Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

• Published in: International journal of interactive multimedia and artificial intelligence Vol. 7; no. 6; pp. 45 - 53
• Main Authors: Martín-Gómez, Lucía, Pérez-Marcos, Javier, Cordero-Gutiérrez, Rebeca, De La Iglesia, Daniel H.
• Format: Journal Article
• Language: English
• Published: IMAI Software 01-09-2022; Universidad Internacional de La Rioja (UNIR)
• Subjects: Artificial Intelligence; Digital Image Processing; Image processing; recommendation systems; Recommender System; social; Social Media; Social networks; Tagging
• ISSN: 1989-1660; 1989-1660
• DOI: 10.9781/ijimai.2022.09.002

Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages. Some social networks use multimedia content to promote information and create social communities. In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags. In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community. It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content. The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram. The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content. KEYWORDS Artificial Intelligence, Digital Image Processing, Recommender System, Social Media, Tagging.