An Expertise Recommender System based on Data from an Institutional Repository (DiVA)

Angelova, Milena; Manasa, Devagiri Vishnu; Boeva, Veselka; Linde, Peter; Lavesson, Niklas

doi:10.4000/books.oep.9078

Cited by 3 publications

(11 citation statements)

References 9 publications

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“…1 Student [Aarthi et al, 2019, Angelova et al, 2018, Baskota and Ng, 2018, Bhumichitr et al, 2017, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Chang et al, 2011, Chen et al, 2020, Dahdouh et al, 2019, Praserttitipong and Srisujjalertwaja, 2018, Dussan-Sarria and Leon-Guzman, 2012, Dwivedi and Roshni, 2017, Esteban et al, 2020, Ezz and Elshenawy, 2020, Heras et al, 2020, Hidayat et al, 2020, Hoic-Bozic et al, 2016, Hsu et al, 2013, Iatrellis et al, 2017, Ibrahim et al, 2017, Ibrahim et al, 2020, Isma'il et al, 2020, Janpla and Kularbphettong, 2016, Kanika et al, 2019, Kopeinik, 2018, Liao et al, 2020, Lin et al, 2018, Liu et al, 2017, Ma et al, 2020, Mokarrama et al, 2020, Nho et al, 2020, Nuswantari et al, 2020, Obeid et al, 2018, Palilingan and Batmetan, 2018, Pardos and Jiang, 2020, Pariserum Perumal et al, 2019, Pereira et al, 2018, Porcel et al, 2020, Putri and Zulkarnain, 2...…”

Section: What Kind Of Users Is the Recommendation Aimed At?mentioning

confidence: 99%

“…Higher education [Almutairi et al, 2017, Angelova et al, 2018, Benhamdi et al, 2017, Bhumichitr et al, 2017, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Cerna, 2020, Chen et al, 2020, Cobos et al, 2013, Dahdouh et al, 2019, De Medio et al, 2020, Praserttitipong and Srisujjalertwaja, 2018, Dussan-Sarria and Leon-Guzman, 2012, El-Bishouty et al, 2014, Esteban et al, 2020, Faisal et al, 2020, Ferreira et al, 2020, Heras et al, 2020, Hoic-Bozic et al, 2016, Iatrellis et al, 2017, Ibrahim et al, 2020, Isma'il et al, 2020, Jun Liu et al, 2010, Kanika et al, 2019, Kopeinik, 2018, Lin et al, 2018, Liu et al, 2017, Ma et al, 2020, Nho et al, 2020, Nuswantari et al, 2020, Pardos and Jiang, 2020, Pariserum Perumal et al, 2019, Pereira et al, 2018, Porcel et al, 2020, Putri and Zulkarnain, 2020, Sabic and El-Zayat, 2010, Salehi et al, 2014, Sauer et al, 2014, So...…”

mentioning

confidence: 99%

“…To recommend: , Baskota and Ng, 2018, Mokarrama et al, 2020, Obeid et al, 2018, Yuliansyah et al, 2020 13.75% Degree programs, careers, majors or departments [Chang et al, 2011, Ezz and Elshenawy, 2020, Janpla and Kularbphettong, 2016, Nuswantari et al, 2020, Selvam et al, 2020 Collaborators in R&D projects [Wang et al, 2017] 4 To recommend jobs or personalized curriculum [Liu et al, 2017, Palilingan andBatmetan, 2018] 2.50% 5 To recommend learning paths [Iatrellis et al, 2017, Jiang et al, 2014 2.50% 6 Other [Angelova et al, 2018, Faisal et al, 2020, Ma et al, 2020, Sauer et al, 2014 5.00%…”

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confidence: 99%

See 2 more Smart Citations

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Ugarte¹,

Larrañaga²,

Arruarte³

2022

jucs

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Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

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Section: What Kind Of Users Is the Recommendation Aimed At?mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Ugarte¹,

Larrañaga²,

Arruarte³

2022

jucs

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show abstract

“…SRS-E help students and teachers to make choices (e.g., of suitable courses) in the educational (E) context [23]. Further tasks for SRS-E are given in [4], where the authors describe an RS that can be applied for finding experts in academia, for example, supervisors for students' qualifications or research, reviewers for conferences, journal or project submissions, or partners for R&D proposals. SRS-STI, that is, RS for scientific and technical information (STI), are addressed in [48].…”

Section: Topics For Scientific Recommendationmentioning

confidence: 99%

“…[2], Chapter 7), the related notions of testing and test environments might require a further explanation. Their obvious goal is direct certification of a RS, as, for example, in case of the data set and precise test descriptions provided in the context of the Netflix prize 4 . On the other hand, their development might be motivated by human factors: While some consumer groups might make decisions taking into account just the quality criteria, fulfillment of requirements, known benchmarks results, or recommendations from peers, other consumer groups, in particular, professionals, might want to parameterize and try out different digital products in a common test environment themselves to find the right product or the right solution in a given situation and for a specified task.…”

Section: Introductionmentioning

confidence: 99%

Recommender Techniques for Software With Result Verification

Auer¹,

Luther²

2019

Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Information Retrieval System for Scientific Publications of Lampung University by using VSM, K-Means, and LSA

Taufik,

Kurniawan,

Irawati

et al. 2023

IJACSA

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The Lampung University repository system is a repository of data related to study, community service, and other scientific works, currently has 37242 documents accessible through repository.lppm.unila.ac.id. Despite the amount of data, its optimal use as an information retrieval remains unrealized, hindering the effective promotion of Lampung University's scientific publication excellence. Recognizing the limitations of existing information retrieval systems that are limited to specific methods for topic identification through clustering, this study aims to develop a retrieval system for Lampung University's repository using Vector Space Model (VSM), K-Means and Latent Semantic Analysis (LSA) that generates clusters and study expertise at the level of study program, faculty and Lampung University. The methodology includes data collection, preprocessing, modeling, evaluation and system deployment. The results show that the number of clusters obtained for the university level is 7 clusters, for the faculty level are 6, 7, 8 and 10 clusters, and for the program level are 3 to 5 clusters. In addition, the finding topic identification indicate that the expertise topics at Lampung University, which are agriculture, soil, education, plants, learning, society, Lampung. This study contributes to optimizing the information retrieval system, promoting academic excellence, and advancing the understanding of study expertise at Lampung University.

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An Expertise Recommender System based on Data from an Institutional Repository (DiVA)

Cited by 3 publications

References 9 publications

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Recommender Techniques for Software With Result Verification

Information Retrieval System for Scientific Publications of Lampung University by using VSM, K-Means, and LSA

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