G2Rec unifies graph co-engagement modeling with semantic tokenization for generative recommendation.

A Meta-affiliated team introduces G2Rec, which captures user interest patterns without ground-truth interest labels and reports online deployment across production surfaces plus public-dataset gains over prior generative-rec and graph-tokenization baselines. The interesting bit for practitioners: it goes after the two things that actually block recsys deployments, the scalability ceiling of graph methods and the heuristic, unsupervised nature of semantic tokenization. Worth a read if you're anywhere near large-scale recommendation.