The paper presents a scalable graph neural network (GNN) system for friend recommendation on a massive social graph. It introduces two key design choices: multi-hash ID embeddings that shrink the embedding table by over 98% without hurting ranking quality, and a timestamp-sorted compressed sparse row (CSR) storage with binary search that reduces temporal neighbor sampling from linear to logarithmic time. Experiments on a 194‑million‑user, 28‑billion‑edge graph show that these techniques enable production‑grade performance, boosting friend additions by 16% and unique friend adders by 11.5% in an online A/B test.
arXiv:2606. 18379v1 Announce Type: cross Abstract: Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet existing work addresses each in isolation.
arXiv:2605.23753v2 Announce Type: replace Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph e...
arXiv:2607. 20737v1 Announce Type: new Abstract: Graph Neural Networks trained on heterogenous bipartite graphs form a common basis in recommendation systems.
arXiv:2602.09716v2 Announce Type: replace Abstract: Computing the importance of nodes in networks is a long-standing fundamental problem that has driven extensive study of various centrality measures...
The paper introduces Multi-Probe Zero Collision Hash (MPZCH), an indexing mechanism that uses linear probing to reduce or eliminate collisions in embedding tables for large-scale recommendation systems. MPZCH employs auxiliary tensors and CUDA kernels to support configurable probing and active eviction, thereby retiring obsolete IDs and resetting reassigned slots to prevent stale embeddings. Experiments show that MPZCH achieves zero collisions for user embeddings, improves item embedding freshness and quality, and maintains training and inference performance comparable to existing methods.
arXiv:2607. 28311v1 Announce Type: cross Abstract: Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation.
arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.
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.
arXiv:2510. 04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines that ground large language models, yet the methods that address it have multiplied across communities until they are seldom read as a single field.
The paper introduces Connected Content Retriever (CC Retriever), a pre‑ranking system for LinkedIn’s Feed that uses dense graph edge features to score candidate content from a billion‑scale index within a 120 ms latency budget. By leveraging GPU‑based sorted‑search primitives, the system can apply a full deep ranking model with 50× more parameters, achieving a 2.5% lift in content time spent in online experiments. The work details the economic‑graph features and model architecture that enable this scalable, low‑latency scoring pipeline.
arXiv:2608. 09016v1 Announce Type: cross Abstract: Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation.
arXiv:2508.02609v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSage\cite{GraphSage...