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GNNs scaled for friend recommendations boost user connections by 16%

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a scalable Graph Neural Network (GNN) system for friend recommendations on large social graphs, addressing challenges with massive datasets. The system incorporates multi-hash ID embeddings to significantly reduce embedding table size and temporal neighbor sampling with optimized data structures for efficient processing. An online A/B test demonstrated a 16% increase in friend additions from recommendations and an 11.5% rise in unique friend adders compared to a baseline system. AI

IMPACT This research offers a scalable solution for recommendation systems, potentially improving user engagement in social platforms.

RANK_REASON Academic paper detailing a novel technical approach and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GNNs scaled for friend recommendations boost user connections by 16%

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Academic paper detailing a novel technical approach and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Alexander D'yakonov ·

    Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling

    Friend recommendation is inherently graph-structured: the relevance of a potential connection depends on multi-hop social context rather than user attributes alone. However, deploying message-passing GNNs on a production-scale social graph with hundreds of millions of users and t…

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