arXiv AI
Jul 29
Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation
arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
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.
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arXiv Machine Learning
Aug 11
Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation
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 AI
Jul 29
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
arXiv AI
Jul 29
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
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.
arXiv AI
Aug 12
FedCGR: Federated Cross-Domain Generative Recommendation
arXiv:2608. 10929v1 Announce Type: new Abstract: Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or privacy-sensitive across clients.
arXiv AI
Jun 8
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
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 AI
Jun 9
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
arXiv:2606. 07688v1 Announce Type: cross Abstract: Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs).
arXiv AI
Aug 3
Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding
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 AI
Jun 10
Reasoning over Semantic IDs Enhances Generative Recommendation
arXiv:2603. 23183v2 Announce Type: replace-cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs).
arXiv AI
Jun 15
ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation
arXiv:2606. 14260v1 Announce Type: cross Abstract: Semantic IDs are crucial in generative recommendation, but with a fundamental limitation: temporal information is not well incorporated into semantic IDs.
arXiv Machine Learning
Jun 10
Generative Archetype-Grounded Item Representations for Sequential Recommendation
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv Machine Learning
Jun 25
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.