Workshop Title:

Unlocking Video Contextual Ad Insights: Enhancing Topic Explainability with Rich Multimodal Content Retrieval

Date:

August 9th, 2024 (GMT+1)

Organizer:

University of Strathclyde

Keywords:

• Machine Learning
• Artificial Intelligence
• Content Retrieval
• Video Contextual Adverts
• Advertising in Media Landscape

Workshop Chair:

Personal Bio:

Prof. Anil Fernando received the B.Sc. (Hons.) degree (First Class) in electronics and telecommunication engineering from the University of Moratuwa, Sri Lanka, in 1995, and the M.Sc. in Communications (Distinction) from the Asian Institute of Technology, Bangkok, Thailand in 1997 and Ph.D. in Computer Science (Video Coding and Communications) from the University of Bristol, UK in 2001. He is a professor in Video Coding and Communications at the Department of Computer and Information Sciences, University of Strathclyde, UK. He leads the video coding and communication research team at Strathclyde. He has worked on major national and international multidisciplinary research projects and led most of them. He has published over 425 papers in international journals and conference proceedings and published a book on 3D video broadcasting. He has been working with all major EU broadcasters, BBC, and major European media companies/SMEs in the last decade in providing innovative media technologies for British and EU citizens. His main research interests are in Video coding and Communications, Machine Learning (ML) and Artificial Intelligence (AI), Semantic Communications, Signal Processing, Networking and Communications, Interactive Systems, Resource Optimizations in 6G, Distributed Technologies, Media Broadcasting and Quality of Experience (QoE).

Workshop Committee Members:

Mr. Waruna De Silva University of Strathclyde [email protected]

Workshop Description:

Background:

The advertising digital ecosystem has experienced tremendous changes with the rise of free video streaming platforms that deliver hundreds of programs each day. In addition, the transition to a cookieless world presents a distinctive and urgent challenge. Especially so with respect to the access of behavioural data. As cookies become less prevalent, the ability to record and analyse user behaviour becomes a critical concern for the industry players. This emphasises the importance of integrating data analysis requirements while protecting user privacy and adhering to evolving data protection regulations. Whilst stressing on the significance of standards for effective ad requests across diverse systems, it is realised that the necessity of relevant and personalised ad targeting practices are more important than ever in this setting. Moreover, It is also essential to address challenges related to ensuring brand safety and providing a satisfactory user experience. Such challenges have highlighted the significance of context analysis in identifying suitable advertisements. Therefore, exploring the minute details of contextual advertising is a proactive point of view to overcoming these issues and also a technique to deliver successful targeted advertising of videos in the industrial setting.

Goal/Rationale:

The exponential rise of free video platforms continues to reshape content consumption and the dynamics of advertising. In addition, the declining relevance of cookies emphasises the need for a modern and suitable method to record and analyse user behaviour as well as prioritise User Privacy and comply with Data Protection and regulatory guidelines. As a result, contextual advertising has become a suitable strategy in the delivery of relevant and customised internet based advertisements to users. This has led to a noticeable increase in ad-user relevance and more importantly engagement. In light of this evolving process, this proposes a novel framework to facilitate discovering contextual information of video content within the industrial context. The proposed framework seamlessly integrates data modalities and visual and audio features that are extracted from video content, to obtain a comprehensive comprehension of videos. The classification, with respect to industry contextual norms, is further improved by the incorporation of industry taxonomy alignments. Advertisers can harness these insights to serve ads that perfectly match users professional interests and preferences within specific industries. Through the synergy of multimodal analysis, industry taxonomy, and contextual advertising, this method assures that ads are presented within a context that resonate with users.

The effectiveness of the framework is validated through experimental results on the YouTube-8M data set, thereby showcasing its potential to revolutionise contextual advertising, by capturing the essence of video content and aligning it with industry content taxonomy. These findings represent an advancement in the field of contextual advertising for video streaming platforms.

Scope and Information for Participants:

In the context of streaming video advertising, this seeks to present a thorough breadth of targeted video advertising algorithms. The focus includes an overview of video based advertising selection algorithms applied in this field and an examination of their uses and advantages. The workshop will also point out the constraints and difficulties associated with deriving context and meaning from video footage. The study will explore cutting-edge strategies and techniques that help to create more precise algorithms by examining recent research developments in video advertising. Through careful study, the performance of improved algorithms in video context extraction will be assessed. Additionally, this will assess the effects of cookie removal, consider the implications for future streaming video advertising, and investigate alternative solutions to the challenges associated with personalized targeting in this evolving landscape.

Venue:

LT403, Department of Computer and Information Sciences, University of Strathclyde, UK

VISA:

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Attend in person:

If you want to attend the workshop on-site, please email the Conference Committee: [email protected].