• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1097-1104
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• 2011

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.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.127-152
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• 2021

The purpose of this study is to analyze the aspects of teacher librarians' book recommendation services for individual readers. For this purpose, data were collected through in-depth interviews with the teacher librarians of elementary school. Through the analysis of the collected data, the process of the teacher librarians' book recommendation, the main characteristics of the book recommendation, and the factors considering in the book recommendation, as well as information on major issues that arise in the book recommendation activity were derived and presented. Specifically, it was confirmed that the teacher librarians's book recommendation process was implemented in the following stages: questioning and interviewing, book recommendation, and follow-up. And, it was investigated that the factors considered when recommending books were students' interest, reading history, book fun, reading level, book level, teacher, class, and curriculum. In addition, it was confirmed that differences occurred in the experiences and perceptions of teacher librarians in the process of considering these factors. These results can provide the implications for resolving the problems of the teacher librarians who perform book recommendation services.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.1-22
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• 2022

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.305-314
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• 2002

In order to adapt to today's rapidly changing environment and gain a competitive advantage, many companies are interested in CRM(Customer Relationship Management). Especially, the product recommendation system that can be implemented by personalizing the marketing strategy becomes the focus of CRM. In this research, we employed CBR(Case-Based Reasoning) technique that can overcome the limitation of CF(Collaborative Filtering) technique. Our system recommends the books that the customer is very likely to buy next time considering the factors such as 'Personal Features of Customer,' Similarity between Book Categories' and 'Sequence of Book Purchases'. Accuracy of predicting a book-not a particular book, but in the middle level of classification that contains about 190 categories-was about 57%.

• Journal of Information Management
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• v.33 no.1
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• pp.1-17
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• 2002

This study examined several recommendation techniques to construct an effective book recommender system in a library. Experiments revealed that a hybrid recommendation technique is more effective than either collaborative filtering or content-based filtering technique in recommending books to be borrowed in an academic library setting. The recommendation technique based on association rule turned out the lowest in performance.

• Journal of Korean Library and Information Science Society
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• v.47 no.1
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• pp.279-303
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• 2016

This study proposes the Book-Curation service as part of the information service offered through school library websites. Also, this study aims to establish recommendation standards for curation prior to detailed system planning. For such service, the following tasks were carried out. First, the list of recommended books of existing systems were analyzed to identify the attributes that can be used for recommendation in the user and book information. Second, the analyzed attributes were utilized to establish 12 recommendation standards. Finally, a survey was carried out to identify the user preferences as to each standards. The results are as follows. First, the majority of students responded that curation service is necessary for using a library. Second, the top three standards are as follows: "best lending books based on the keywords of individual users"; "best lending books of the same year students"; "best lending books on the textbook-related reference booklist".

• Journal of Information Technology Services
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• v.14 no.1
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• pp.251-262
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• 2015

This study checks the conceptual definition of domestic book curation which is still in the beginning stage, the necessity of developing service and business, domestic and overseas case of relevant service. Further, the problem of book recommendation service and the difficulty anticipated in the embodiment of service are investigated together and the business model as new IT service is suggested to supplement them. Specifically, the collection of book information and customer information (interest and purchase pattern) and the procedure of mining the collected information and the process of embodying visualization was presented in the sector of service in the first place. Then, the technical transfer of developed solution and the construction cost and the method to impose commission over contents sales are presented in the sector of business. Diverse social and economic effects are expected to realize by developing and utilizing such services, namely, promoting the distribution of excellent book which were kept in dead storage so far due to lack of marketing support, recommendation readers the proper books which are convenient and necessary.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.113-127
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• 2021

The purpose of this study is to propose a personalized book recommendation system to promote the use of university libraries. In particular, unlike many recommended services that are based on existing users' preferences, this study proposes a method that derive evaluation metrics using individual users' book rental history and tendencies, which can be an effective alternative when users' preferences are not available. This study suggests models using two matrix decomposition methods: Singular Value Decomposition(SVD) and Stochastic Gradient Descent(SGD) that recommend books to users in a way that yields an expected preference score for books that have not yet been read by them. In addition, the model was implemented using a user-based collaborative filtering algorithm by referring to book rental history of other users that have high similarities with the target user. Finally, user evaluation was conducted for the three models using the derived evaluation metrics. Each of the three models recommended five books to users who can either accept or reject the recommendations as the way to evaluate the models.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.121-129
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• 2015

With the explosive growth of information about books, there is a growing number of customers who find it difficult to pick a book. Against the backdrop, the importance of a book recommendation system becomes greater, through which appropriate information about books could be offered then to encourage customers to buy a book in the end. However, existing recommendation systems based on the bibliographical information or user data reveal the reliability issue found in their recommendation results. This is why it is necessary to reflect semantic information extracted from the texts of a book's main body in a recommendation system. Accordingly, this paper suggests a method for extracting keywords from the main body of novels, as a preceding research, by using TF-IDF method as well as the text structure. To this end, the texts of 100 novels have been collected then to divide them into four structural elements of preface, dialogue, non-dialogue and closing. Then, the TF-IDF weight of each keyword has been calculated. The calculation results show that the extraction accuracy of keywords improves by 42.1% in performance when more weight is given to dialogue while including preface and closing instead of using just the main body.