260 citations · 333 across the 15 of their papers we have counts for
5 papers · 1 filter
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
Bin Yin, Junjie Xie, Yu Qin +4
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of h…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Jingwei Zhuo, Bin Liu, Xiang Li +2
Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates l…
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Gang Wang, Ziyi Guo, Xiang Li +2
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learnin…