Conditional Memory Enhanced Item Representation for Generative Recommendation
Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follow a quantization-representation-generation pipeline, first assigning each item a SID, then constructing input representations from SID-token embeddings, and finally predicting the target SID through autoregressive generation. Existing item-level representation constructions mainly take two forms: directly merging SID-token embeddings into a compact vector, or enriching item-level representations with external inputs through additional networks. However, these item-level constructors still expose two practical ch...
| Authors | Ziwei Liu, Yejing Wang, Shengyu Zhou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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