VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
arXiv:2606.19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unlike traditional items, new short-form videos lack the dense interaction history required for collaborative filtering. Furthermore, immersive feeds introduce strong position and duration biases that distort standard engagement signals. In this paper, we demonstrate the Video Candidate Generation (VCG) system, a scalable multimodal retrieval engine designed to solve thes
The rapid shift in digital commerce towards dynamic video feeds, driven by platforms like TikTok, necessitates new AI paradigms to overcome 'extreme cold-start' problems for new content.
This development addresses a critical challenge for e-commerce platforms, enabling effective content recommendation and monetization in a video-first environment, impacting user engagement and advertising revenue.
The ability to effectively retrieve and recommend new video content without extensive interaction history significantly alters how products and media can be introduced and consumed online.
- · E-commerce platforms
- · AI/ML developers
- · Content creators
- · Digital advertising industry
- · Traditional e-commerce search models
- · Platforms reliant on static catalogs
- · Recommendation systems based solely on collaborative filtering
E-commerce platforms deploy more sophisticated multimodal AI to handle video content retrieval and recommendations.
Increased consumer engagement with video shopping experiences leads to higher conversion rates and new advertising formats.
The success of these multimodal systems influences the development of AI for other 'cold-start' challenges across various media and data types.
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Read at arXiv cs.LG