2 citations · 2 across the 16 of their papers we have counts for
5 papers · 1 filter
FAVE: Flow-based Average Velocity Establishment for Sequential Recommendation
Ke Shi, Yao Zhang, Feng Guo +4
Generative recommendation has emerged as a transformative paradigm for capturing the dynamic evolution of user intents in sequential recommendation. While flow-based methods improv…
TOOL4POI: A Tool-Augmented LLM Framework for Next POI Recommendation
Dongsheng Wang, Shen Gao, Chengrui Huang +3
Next Point-of-Interest (POI) recommendation is a fundamental task in location-based services. While recent advances leverage Large Language Model (LLM) for sequential modeling, exi…
CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Yifan Wang, Shen Gao, Jiabao Fang +3
Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail…
Generative Next POI Recommendation with Semantic ID
Dongsheng Wang, Yuxi Huang, Shen Gao +3
Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendat…
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Shen Gao, Yifan Wang, Jiabao Fang +3
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior.However, the lack of transparency in presenting recommendations can lead to u…