Departmental Seminar - Multimodal Learning in Multimedia Recommender Systems: Challenges and Future Directions | Prof. Jialie Shen
| Dates: | 6 November 2024 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Computer Science |
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| How much: | Free |
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| Who is it for: | University staff |
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| Speaker: | Prof. Jialie Shen |
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With the exponential growth of multimedia big data, 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
Prof. Jialie Shen
Role: Professor in computer vision and machine learning (Chair) with the Department of Computer Science
Organisation: City, University of London, UK
Travel and Contact Information
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Kilburn_TH 1.3
Kilburn Building
Manchester
