6 papers
Reasoning over Semantic IDs Enhances Generative Recommendation
Yingzhi He, Yan Sun, Junfei Tan +6
Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space compris…
Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion
ShiYing Huang, Liang Lin, Yuer Li +6
In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. Specifically, the intrinsic tensi…
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
Antong Zhang, Han Qi, Heng Yang
We introduce BEACON--Best-Effort Adaptation for Cross-Domain Co-Training--a theory-driven framework for training generative robot policies with abundant source demonstrations and l…
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language Embeddings
Guoqing Hu, An Zhang, Shuo Liu +3
Recent advancements in sequential recommendation have underscored the potential of Large Language Models (LLMs) for enhancing item embeddings. However, existing approaches face thr…
Preference Diffusion for Recommendation
Shuo Liu, An Zhang, Guoqing Hu +2
Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained at…
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation
Guoqing Hu, Zhengyi Yang, Zhibo Cai +2
Recent advancements in generative recommendation systems, particularly in the realm of sequential recommendation tasks, have shown promise in enhancing generalization to new items.…