Special issue on multimedia recommendation and multi-modal data analysis

• Main Authors: HE, Xiangnan, LIU, Zhenguang, ZHANG, Hanwang, NGO, Chong-wah, KARAMAN, Svebor, ZHANG, Yongfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6618; https://ink.library.smu.edu.sg/context/sis_research/article/7621/viewcontent/He2019_Article_SpecialIssueOnMultimediaRecomm.pdf
• Institution: Singapore Management University

Rich multimedia contents are dominating the Web. In popular social media platforms such as FaceBook, Twitter, and Instagram, there are over millions of multimedia contents being created by users on a daily basis. In the meantime, multimedia data consist of data in multiple modalities, such as text, images, audio, and so on. Users are heavily overloaded by the massive multi-modal data, and it becomes critical to explore advanced techniques for heterogeneous big data analytics and multimedia recommendation. Traditional multimedia recommendation and data analysis technologies cannot well address the problem of understanding users’ preference in the feature-rich multimedia contents, and have challenges in processing massive and multi-modal data. Moreover, previous works on multimedia recommendation and multi-modal data analysis mainly used shallow features and conventional deep learning methods to process the multimedia contents. Advanced deep learning and machine learning methods and novel deep-feature extraction mechanism are yet to be explored. This special issue seeks original contributions related to either multimedia recommendation or multi-modal data analysis related to recommendation