Search Results for author:
Found 19 papers, 7 papers with code
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Specifically, we present FilterLLM, a framework that extends the next-word prediction capabilities of LLMs to billion-scale filtering tasks.
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
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.
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
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.
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
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.
Graph Cross-Correlated Network for Recommendation
Encoding all subgraph information into single vectors and inferring user-item relations with dot products can weaken the semantic information between user and item subgraphs, thus leaving untapped potential.
CLR-Bench: Evaluating Large Language Models in College-level Reasoning
While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final prediction on multi-choice questions.
Graph Neural Patching for Cold-Start Recommendations
To this end, we introduce Graph Neural Patching for Cold-Start Recommendations (GNP), a customized GNN framework with dual functionalities: GWarmer for modeling collaborative signal on existing warm users/items and Patching Networks for simulating and enhancing GWarmer's performance on cold-start recommendations.
Feedback Reciprocal Graph Collaborative Filtering
Training graph collaborative filtering models in the absence of distinction between them can lead to the recommendation of unfascinating items to users.
Better Late Than Never: Formulating and Benchmarking Recommendation Editing
This paper introduces a novel and significant task termed recommendation editing, which focuses on modifying known and unsuitable recommendation behaviors.
Revisiting the Message Passing in Heterophilous Graph Neural Networks
Based on HTMP and empirical analysis, we reveal that the success of message passing in existing HTGNNs is attributed to implicitly enhancing the compatibility matrix among classes.
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods.
Large Language Model Simulator for Cold-Start Recommendation
While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
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.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
(2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times.
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
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.
Flattened Graph Convolutional Networks For Recommendation
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
