1 citations · 1 across the 3 of their papers we have counts for
5 papers
Efficient Model-Agnostic Continual Learning for Next POI Recommendation
Chenhao Wang, Shanshan Feng, Lisi Chen +2
Next point-of-interest (POI) recommendation improves personalized location-based services by predicting users' next destinations based on their historical check-ins. However, most…
From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting
Xinyu Zhang, Shanshan Feng, Xutao Li +3
Using pre-trained large language models (LLMs) as a backbone for time series prediction has recently attracted growing research interest. Existing approaches typically split time s…
A New Era in Human Factors Engineering: A Survey of the Applications and Prospects of Large Multimodal Models
Li Fan, Lee Ching-Hung, Han Su +3
In recent years, the potential applications of Large Multimodal Models (LMMs) in fields such as healthcare, social psychology, and industrial design have attracted wide research at…
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
Zhuoxuan Jiang, Haoyuan Peng, Shanshan Feng +2
Self-correction is emerging as a promising approach to mitigate the issue of hallucination in Large Language Models (LLMs). To facilitate effective self-correction, recent research…
Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
Shanshan Feng, Haoming Lyu, Caishun Chen +1
Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment. Existing studies rely on building recommendation model…