19 citations · 30 across the 16 of their papers we have counts for
17 papers
Generative Archetype-Grounded Item Representations for Sequential Recommendation
Yifan Li, Jiahong Liu, Xinni Zhang +5
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a…
PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation
Ruijie Du, Hao Chen, Xin Zhang +5
In multi-behavior recommendation, auxiliary behaviors such as clicks, add-to-cart, and purchases can provide richer supervisory information for predicting target behaviors. Althoug…
The Semantic Lifecycle in Embodied AI: Acquisition, Representation and Storage via Foundation Models
Shuai Chen, Hao Chen, Yuanchen Bei +3
Semantic information in embodied AI is inherently multi-source and multi-stage, making it challenging to fully leverage for achieving stable perception-to-action loops in real-worl…
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Hongyu Yao, Zijin Hong, Hao Chen +6
Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs. Traditional multi-task learning m…
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent
Liang-bo Ning, Shijie Wang, Wenqi Fan +4
Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attent…
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Ruochen Liu, Hao Chen, Yuanchen Bei +6
Large Language Model (LLM)-based cold-start recommendation systems continue to face significant computational challenges in billion-scale scenarios, as they follow a "Text-to-Judgm…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
Graph Cross-Correlated Network for Recommendation
Hao Chen, Yuanchen Bei, Wenbing Huang +3
Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powe…
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Yuanchen Bei, Weizhi Chen, Hao Chen +5
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Alth…
Graph Neural Patching for Cold-Start Recommendations
Hao Chen, Yu Yang, Yuanchen Bei +3
The cold start problem in recommender systems remains a critical challenge. Current solutions often train hybrid models on auxiliary data for both cold and warm users/items, potent…
Feedback Reciprocal Graph Collaborative Filtering
Weijun Chen, Yuanchen Bei, Qijie Shen +3
Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw di…
Large Language Model Simulator for Cold-Start Recommendation
Feiran Huang, Yuanchen Bei, Zhenghang Yang +6
Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely sol…
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Yijie Zhang, Yuanchen Bei, Hao Chen +6
Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors natur…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Qiaoyu Tan +4
Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly fr…
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Yijie Zhang, Yuanchen Bei, Shiqi Yang +4
Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance re…