5 papers
Grevo: A Unified Generative Recommendation Framework with Evolutionary Item Indexing
Huanjie Wang, Liwei Guan, Zekai Sun +2
Generative recommendation has recently emerged as a promising paradigm that reformulates retrieval as autoregressive generation over semantic identifiers (SIDs), achieving strong p…
DREAM: Dynamic Refinement of Early Assignment Mappings
Liwei Guan, Huanjie Wang, Hongwei Zhang +2
Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While…
UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
Hongwei Zhang, Qiqiang Zhong, Jiangxia Cao +8
Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic…
PIT: A Dynamic Personalized Item Tokenizer for End-to-End Generative Recommendation
Huanjie Wang, Xinchen Luo, Honghui Bao +6
Generative Recommendation has revolutionized recommender systems by reformulating retrieval as a sequence generation task over discrete item identifiers. Despite the progress, exis…
Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning
Xingke Yang, Liang Li, Sicong Li +6
Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a…