The paper introduces ComeIR, a Conditional Memory Enhanced Item Representation framework for generative recommendation. It reconstructs semantic identifier (SID) code embeddings into item-aware inputs, preserving structured SID relations through item-guided token scoring and dual-level conditional memory. The approach integrates these signals into historical item representations and conditions SID-level predictions on generated prefixes and transition evidence, showing improved performance in experiments.
arXiv:2608. 07816v1 Announce Type: cross Abstract: Recent advances in generative recommendation (GR) leverage large language models (LLMs) as recommender backbones, enabling LLMs to directly generate recommendations conditioned on item-interaction histories.
arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
REGER is a unified framework that enhances generative recommendation by explicitly modeling inter-item relations through semantic pretraining, history encoding, and SID generation. It pretrains the model to generate semantic neighbors’ IDs, uses combined global and local attention to capture historical context, and introduces a hierarchical preference decomposition over the SID prefix tree. The approach also distills a discriminative teacher’s full conditional distributions to improve next-token predictions, yielding consistent performance gains on real-world datasets.
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.
arXiv:2509. 25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals.
arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...
REGER is a framework that enhances generative recommendation by explicitly modeling inter-item relations through semantic pretraining, refined history encoding, and SID generation. It pretrains the model to generate semantic neighbors’ IDs, uses a combination of global and local attention to capture context, and introduces a hierarchical preference decomposition that allows exact factorization of collaborative preferences. The method also distills a discriminative teacher’s full conditional distributions to improve next-token predictions, yielding consistent performance gains on real-world datasets.
arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.
arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.
The paper proposes replacing a static multi‑level small semantic codebook with a dynamic single‑level large semantic codebook for generative recommendation. It introduces an exposure‑aware update mechanism and an offline evaluation framework, achieving significant improvements in recall, NDCG, decoding efficiency, and online consumption metrics on public datasets and production traffic.
arXiv:2607. 21101v1 Announce Type: new Abstract: Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs.