Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
arXiv:2606.06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content has no interaction history. In Tubi's production retrieval system, this challenge is further constrained by the serving interface: new content must be assigned a standalone embedding immediately, and the model must also produce device embeddings suitable for approximate nearest-neighbor retrieval. We address this setting by for
The proliferation of new content online and the demand for personalized user experiences necessitate continuous improvements in recommendation systems, particularly for cold-start scenarios.
Advanced recommendation systems are critical for digital platforms to retain users, monetize content effectively, and overcome the challenge of new item discovery.
This research outlines an improved method for handling cold-start item recommendations, potentially leading to faster and more accurate content integration on platforms.
- · Content platforms
- · AI/ML researchers
- · Digital media companies
- · E-commerce
- · Platforms with unsophisticated recommendation systems
- · Less agile content providers
Improved user engagement and content discoverability on platforms adopting this technology.
Increased speed of content rollout and return on investment for new content producers.
Enhanced AI-driven personalization could further fragment media consumption patterns, reducing reach for non-optimized content.
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Read at arXiv cs.LG