidate books. In this case, this paper designs a personalized recommendation system for college libraries based on hybrid recommendation algorithm. First of all, paper studies the application of collaborative filtering and content-based recommendation algorithm in the recommendation of university books, which involves reader classification, the establishment of user-item scoring matrix, the construction of vector space model and the calculation of similarity among users. And considering the characteristics of books and readers in universities, the user - item scoring matrix is improved, and clustering is used to alleviate the data sparsity problem. Do comparative experiments using the hybrid algorithm in data sets of Library of Inner Mongolia University of Technology. The results demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. Finally, the Spark big data platform combined with the hybrid recommendation algorithm is used to achieve the personalized book recommendation system design.

Personalized Book Recommendation Based on User Preferences and Commodity Features

Personalized Book Recommendation Based on User Preferences and Commodity Features

A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries

Book recommendations are of great significance in colleges and universities. Although current recommendation approaches have made significant achievements, these approaches do not consider college students’ similar learning trajectories in the same major. In order to recommend books more accurately, mining the knowledge system is very crucial for college students in the same major. This paper proposes a personalized book recommendation algorithm that is based on the time sequential collaborative filtering recommendation, combined with students’ learning trajectories. In order to recommend books effectively, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: the time sequence information of borrowing books and the circulation times of books. Our experimental results demonstrate that our book recommendation algorithm is in accordance with the college students’ demand for professional learning.

Personalized Book Recommendation Based on Ontology and Collaborative Filtering Algorithm

Information recommendation service is one of important functions of digital library, aiming at the problem that book recommendation service exists the insufficient requirement mining of service object information in the current uni- versity library, personalized book recommendation method based on ontology information and collaborative filtering al- gorithm (abbreviated as OI-CFA algorithm) is proposed. Firstly, this paper discusses the necessity of collaborative rec- ommendation in digital library, introduces main methods and technology based on collaborative filtering recommendation system. However, there are several problems that are data sparse and new item forecast with collaborative filtering rec- ommendation method based on item. In order to solve these problems, this paper introduced an integrated similarity algo- rithms of structured semantic information based on OI-CFA. Extracting the semantic information of items including knowledge representation based on ontology, through ontology learning, the specified domain ontology is constructed. Compared with the traditional collaborative filtering algorithm and SVM, experimental results show that this method can not only solve the problems caused by the item-based collaborative filtering algorithm, but also improve the accuracy of recommendation.

Personalized Book Recommendation Algorithm Based on Multi-interest Analysis in Library

Personalized Book Recommendation Algorithm Based on Multi-interest Analysis in Library

Research of Personalized Book Recommender System of University Library Based on Collaborative Filter

Research of Personalized Book Recommender System of University Library Based on Collaborative Filter

시맨틱웹 기반 개인 맞춤형 도서 추천 시스템

In this paper, we propose a semantic web approach for personalized book recommendation. Our approach takes advantage of the content-based recommendation and improves its disadvantage that users should input their interesting fields into all book search systems they use. Our approach provides the sharing of users' profile with their interesting fields by enabling user's interesting fields to be described over each book classification ontology of various book information providers. We also provide a middleware that manages users' profiles written in RDF and analizes similarity between user's interesting field and each concept over the book classification ontology. Our approach provide better performance than traditional keyword-based search by sharing the user's profile among book recommendation systems.

On the challenges of implementing Library 2.0 services: A (diverse) panel discussion

AbstractToday, many libraries are at a crossroads: several of the services they have traditionally provided within their walls are increasingly made available online, often by non‐library, commercial entities. For example, Web search engines provide easy access to millions of Websites, online databases provide convenient gateways to news and scholarship, and book‐scanning projects make roaming the shelves seem antiquated. Meanwhile, the traditional authority and expertise enjoyed by librarians has been challenged by the emergence of peer‐produced and collaborative knowledge projects, such as Wikipedia, Yahoo! Answers, or Amazon's personalized book recommendation system. Further, the professional, education, and social spheres of our lives are increasingly merging, marked by the rise of social networking services providing new interfaces for interacting with friends, the sharing of information, and professional collaboration.Libraries face a key question in this new environment: what is the role of the library in providing access to knowledge in today's digitally networked world? One answer has been to actively incorporate features of the online – “Web 2.0” – world into library services, thereby creating “Library 2.0.”Library 2.0 means bringing interactive, collaborative, user‐centered, and web‐based technologies to library services and collections. Launching such Library 2.0 features, however, often brings unique challenges to librarians, administrators, and patrons alike. These include: technological constraints, issues of accessibility and diversity, staffing and cost, IP/copyright concerns, privacy and data retention policies, and the overall impact on the nature and culture of the library itself.By bringing together a diverse collection of information professionals tasked with delivering Library 2.0 services, this panel discussion will outline many of the challenges of implementing Library 2.0, as well as illuminate some solutions and directions for the future. The goal of the panel is to help other information professionals navigate this uncharted terrain to foster the creation of innovative Library 2.0 services for their patrons, and reveal areas that require further attention by the profession and academia alike. This will be accomplished through guided conversation and sharing of ideas, not the traditional presentation of papers.

Using data mining to provide recommendation service

This research introduces personalized recommendation service into library services. Using the borrowing record of the library as basis, the association rules of data mining technique are used to look for book association by focusing on reader's borrowing mode, personal interest and trait in order to simplify the complexity of recommendation structure. The Bayesian network concept is used to build up a personalized book recommender system in order to generate different book recommendations, ranking from high to low, to help reader to locate book information most suitable to his requirement. Meanwhile we use user satisfaction questionnaire to understand the accuracy of recommended books and further to feedback information in order to help the post learning of Bayesian network parameter. This is for the perfection of the overall structure of recommender system so that readers could make use of the resource of the library more effectively and the value of the library system could be further improved.