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:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
arXiv:2605. 17779v2 Announce Type: replace Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs).
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.
The paper introduces API, a framework that adapts pretrained interpretable features for generative recommendation. By reusing sparse autoencoder‑derived feature indices as a reusable semantic ID vocabulary, API avoids additional vocabulary learning and preserves item distinctions even with limited token budgets. The Semantic Prediction Adapter learns user preferences from interaction histories, combining feature‑index and strength predictions to score candidates, while also providing explainable retrieval through feature contributions linked to pretrained semantic annotations.
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.
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.
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:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv:2607. 04270v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task.
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their promise, we identify a pervasive yet underexplored issue: $\textit{Length Bias}$.
arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to r...