{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:13:16Z","timestamp":1761808396276},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T00:00:00Z","timestamp":1603324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Events represent a tipping point that affects users\u2019 opinions and vary depending upon their popularity from local to international. Indeed, social media offer users platforms to express their opinions and commitments to events that attract them. However, owing to the volume of data, users are encountering a difficulty to accede to the preferred events according to their features that are stored in their social network profiles. To surmount this limitation, multiple event recommendation systems appeared. Nevertheless, these systems use a limited number of event dimensions and user\u2019s features. Besides, they consider users\u2019 features stored in a single user\u2019s profile and disregard the semantic concept. In this research, an approach for multi-dimensional event recommendation is set forward to recommend events to users resting on several event dimensions (engagement, location, topic, time and popularity) and some user\u2019s features (demographic data, position and user\u2019s\/friend\u2019s interests) stored in multi-user\u2019s profiles by considering the semantic relationships between user\u2019s features, specifically user\u2019s interests. The performance of our approach was assessed using error rate measurements (mean absolute error, root mean squared error and cross-validation). Experiment that results on real-world event data sets confirmed that our approach recommends events that fit the user more than the previous approaches with the lowest error rate values.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa126","type":"journal-article","created":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T19:09:54Z","timestamp":1599937794000},"page":"369-382","source":"Crossref","is-referenced-by-count":7,"title":["MDER: Multi-Dimensional Event Recommendation in Social Media Context"],"prefix":"10.1093","volume":"64","author":[{"given":"Abir","family":"Troudi","sequence":"first","affiliation":[{"name":"Multimedia InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Tunis Street km. 10, Technopole, Sfax 3029, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leila","family":"Ghorbel","sequence":"additional","affiliation":[{"name":"Multimedia InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Tunis Street km. 10, Technopole, Sfax 3029, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corinne","family":"Amel Zayani","sequence":"additional","affiliation":[{"name":"Multimedia InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Tunis Street km. 10, Technopole, Sfax 3029, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salma","family":"Jamoussi","sequence":"additional","affiliation":[{"name":"Multimedia, InfoRmation systems and Advanced Computing Laboratory, University of Sfax"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikram","family":"Amous","sequence":"additional","affiliation":[{"name":"Multimedia InfoRmation systems and Advanced Computing Laboratory, University of Sfax, Tunis Street km. 10, Technopole, Sfax 3029, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,10,22]]},"reference":[{"key":"2021120506160171400_ref1","first-page":"409","article-title":"Building a Large-Scale Corpus for Evaluating Event Detection on Twitter","volume-title":"Proc. 22nd ACM Int. 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