A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0

2011 ◽  
Vol 181 (9) ◽  
pp. 1503-1516 ◽  
Author(s):  
Jesus Serrano-Guerrero ◽  
Enrique Herrera-Viedma ◽  
Jose A. Olivas ◽  
Andres Cerezo ◽  
Francisco P. Romero
Keyword(s):  
Recommender System ◽  
Digital Libraries

Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage

2010 ◽  
pp. 27-38 ◽  
Author(s):  
Cataldo Musto ◽  
Fedelucio Narducci ◽  
Pasquale Lops ◽  
Marco de Gemmis ◽  
Giovanni Semeraro
Keyword(s):  
Cultural Heritage ◽  
Recommender System ◽  
Digital Libraries

A task‐focused literature recommender system for digital libraries

2013 ◽  
Vol 37 (4) ◽  
pp. 581-601 ◽  
Author(s):  
Wan‐Shiou Yang ◽  
Yi‐Rong Lin
Keyword(s):  
Recommender System ◽  
Digital Libraries

PORE: a personal ontology recommender system for digital libraries

2009 ◽  
Vol 27 (3) ◽  
pp. 496-508 ◽  
Author(s):  
Shu‐Chuan Liao ◽  
Kuo‐Fong Kao ◽  
I‐En Liao ◽  
Hui‐Lin Chen ◽  
Shu‐O Huang
Keyword(s):  
Recommender System ◽  
Digital Libraries ◽  
Personal Ontology

Serendipitous Recommenders for Teachers in Higher Education

2019 ◽  
pp. 333-353 ◽  
Author(s):  
Ahmad Hassan Afridi
Keyword(s):  
Higher Education ◽  
Focus Group ◽  
Data Collection ◽  
Recommender Systems ◽  
Recommender System ◽  
Digital Libraries ◽  
Relevant Literature ◽  
Use Case ◽  
Focus Group Study ◽  
Academic Activities

Currently, most of the recommender systems that are in a prototype or deployed stage are primarily accuracy oriented. This chapter focuses on teacher preferences for designing serendipity-oriented recommender systems for academic activities. Reports on relevant literature about serendipitous recommenders and fac ulty empowerment with such tools, a focus group study of teachers for some industrial recommender system platforms, and a use case on instructor use of recommenders to inform and support recommendations for lectures are covered. Further, a survey of students to explore the feasibility of student-teacher serendipitous activities and operations are also reported. The results from all three studies show that serendipity has a major role to play in the future. The author surveyed the literature on standard digital libraries and used questionnaire-based data collection and standard statistical methods to evaluate the responses.

scholarly journals Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access Infrastructure

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 497 ◽  
Author(s):  
Mladen Borovič ◽  
Marko Ferme ◽  
Janez Brezovnik ◽  
Sandi Majninger ◽  
Klemen Kac ◽  
...  
Keyword(s):  
Open Access ◽  
Recommender System ◽  
Digital Libraries ◽  
Digital Repository ◽  
Real World Data ◽  
Institutional Repositories ◽  
Data Feedback ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Direct Feedback

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the aim to connect researchers and improve collaboration efforts. The hybrid recommender system makes use of document processing techniques, document metadata, and the similarity ranking function BM25 to provide content-based recommendations as a primary method. It also uses collaborative-filtering methods as a secondary method in a cascade hybrid recommendation technique. We also provide a real-world data feedback collection analysis for our hybrid recommender system on an academic digital repository in order to be able to identify suitable time-frames for direct feedback collection during the year.

A personalized recommender system for digital libraries

2008 ◽  
Author(s):  
Giseli Rabello Lopes ◽  
Maria Aparecida Martins Souto ◽  
Leandro Krug Wives ◽  
José Palazzo Moreira de Oliveira
Keyword(s):  
Recommender System ◽  
Digital Libraries ◽  
Personalized Recommender System

Personalized Recommender System for Digital Libraries

2014 ◽  
Vol 9 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Omisore M. O. ◽  
Samuel O. W.
Keyword(s):  
Recommender System ◽  
Digital Libraries ◽  
User Satisfaction ◽  
Information Filtering ◽  
Cold Start ◽  
Web Based ◽  
Reading Abilities ◽  
Filtering Technique ◽  
Personalized Recommender System ◽  
Matching Techniques

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.

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