Integration and Recommendation System of Profiles based on Professional Social Networks | EAI Endorsed Transactions on Context-aware Systems and Applications

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Integration and Recommendation System of Profiles based on Professional Social Networks | EAI Endorsed Transactions on Context-aware Systems and Applications </title> <link rel="icon" href="https://publications.eai.eu/public/journals/12/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.3.0.18"> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Paul Dayang"/> <meta name="DC.Creator.PersonalName" content="Ulriche Mbouche Bomda"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-01-15"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-11-29"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-01-12"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-02-05"/> <meta name="DC.Description" xml:lang="en" content="The aim of our investigation is to personalize bilateral recommendation of job-related proposals based on existing professional social networks. In a context where the points of view of job seekers and employers can be contradictory, our approach consists in trying to bring the both in a best possible matching. To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="4500"/> <meta name="DC.Identifier.DOI" content="10.4108/eetcasa.4500"/> <meta name="DC.Identifier.URI" content="https://publications.eai.eu/index.php/casa/article/view/4500"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 EAI Endorsed Transactions on Context-aware Systems and Applications"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/3.0/"/> <meta name="DC.Source" content="EAI Endorsed Transactions on Context-aware Systems and Applications"/> <meta name="DC.Source.ISSN" content="2409-0026"/> <meta name="DC.Source.Volume" content="10"/> <meta name="DC.Source.URI" content="https://publications.eai.eu/index.php/casa"/> <meta name="DC.Subject" xml:lang="en" content="bilateral matching problem"/> <meta name="DC.Title" content="Integration and Recommendation System of Profiles based on Professional Social Networks"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Research article"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="EAI Endorsed Transactions on Context-aware Systems and Applications"/> <meta name="citation_journal_abbrev" content="EAI Endorsed Trans Context Aware Syst App"/> <meta name="citation_issn" content="2409-0026"/> <meta name="citation_author" content="Paul Dayang"/> <meta name="citation_author_institution" content="University of Ngaoundéré "/> <meta name="citation_author" content="Ulriche Mbouche Bomda"/> <meta name="citation_author_institution" content="University of Ngaoundéré "/> <meta name="citation_title" content="Integration and Recommendation System of Profiles based on Professional Social Networks"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/01/15"/> <meta name="citation_volume" content="10"/> <meta name="citation_doi" content="10.4108/eetcasa.4500"/> <meta name="citation_abstract_html_url" content="https://publications.eai.eu/index.php/casa/article/view/4500"/> <meta name="citation_keywords" xml:lang="en" content="Integration system"/> <meta name="citation_keywords" xml:lang="en" content="job recommender system"/> <meta name="citation_keywords" xml:lang="en" content="social recommendations"/> <meta name="citation_keywords" xml:lang="en" content="personalized recommendations"/> <meta name="citation_keywords" xml:lang="en" content="bilateral matching problem"/> <meta name="citation_pdf_url" content="https://publications.eai.eu/index.php/casa/article/download/4500/2821"/> <meta name="citation_reference" content="Renaud-Deputter, S. 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To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions. 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style="fill:#202826" /> </g> </g> </switch> </svg> </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.4108/eetcasa.4500"> https://doi.org/10.4108/eetcasa.4500 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> Integration system, job recommender system, social recommendations, personalized recommendations, bilateral matching problem </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>The aim of our investigation is to personalize bilateral recommendation of job-related proposals based on existing professional social networks. In a context where the points of view of job seekers and employers can be contradictory, our approach consists in trying to bring the both in a best possible matching. To this end, we propose an integration system that gives a minimum of credit to the users’ data in order to facilitate the discovery of relevant proposals based on the users’ behaviors, on the characteristics of the proposals and on possible relationships. The main contribution is the proposal of an architecture for the recommendation of profiles and job offers including social and administrative factors. The particularity of our approach lies in the freedom from the recommendation problem by using metrics proven in the literature for the estimation of similarity rates. We have used these metrics as default values to appropriate data dimensions. It emerges that, the user’s behavior is exclusively responsible for the recommendations. However, the cross-analysis of randomly generated behaviors on real profiles collected on Cameroonian sites dedicated to job offers, shows the influence of the most active users. But, for requests via the search bar (interface with the script respecting the path of our architecture) the central subject remains the user. Our current work is limited by a data set that is not very representative of changing socio-economic conditions.</p> </section>  <a href="https://plu.mx/plum/a/?doi=10.4108/eetcasa.4500" class="plumx-summary" data-hide-when-empty="true" data-orientation="horizontal" ></a>  <section class="item author_bios"> <h2 class="label"> Author Biographies </h2> <section class="sub_item"> <h3 class="label"> Paul Dayang, <span class="affiliation">University of Ngaoundéré </span> </h3> <div class="value"> <strong>University of Ngaoundere</strong> · Computer Science · <em>Head of Department</em> · Associate Professor<br><br> </div> </section> <section class="sub_item"> <h3 class="label"> Ulriche Mbouche Bomda, <span class="affiliation">University of Ngaoundéré </span> </h3> <div class="value"> <p>University of Ngaoundere . Compter Science . Researcher . 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