ReSID: Rethinking Long-Tail Retrieval in Generative Recommendation through Item Reachability
Muyu Zou, Shunye Tang, Yi Xu, Haibo Xing, Yandong Wang, Lingyu Mu, Jinxin Hu
原文摘要
Generative recommendation (GR) represents items with discrete Semantic IDs (SIDs) and casts retrieval as sequence generation. Under skewed interaction distributions and tight serving budgets, GR struggles to retrieve long-tail items. In this work, we are the first to study long-tail reachability across the complete pipeline of SID construction, decoding, and matching. We find that under equal code lengths, parallel SID construction consistently outperforms sequential SIDs on tail items. Moreover, even when full generation fails, decoded sequences carry locally consistent information that retrieves relevant tail items via partial matching. Yet this recovery is bounded by upstream search, failing once beam search prunes all prefixes completable within a fixed mismatch tolerance. Motivated by these findings, we propose ReSID to jointly preserve and recover tail candidates. Built on long parallel SIDs, ReSID pairs popularity-debiased and user-semantic-aware beam search to keep promising paths alive with partial SID matching to recover items from surviving tokens. ReSID achieves state-of-the-art long-tail retrieval while maintaining competitive overall accuracy, with lower latency and higher throughput under narrow beams. ReSID is deployed in our production system, serving main user traffic with substantial business gains. We release our complete code and model checkpoints to support full reproducibility and real-world deployment at https://github.com/Gabriel-reina/ReSID.
cs.IR