Classifying library catalogue by author profiling

Author(s):  
Tadashi Nomoto
2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model


2010 ◽  
Vol 30 (1) ◽  
pp. 120-129 ◽  
Author(s):  
Leo Gooch

Professor Birrell has remarked that there is ‘an extensive literature on how to describe a book, but there is no literature whatever on how to describe a library or a library catalogue’. Well, what follows is an account of a library in a Northumbrian-Catholic-Jacobite peer's house, not, admittedly, a common category but one having some cultural and recusant interest nevertheless.


2020 ◽  
Vol 24 (3) ◽  
Author(s):  
Miguel Á. Álvarez Carmona ◽  
Esaú Villatoro Tello ◽  
Manuel Montes y Gómez ◽  
Luis Villaseñor Pineda

2021 ◽  
Author(s):  
Shloak Rathod

<div><div><div><p>The proliferation of online media allows for the rapid dissemination of unmoderated news, unfortunately including fake news. The extensive spread of fake news poses a potent threat to both individuals and society. This paper focuses on designing author profiles to detect authors who are primarily engaged in publishing fake news articles. We build on the hypothesis that authors who write fake news repeatedly write only fake news articles, at least in short-term periods. Fake news authors have a distinct writing style compared to real news authors, who naturally want to maintain trustworthiness. We explore the potential to detect fake news authors by designing authors’ profiles based on writing style, sentiment, and co-authorship patterns. We evaluate our approach using a publicly available dataset with over 5000 authors and 20000 articles. For our evaluation, we build and compare different classes of supervised machine learning models. We find that the K-NN model performed the best, and it could detect authors who are prone to writing fake news with an 83% true positive rate with only a 5% false positive rate.</p></div></div></div>


The Library ◽  
1981 ◽  
Vol s6-III (4) ◽  
pp. 305-319 ◽  
Author(s):  
ROBERT S. MATTESON
Keyword(s):  

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