5 papers
On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
Wenyu Mao, Jiancan Wu, Guoqing Hu +3
Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories…
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao +7
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models h…
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.…