Library Recommender System Research Articles

Personalized recommendation by using fused user preference to construct smart library

In online digital library, it makes people be convenient to visit latest books or magazines via digitization. Online digital library becomes the platform for people to search and browse latest books or magazines. From digital library to smart library, the key is how to recommend interesting items from personalized preference according to visiting history records, comments, and discussions. This paper introduces user preference into digital library recommendation system. Generally, if two users have similar preference, they also like the books or magazines from the same or related topics. First, the topics is estimated by a Latent Dirichlet Allocation (LDA) model; second, the topics with high user preference score is recommended to the user. Thus, the user can receive the item list which are close to the topics which he or she is interested in. The experimental results on a university digital library show the effectiveness of the user preference based recommendation system for smart library.

A hybrid recommender system for patron driven library acquisition and weeding

Nowadays the explosion of information sources has shaped how library users access information and provide feedback on their preferences. Therefore, faced with this explosion and the blossoming of digital libraries, modern libraries must take up the challenge of meeting the needs of their users and considering their opinions and preferences in order to offer them adequate resources and henceforth eliminate those that are unsatisfactory.Almost all library recommender systems aim to provide users with items of interest to them. However, despite its definite interest, the staff-oriented adaptation and application of this revolutionary technique to the collection development process is still in an embryonic stage.We propose a patron-driven hybrid library recommender system that uses machine learning techniques to recommend and assist in the acquisition and weeding decision-making operations by extracting and analyzing users’ opinions and ratings. The recommender system is applied and validated in a real national library case using Amazon’s digital library and the library’s catalog as a data source.

Research on Library Personalized Recommendation System Based on Restricted Boltzmann Machine

At present, most recommendation systems in libraries are content-based and collaborative filtering-based, but these recommendation systems ignore the deep-seated characteristics of readers' personalized information. In order to improve the function of Library recommendation system, this paper proposes a library recommendation system based on restricted Boltzmann machine and collaborative filtering algorithm, and simulates the performance of the algorithm. The results show that the proposed algorithm has good application effect.

Effects of case library recommendation system on problem solving and knowledge structure development

Case-based reasoning posits that learners can use their prior experience to solve new problems. This theory is cited to explain the benefits of problem-based learning (PBL), especially as it relates to knowledge structure development. However, critics argue that learners lack the relevant knowledge structures to simultaneously learn new content and solve complex problems. In terms of learning design, theorists suggest a set of cases (case library) can be used as vicarious memory and thus bridge the experience gap. While this may be beneficial in theory, studies show experts and novices tend to process the details of a case in markedly different ways, which would be problematic in terms of case libraries' ability to scaffolding problem-solving. To address this challenge, this study compared the following conditions in terms of argumentation and knowledge structure development: PBL only, PBL with static case library, PBL with recommendation system case library. Both the case library conditions outperformed the PBL-only condition in terms of initial argument development. However, the PBL with recommendation system case library outperformed the other conditions on rebuttal development. Implications for PBL, CBR, knowledge structure development, and learning design are discussed.

Book Recommender System Using Genetic Algorithm and Association Rule Mining

Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.

The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.

BackgroundIt is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature.ObjectiveWe aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract.MethodsIn Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals.ResultsWe collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering–based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/.ConclusionsOur deep learning–based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

Designing Punjabi Poetry Classifiers Using Machine Learning and Different Textual Features

Analysis of poetic text is very challenging from computational linguistic perspective. Computational analysis of literary arts, especially poetry, is very difficult task for classification. For library recommendation system, poetries can be classified on various metrics such as poet, time period, sentiments and subject matter. In this work, content-based Punjabi poetry classifier was developed using Weka toolset. Four different categories were manually populated with 2034 poems Nature and Festival (NAFE), Linguistic and Patriotic (LIPA), Relation and Romantic (RORE), Philosophy and Spiritual (PHSP) categories consists of 505, 399, 529 and 601 numbers of poetries, respectively. These poetries were passed to various pre-processing sub phases such as tokenization, noise removal, stop word removal, and special symbol removal. 31938 extracted tokens were weighted using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme. Based upon poetry elements, three different textual features (lexical, syntactic and semantic) were experimented to develop classifier using different machine learning algorithms. Naive Bayes (NB), Support Vector Machine, Hyper pipes and K-nearest neighbour algorithms were experimented with textual features. The results revealed that semantic feature performed better as compared to lexical and syntactic. The best performing algorithm is SVM and highest accuracy (76.02%) is achieved by incorporating semantic information associated with words.

A Big Data smart library recommender system for an educational institution

PurposeWith the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations.Design/methodology/approachThis paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions.FindingsAs an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction.Social implicationsThe need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization.Originality/valueA unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

A Recommender System for Library Based on Hadoop Ecosystem

With the rapid development of network and information technology, big data already exists in various fields. Its main features include large amount of data (Volume), speed (Velocity) and diversity (Variety). The information system of library also has the characteristics of big data which have a large number of collected books, readers, and diverse resources. Because of the era of big data, libraries need to assist the reader to rapidly get the appropriate information from a large number of collected resources in the library information system. It is an important issue to establish the suitable recommender system in order to recommend readers to the related library resources and to enhance the usage of library resources. In this paper, a recommender system for library based on Hadoop Ecosystem is proposed. First, it uses Hadoop ecosystem to import data from the information system of library. Furthermore, the association rule and parallel CLARANS are implemented in Hadoop platform for the library recommender system. The proposed approach can assist readers to find the suitable resources in library.

PEMETAAN BASIS DATA RELASIONAL MENJADI BASIS DATA RDF UNTUK SISTEM PEREKOMENDASI KOLEKSI PERPUSTAKAAN

In order to develop a library recommender system based on Semantic Web technology, it is a must to exploit the use of RDF model database. In this paper we explain our experiences in converting library inforation system’s data from relational database (RDB) into RDF model database. We achieve it by using an open source tool, D2RQ. We test the RDF database by querying the data using SPARQL. The result showing that we can use this tool easily to obtain our objectives.

Ontology-based library recommender system using MapReduce

Recommender systems have been proven useful in numerous contemporary applications and helping users effectively identify items of interest within massive and potentially overwhelming collections. Among the recommender system techniques, the collaborative filtering mechanism is the most successful; it leverages the similar tastes of similar users, which can serve as references for recommendation. However, a major weakness for the collaborative filtering mechanism is its performance in computing the pairwise similarity of users. Thus, the MapReduce framework was examined as a potential means to address this performance problem. This paper details the development and employment of the MapReduce framework, examining whether it improves the performance of a personal ontology based recommender system in a digital library. The results of this extensive performance study show that the proposed algorithm can scale recommender systems for all-pairs similarity searching.

A library recommender system based on a personal ontology model and collaborative filtering technique for English collections

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.