6 accepted papers
Current methods for formation flight primarily focus on maintaining formations, often neglecting the swarm's agility. Furthermore, most of these approaches fail to leverage global information from the swarm for obstacle avoidance, making them incapable of generating efficient and safe trajectories i…
Current methods for formation flight primarily focus on maintaining formations, often neglecting the swarm's agility. Furthermore, most of these approaches fail to leverage global information from the swarm for obstacle avoidance, making them incapable of generating efficient and safe trajectories i
Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground truth for explanations and focus on improving review prediction accuracy by designing various model architectures. Howe
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generate short-sighted responses that fail to sustain user guidance and meet expectations. Although preference optimization has…
Explainable recommendation has gained significant attention due to its potential to enhance user trust and system transparency. Previous studies primarily focus on refining model architectures to generate more informative explanations, assuming that the explanation data is sufficient and easy to acq…
Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential to improve the recommendation persuasiveness, informativeness and user satisfaction. In the past few years, while a lot of promising explainable recommender models have be…