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arXiv · 2508.07241

SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations

Amit Jaspal·Kapil Dalwani·Ajantha Ramineni
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Abstract

Most industry scale recommender systems face critical cold start challenges new items lack interaction history, making it difficult to distribute them in a personalized manner. Standard collaborative filtering models underperform due to sparse engagement signals, while content only approaches lack user specific relevance. We propose SocRipple, a novel two stage retrieval framework tailored for coldstart item distribution in social graph based platforms. Stage 1 leverages the creators social connections for targeted initial exposure. Stage 2 builds on early engagement signals and stable user embeddings learned from historical interactions to "ripple" outwards via K Nearest Neighbor (KNN) search. Large scale experiments on a major video platform show that SocRipple boosts cold start item distribution by +36% while maintaining user engagement rate on cold start items, effectively balancing new item exposure with personalized recommendations.

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Amit Jaspal, Kapil Dalwani, Ajantha Ramineni. 2025-08-10. SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations. https://arxiv.org/abs/2508.07241

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