RankGraph-2 jointly optimizes graph construction, training, and serving for billion-node recommendation retrieval, reporting 3.8x recall gains and CTR/CVR improvements via subsampling, pre-computed neighborhoods, and co-learned indexing.
Breaking the hour- glass phenomenon of residual quantization: Enhancing the upper bound of generative retrieval.arXiv preprint arXiv:2407.21488,
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RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
RankGraph-2 jointly optimizes graph construction, training, and serving for billion-node recommendation retrieval, reporting 3.8x recall gains and CTR/CVR improvements via subsampling, pre-computed neighborhoods, and co-learned indexing.