research-article

Filtering Graduate Courses based on LinkedIn Profiles

Authors:

Leandro Figueira Lessa,

Wladmir Cardoso BrandãoAuthors Info & Claims

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

Pages 141 - 147

https://doi.org/10.1145/3243082.3243094

Published: 16 October 2018 Publication History

Abstract

People are overloaded with the everyday massive amount of information they consume. There are several options available for choice, from TV shows, books, traffic routes to graduate courses. In this scenario of multiple choices, the manual search and evaluation of all possibilities to make decisions is unfeasible. In the academic context, the HEIs (Higher Education Institutions) offer several graduate courses and, with so many options, students need mechanisms to choose relevant courses to their interest in order to reduce the dropout and financial loss risks. In this article, we propose a recommendation approach that filters graduate courses for students using their LinkedIn professional profiles. Experiments show that features based on competences and activity area are more effective than professional summary and experiences to recommend graduate courses within a content-based approach. In addition, our proposed approach performs recommendations with precision of up to 68.50% in the top-1 recommendation lists, achieving 100% coverage.

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Cited By

Ma BYang TRen B(2024)A Survey on Explainable Course Recommendation SystemsDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-60012-8_17(273-287)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60012-8_17
Al-Twijri MLuna JHerrera FVentura S(2022)Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential PatternsCognitive Computation10.1007/s12559-022-10015-514:4(1474-1495)Online publication date: 19-Apr-2022
https://doi.org/10.1007/s12559-022-10015-5
Urdaneta-Ponte MMendez-Zorrilla AOleagordia-Ruiz I(2021)Recommendation Systems for Education: Systematic ReviewElectronics10.3390/electronics1014161110:14(1611)Online publication date: 6-Jul-2021
https://doi.org/10.3390/electronics10141611
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Index Terms

Filtering Graduate Courses based on LinkedIn Profiles
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web applications
      1. Social networks
    2. Web mining

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Published In

cover image ACM Other conferences

WebMedia '18: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web

October 2018

437 pages

ISBN:9781450358675

DOI:10.1145/3243082

General Chairs:
Manoel Carvalho Marques Neto
IFBA
,
Renato Lima Novais
IFBA
,
Carlos Ferraz
UFPE
,
Windson Viana
UFC

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SBC: Brazilian Computer Society
SIGMM: ACM Special Interest Group on Multimedia
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2018

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WebMedia '18

WebMedia '18: Brazilian Symposium on Multimedia and the Web

October 16 - 19, 2018

BA, Salvador, Brazil

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WebMedia '18 Paper Acceptance Rate 37 of 111 submissions, 33%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Ma BYang TRen B(2024)A Survey on Explainable Course Recommendation SystemsDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-60012-8_17(273-287)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60012-8_17
Al-Twijri MLuna JHerrera FVentura S(2022)Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential PatternsCognitive Computation10.1007/s12559-022-10015-514:4(1474-1495)Online publication date: 19-Apr-2022
https://doi.org/10.1007/s12559-022-10015-5
Urdaneta-Ponte MMendez-Zorrilla AOleagordia-Ruiz I(2021)Recommendation Systems for Education: Systematic ReviewElectronics10.3390/electronics1014161110:14(1611)Online publication date: 6-Jul-2021
https://doi.org/10.3390/electronics10141611
Guruge DKadel RHalder S(2021)The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent ResearchData10.3390/data60200186:2(18)Online publication date: 11-Feb-2021
https://doi.org/10.3390/data6020018
Adak MErcan S(2021)Support Vector Machine and Decision Tree-Based Elective Course Suggestion System: A Case Study2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)10.1109/3ICT53449.2021.9581846(552-556)Online publication date: 29-Sep-2021
https://doi.org/10.1109/3ICT53449.2021.9581846
Franco CHernández NNúñez H(2021)Enhancing Social Recommenders with Implicit Preferences and Fuzzy Confidence FunctionsModeling Decisions for Artificial Intelligence10.1007/978-3-030-85529-1_10(118-130)Online publication date: 20-Sep-2021
https://doi.org/10.1007/978-3-030-85529-1_10
Aguiar JFechine Jde Barros Costa Ede Salles Soares Neto C(2020)Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of CustomersProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430969(157-164)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3430969
Ponte MZorilla ARuiz I(2020)Taxonomy-Based Hybrid Recommendation System for Lifelong Learning to Improve Professional Skills2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE48869.2020.9368398(595-600)Online publication date: 8-Dec-2020
https://doi.org/10.1109/TALE48869.2020.9368398

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