Yuanchen Bei
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View article: Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory Open
Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and evolution remain largely underexplored. Existing evaluations …
View article: AliBoost: Ecological Boosting Framework in Alibaba Platform
AliBoost: Ecological Boosting Framework in Alibaba Platform Open
View article: From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents Open
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi…
View article: AliBoost: Ecological Boosting Framework in Alibaba Platform
AliBoost: Ecological Boosting Framework in Alibaba Platform Open
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. This ''rich-get-richer'' phenomenon hinders the growth of pot…
View article: Large Language Model Simulator for Cold-Start Recommendation
Large Language Model Simulator for Cold-Start Recommendation Open
View article: FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation Open
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-Judgment" paradigm. This approach processes user-item content pa…
View article: A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models Open
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-Augmented generation (RAG) has …
View article: Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap Open
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 recommendations. Due to the diversification of internet platforms…
View article: A Survey of RAG-Reasoning Systems in Large Language Models
A Survey of RAG-Reasoning Systems in Large Language Models Open
View article: Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification Open
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. Although a few efforts have been made by utilizing Graph Convol…
View article: Graph Cross-Correlated Network for Recommendation
Graph Cross-Correlated Network for Recommendation Open
Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-…
View article: CLR-Bench: Evaluating Large Language Models in College-level Reasoning
CLR-Bench: Evaluating Large Language Models in College-level Reasoning Open
Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer scie…
View article: Graph Neural Patching for Cold-Start Recommendations
Graph Neural Patching for Cold-Start Recommendations Open
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, potentially degrading the experience for the latter. This drawbac…
View article: Feedback Reciprocal Graph Collaborative Filtering
Feedback Reciprocal Graph Collaborative Filtering Open
Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw dilemma for collaborative filtering models learned from the i…
View article: Better Late Than Never: Formulating and Benchmarking Recommendation Editing
Better Late Than Never: Formulating and Benchmarking Recommendation Editing Open
Recommendation systems play a pivotal role in suggesting items to users based on their preferences. However, in online platforms, these systems inevitably offer unsuitable recommendations due to limited model capacity, poor data quality, o…
View article: Revisiting the Message Passing in Heterophilous Graph Neural Networks
Revisiting the Message Passing in Heterophilous Graph Neural Networks Open
Graph Neural Networks (GNNs) have demonstrated strong performance in graph mining tasks due to their message-passing mechanism, which is aligned with the homophily assumption that adjacent nodes exhibit similar behaviors. However, in many …
View article: Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems Open
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 complexity involved in aggregating billions of neighbors.To tac…
View article: Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection Open
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Ne…
View article: Large Language Model Simulator for Cold-Start Recommendation
Large Language Model Simulator for Cold-Start Recommendation Open
Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation perf…
View article: Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks Open
Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and learn separate CF em…
View article: Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems Open
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 complexity involved in aggregating billions of neighbors. To ta…
View article: Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection Open
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 from the majority of nodes. Recent advancements have utilized…
View article: Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering Open
Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph colla…
View article: Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation Open
Online local-life service platforms provide services like nearby daily essentials and food delivery for hundreds of millions of users. Different from other types of recommender systems, local-life service recommendation has the following c…
View article: CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks Open
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich inf…
View article: Flattened Graph Convolutional Networks For Recommendation
Flattened Graph Convolutional Networks For Recommendation Open
Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can aris…