Multimodal Learning in Multimedia Recommender Systems: Challenges and Future Directions
Multimodal Learning in Multimedia Recommender Systems: Challenges and Future Directions
With the exponential growth of multimedia big data and multimodal learning, particularly deep learning across diverse data types, has become central to the development of cutting-edge multimedia recommender systems.
These systems necessitate the integration of data from various modalities, such as text, images, and audio, to deliver accurate and personalised recommendations. As the complexity and scale of these systems increase, the demand for more sophisticated models, architectures, and data processing algorithms becomes ever more pressing.
In this talk, I will explore the critical role of multimodal learning in shaping state-of-the-art recommendation systems. I will discuss the key challenges and opportunities in this rapidly evolving domain, including:
- The importance of multimodal learning in improving recommendation accuracy and scalability in large-scale systems.
- Current limitations in existing models and architectures that restrict performance, particularly in handling diverse and complex data types.
- Technical hurdles in building, deploying, and evaluating multimodal deep learning-powered recommender systems across various application domains.
- Future research directions and industry practices, focusing on the transformative impacts of AI, including generative models, on the next wave of innovations in multimedia computing.
This talk aims to spark dialogue on the evolving role of multimodal AI and deep learning in multimedia computing, with a vision for advancing the understanding and processing of large-scale, multimodal datasets.
Speaker
Professor Jialie Shen
Professor Shen is currently a Professor in computer vision and machine learning with the Department of Computer Science, City St George's, University of London. His research spans key areas of artificial intelligence (AI), including computer vision, deep learning, machine learning, data science, and multimodal intelligence, with an emphasis on scalable, high-impact AI systems. His research results have expounded in more than 150 publications at prestigious journals and conferences (e.g,. ICML, NeurIPS, ICCV, CVPR, IJCAI, and AAAI), with several awards: the Lee Foundation Fellowship for Research Excellence Singapore, the Microsoft Mobile Plus Cloud Computing Theme Research Program Award, the Best Reviewer Award for Information Processing and Management (IP&M) 2019 and ACM Multimedia 2020, the Test of Time Reviewer Award for Information Processing and Management (IP&M) 2022, and Associate Editor with Honourable Mention for Pattern Recognition (PR) 2023/24.
Professor Shen has a strong record as a technical champion in international AI and Multimedia competitions. Professor Shen also plays active editorial and professional role within the research community, serving as Senior PC member or Area Chair for major conferences (e.g., NeurIPS, IJCAI, AAAI, SIGIR, ACM MM), and as Associate Editor, Senior Area Editor or Editorial Board Member for leading journals including Neurocomputing, IP&M, Information Retrieval Research Journal (IRRJ), Pattern Recognition, IEEE Transactions on Big Data, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Multimedia, and ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM). In parallel, he has an established grant review and evaluation record, serving as an invited reviewer and panel member for major national and international funding agencies (e.g, EPSRC, AHRC, The Swiss National Science Foundation, NSERC Canada). He is also a member of the EPSRC Peer Review College. He is Fellow of RSA and senior member of IEEE."
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