Xinyu Lin

Causal Disentangled Recommendation against User Preference Shifts

2023 · ACM Transactions on Information Systems

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on …

Data-efficient Fine-tuning for LLM-based Recommendation

2024 · arXiv (Cornell University)

Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical …

Efficient Inference for Large Language Model-based Generative Recommendation

2024 · arXiv (Cornell University)

Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration, Speculative Decoding (SD) has …