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arXiv Computation and Language By Qiyong Zhong, Jiajie Su, Ming Yang, Li Zhang

JevRec: Linguistic Encoding with Jev-like Decision Modeling for LLM-based Generative Recommendation

Read the original on arXiv Computation and Language →

JevRec introduces a variable‑length linguistic encoding for items in generative recommendation, assigning shorter semantic identifiers (SIDs) to popular items and richer SIDs to long‑tail items. It separates length prediction and content generation into distinct heads while sharing the interaction‑history representation, mitigating gradient conflicts. Experiments on three public datasets demonstrate that JevRec surpasses baseline models in recommendation metrics and reduces training and inference time compared to TIGER.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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