Developing a novel recommender network-based ranking mechanism for library book acquisition

Wu, Fan; Hu, Ya-Han; Wang, Ping Rong

doi:10.1108/el-06-2015-0094

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…29 Wu et al have introduced a library book acquisition recommender system that employs a network ranking mechanism. 30 Cabrerizo et al suggest an extension to the LibQUAL+ model to address users' perceptions and evaluate the quality of library services. [31][32] Some researchers use linked information spaces for different scientific digital libraries in Digital Humanities.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid recommender system model for digital library from multiple online publishers

Jomsri,

Prangchumpol,

Poonsilp

et al. 2024

F1000Res

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Background The demand for online education promotion platforms has increased. In addition, the digital library system is one of the many systems that support teaching and learning. However, most digital library systems store books in the form of libraries that were developed or purchased exclusively by the library, without connecting data with different agencies in the same system. Methods A hybrid recommender system model for digital libraries, developed from multiple online publishers, has created a prototype digital library system that connects various important knowledge sources from multiple digital libraries and online publishers to create an index and recommend e-books. The developed system utilizes an API-based linking process to connect various important sources of knowledge from multiple data sources such as e-books on education from educational institutions, e-books from government agencies, and e-books from religious organizations are stored separately. Then, a hybrid recommender system suitable for users was developed using Collaborative Filtering (CF) model together with Content-Based Filtering. This research purposed the hybrid recommender system model, which took into account the factors of book category, reading habits of users, and sources of information. The evaluation of the experiments involved soliciting feedback from system users and comparing the results with conventional recommendation methods. Results A comparison of NDCG scores was conducted for Hybrid Score 50:50, Hybrid Score 20:80, Hybrid Score 80:20, CF-score and CB-score. The experimental result was found that the Hybrid Score 80:20 method had the highest average NDCG score. Conclusions Using a hybrid recommender system model that combines 80% Collaborative Filtering and 20% Content-Based Filtering can improve the recommender method, leading to better referral efficiency and greater overall efficiency compared to traditional approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid recommender system model for digital library from multiple online publishers

Jomsri,

Prangchumpol,

Poonsilp

et al. 2024

F1000Res

View full text Add to dashboard Cite

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“…In the past, recommendation techniques have been used in a wide variety of applications to improve searching effectiveness. Examples include books (Wu et al, 2017), personized item (Lai and Zeng, 2013), keywords (Su et al, 2010), annotation (Chen and Tsay, 2017), and so on. Among so many, citation recommendation is also a typical application of recommender systems.…”

Section: Literature Reviewmentioning

confidence: 99%

An innovative citation recommendation model for draft papers with varying degrees of information completeness

Chen

Weng

Huang

et al. 2019

DTA

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Purpose As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation. Design/methodology/approach This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance. Findings The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy. Originality/value None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.

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“…Mining circulation records using association rules is also often used in book recommendation services (Ping, 2015). Wu et al (2017) employed social computing techniques on a recommender network to develop such a recommendation system.…”

Section: Library Usage Mining – Literature Reviewmentioning

confidence: 99%

Library usage mining in the context of alternative costs

Hájek

Stejskal

2017

LHT

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Purpose The behaviour of a library user is based on his ability to evaluate the consumption of library services based on alternative market services. The purpose of this paper is to study the usage behaviour in a public library building in the context of alternative costs instead of a narrow focus on book circulation data. Design/methodology/approach By library usage mining, using associative rules, the authors described the behaviour of library users and identified the typical behaviour during the visits. The authors analyse the results in the context of alternative costs assigned to the visits. Findings The results confirm that some underused services, such as digital services, deliver significantly greater benefit. The frequency of use, the duration of visit and the number of items used are associated with higher alternative costs. There were no significant differences in alternative cost within economic groups (excluding pensioners). This paper identified 41 frequent patterns with different alternative costs. Research limitations/implications The limitation of this study is the fact that data for library usage mining were collected using a questionnaire survey. Practical implications This may be particularly important for both policy makers and library management. The first beneficiaries are donors and patrons, who can learn about the benefit that libraries bring to society. The proposed system for library usage mining also enables managers to promote specific (effective) services, take steps to avoid readers leaving, and improve the adoption of library services. It can also be used to adapt the location of library services. Librarians, especially those who engage in acquisitions, may also use this information in their evidence-based decisions about the design of services. Originality/value So far, there has been no relevant research on the economic aspects of extracted behaviour patterns. Therefore, this study revealed users’ economic preferences using a questionnaire survey that supplemented transaction data. The ability to describe users’ behaviour can provide library management with enough information to realise evidence-based decision making.

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Developing a novel recommender network-based ranking mechanism for library book acquisition

Cited by 10 publications

References 17 publications

Hybrid recommender system model for digital library from multiple online publishers

Hybrid recommender system model for digital library from multiple online publishers

An innovative citation recommendation model for draft papers with varying degrees of information completeness

Library usage mining in the context of alternative costs

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